Land and Food Systems, Faculty of
DSpace
UBCV
Hu, Yaxi
Doctor of Philosophy - PhD
University of British Columbia
Food fraud was estimated to cost the global food industry $10-15 billion per year. Various traceability and risk assessment systems have been developed to deter food fraud. With rapid globalization and complex supply chain, effective product tracing and tracking and accurate vulnerability assessment have been inevitably hindered. Serving as the last barrier to ensure food authenticity, reliable techniques to identify fraudulent foods are indispensable. Traditional techniques (e.g. liquid chromatography-based assays) are usually time-consuming, labor- intensive, lack the sensitivity and/or specificity, and/or complicated. Therefore, the overall objective of my Ph.D. thesis project was to validate the feasibility of spectroscopic techniques [i.e. Raman, mid-infrared, and nuclear magnetic resonance (NMR) spectroscopies] and an advanced DNA amplification method [i.e. loop-mediated isothermal amplification (LAMP)] to detect food fraud. During my studies, an optimized protocol was developed to authenticate ground beef meat and identify and quantify the offal adulterants using mid-infrared spectroscopy coupled with chemometric models, providing a limit of detection (LOD) <10% w/w of offal in ground beef meat. With simple/no sample pretreatment, solution NMR spectroscopy and solid-state NMR spectroscopy were confirmed to detect 6.7 and 128.6 mg/kg Sudan I in paprika powder in <30 min, respectively. A confocal micro-Raman spectrometer and a portable Raman spectrometer were applied to identify 11 species of raw finfish purchased from seafood markets in Vancouver. The method developed using the portable Raman spectrometer exhibited promising results with a 100% accuracy in differentiating Salmonidae and non-Salmonidae, 88% accuracy in identifying four species of salmon, and 91% accuracy in separating the seven species of non-Salmonidae fish. A LAMP-based assay was developed to authenticate pure pomegranate juice from juice adulterated by apple and/or grape juice. With a novel paper-based DNA extraction device and a simple LAMP result visualization method, the overall sample-to-result analysis was completed in ~1 h with a LOD of 10~100 ng of DNA. Methods and devices developed in my studies may be used to authenticate many other food commodities and have the potential to be adopted by governmental laboratories, food industries and even consumers to rapidly authenticate food products, and thus better ensure the integrity of foods.
DEVELOPMENT OF INNOVATIVE TECHNIQUES FOR FOOD AUTHENTICATION – THE LAST BARRIER TO PREVENT FOOD FRAUD by Yaxi Hu BSc. China Agricultural University, 2013 MSc. The University of British Columbia, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (FOOD SCIENCE) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2018 © Yaxi Hu, 2018 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Development of Innovative Techniques for Food Authentication – the Last Barrier to Prevent Food Fraud submitted by Yaxi Hu in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Food Science Examining Committee: Dr. Xiaonan Lu Supervisor Dr. Corey Nislow Supervisory Committee Member Dr. Keng C. Chou University Examiner Dr. David Perrin University Examiner Additional Supervisory Committee Members: Dr. Rickey Yada Supervisory Committee Member Dr. Jie Xu Supervisory Committee Member iii Abstract Food fraud was estimated to cost the global food industry $10-15 billion per year. Various traceability and risk assessment systems have been developed to deter food fraud. With rapid globalization and complex supply chain, effective product tracing and tracking and accurate vulnerability assessment have been inevitably hindered. Serving as the last barrier to ensure food authenticity, reliable techniques to identify fraudulent foods are indispensable. Traditional techniques (e.g. liquid chromatography-based assays) are usually time-consuming, labor-intensive, lack the sensitivity and/or specificity, and/or complicated. Therefore, the overall objective of my Ph.D. thesis project was to validate the feasibility of spectroscopic techniques [i.e. Raman, mid-infrared, and nuclear magnetic resonance (NMR) spectroscopies] and an advanced DNA amplification method [i.e. loop-mediated isothermal amplification (LAMP)] to detect food fraud. During my studies, an optimized protocol was developed to authenticate ground beef meat and identify and quantify the offal adulterants using mid-infrared spectroscopy coupled with chemometric models, providing a limit of detection (LOD) <10% w/w of offal in ground beef meat. With simple/no sample pretreatment, solution NMR spectroscopy and solid-state NMR spectroscopy were confirmed to detect 6.7 and 128.6 mg/kg Sudan I in paprika powder in <30 min, respectively. A confocal micro-Raman spectrometer and a portable Raman spectrometer were applied to identify 11 species of raw finfish purchased from seafood markets in Vancouver. The method developed using the portable Raman spectrometer exhibited promising results with a 100% accuracy in differentiating Salmonidae and non-Salmonidae, 88% accuracy in identifying iv four species of salmon, and 91% accuracy in separating the seven species of non-Salmonidae fish. A LAMP-based assay was developed to authenticate pure pomegranate juice from juice adulterated by apple and/or grape juice. With a novel paper-based DNA extraction device and a simple LAMP result visualization method, the overall sample-to-result analysis was completed in ~1 h with a LOD of 10~100 ng of DNA. Methods and devices developed in my studies may be used to authenticate many other food commodities and have the potential to be adopted by governmental laboratories, food industries and even consumers to rapidly authenticate food products, and thus better ensure the integrity of foods. v Lay Summary Food fraud, or economically motivated adulteration, is an umbrella term for illegal practices that hinder consumers from knowing the true ingredients of the food products they purchase. Food fraud is estimated to cost the global food industry $10-15 billion per year. Traceability techniques such as the barcode systems play a proactive role in preventing food fraud, but effective analytical methods for food authentication are also critical. Current food authentication methods are generally complicated, slow, costly and/or laborious, and thus are not ideal for high frequent inspections. My Ph.D. study focused on using more advanced techniques to develop novel analytical methods to conquer the drawbacks of the established methods and make the methods more suitable for frequent inspections. We expect that the four analytical methods developed in this study can be adopted by inspection agencies, food industries, and even consumers for better protection of all parties from impacted by fraudulent foods. vi Preface The work in Chapter 2 has been published in Scientific Reports [7(1), 15162. doi:10.1038/s41598-017-15389-3] with the title of “Detection and quantification of offal content in ground beef meat using vibrational spectroscopic-based chemometric analysis”. I, as the first author, designed the experiment, collected and analyzed the data, and wrote the manuscript. Dr. Xiaonan Lu provided significant feedback on the experimental design and data analysis and critically reviewed the manuscript. Dr. Liang Zou provided technical supports for the data analysis. Xiaonlin Huang helped with data collection. Both Dr. Liang Zou and Xiaolin Huang reviewed and revised the manuscript. Chapter 3 is a version of a published journal article “Hu, Y., Wang, S., Wang, S., & Lu, X. (2017). Application of nuclear magnetic resonance spectroscopy in food adulteration determination: the example of Sudan dye I in paprika powder. Scientific Reports, 7(1), 2637. doi: 10.1038/s41598-017-02921-8”. The experiment was designed collaboratively by all the four authors. Dr. Shenlin Wang and I conducted the experiments and analyzed the results. I wrote the manuscript and Dr. Shenlin Wang, Dr. Shuo Wang and Dr. Xiaonan Lu provided critical feedbacks and reviewed the manuscript. Chapter 4 has been published in Food Control “Hu, Y., Huang, S., Hanner, R., Levin, J., & Lu, X. (2018). Study of fish products in Metro Vancouver using DNA barcoding methods reveals fraudulent labeling. Food Control, 94, 38-47. doi: 10.1016/j.foodcont.2018.06.023”. The study was designed collaboratively by all the five authors. The samples were collected and analyzed by vii Shr Yun Huang, Julia Levin and myself. I wrote the manuscript, while all the other four authors provided instructional feedbacks and revised the manuscript. Chapter 5 is the basis of an unpublished manuscript. I designed the experiment with the help of Dr. Xiaonan Lu. I conducted the experiments with Shr Yun Huang. I analyzed the results and wrote the manuscript. Shr Yun Huang and Dr. Xiaonan Lu provided critical feedback and Dr. Xiaonan Lu reviewed the chapter. Chapter 6 is the basis of another unpublished manuscript. I designed the experiment with the help of Dr. Xiaonan Lu. I conducted the experiments and analyzed the results. I wrote the manuscript with instrumental feedback from Dr. Xiaonan Lu. Dr. Xiaonan Lu also helped with revising the writing. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables .............................................................................................................................. xiv List of Figures ............................................................................................................................. xvi List of Abbreviations ................................................................................................................ xxii Acknowledgements ................................................................................................................... xxv Dedication ................................................................................................................................ xxvii Chapter 1: Introduction ............................................................................................................... 1 1.1 Food fraud ....................................................................................................................... 1 1.2 Traceability and prevention systems ............................................................................... 3 1.3 Detection methods based on characterizing the chemical compositions ........................ 4 1.3.1 Traditional methods ................................................................................................ 4 1.3.2 Fingerprinting spectroscopic techniques ................................................................. 5 1.4 Detection methods based on characterizing the DNA sequence .................................... 7 1.4.1 Traditional polymerase chain reaction (PCR)-based methods ................................ 7 1.4.2 Isothermal amplification ......................................................................................... 8 1.5 Instrumental free microfluidic lab-on-a-chip platform ................................................... 9 1.6 Central hypothesis ......................................................................................................... 11 Chapter 2: Determination of ground beef adulterated with beef and pork offal using Raman spectroscopy and FT-IR spectroscopy ......................................................................... 12 ix 2.1 Introduction ................................................................................................................... 12 2.2 Hypotheses .................................................................................................................... 14 2.3 Objectives ..................................................................................................................... 14 2.4 Experimental design ...................................................................................................... 15 2.4.1 Preparation of meat and offal samples .................................................................. 15 2.4.2 Preparation of adulterated samples ....................................................................... 15 2.4.3 FT-IR spectral collection ...................................................................................... 16 2.4.4 Raman spectral collection ..................................................................................... 16 2.4.5 Vibrational spectral preprocessing ........................................................................ 17 2.4.6 Chemometric analyses .......................................................................................... 17 2.5 Results and Discussion ................................................................................................. 21 2.5.1 Exploratory analysis .............................................................................................. 21 2.5.2 Classification analysis ........................................................................................... 30 2.5.2.1 Seven-class classification .................................................................................. 30 2.5.2.2 Three-class classification .................................................................................. 36 2.5.2.3 Identification of different types of beef and pork offal ..................................... 41 2.5.3 Quantitative analysis ............................................................................................. 45 2.5.3.1 Quantitative analysis using Raman spectroscopy ............................................. 45 2.5.3.2 Quantitative analysis using FT-IR spectroscopy .............................................. 45 2.5.4 Procedure for real sample analysis ....................................................................... 50 2.6 Conclusion .................................................................................................................... 54 Chapter 3: Determination of Sudan I in paprika powder with solution and solid-state 1H nuclear magnetic resonance spectroscopy ................................................................................ 55 3.1 Introduction ................................................................................................................... 55 x 3.2 Hypotheses .................................................................................................................... 58 3.3 Objectives ..................................................................................................................... 59 3.4 Material and Methods ................................................................................................... 59 3.4.1 Materials ............................................................................................................... 59 3.4.2 Solvent optimization ............................................................................................. 59 3.4.3 Sample preparation ............................................................................................... 60 3.4.4 Instrumentation ..................................................................................................... 61 3.4.5 Spectral analysis .................................................................................................... 62 3.5 Results and Discussion ................................................................................................. 63 3.5.1 Solvent selection ................................................................................................... 63 3.5.2 Determination of Sudan I by solution NMR spectroscopy ................................... 64 3.5.3 Determination of Sudan I by SSNMR spectroscopy ............................................ 68 3.5.4 Comparison of techniques for the determination of Sudan I in food samples ...... 71 3.6 Conclusion .................................................................................................................... 75 Chapter 4: A Market survey study using DNA barcoding revealed fraudulent labeling in fish products ................................................................................................................................ 77 4.1 Introduction ................................................................................................................... 77 4.2 Hypotheses .................................................................................................................... 82 4.3 Objectives ..................................................................................................................... 82 4.4 Material and Methods ................................................................................................... 83 4.4.1 Collection of fish samples ..................................................................................... 83 4.4.2 Total DNA extraction ........................................................................................... 83 4.4.3 Amplification of the DNA barcode regions .......................................................... 84 4.4.4 DNA sequencing analysis ..................................................................................... 86 xi 4.4.5 Statistical analysis ................................................................................................. 87 4.5 Results and Discussion ................................................................................................. 87 4.5.1 Overall results ....................................................................................................... 87 4.5.2 Sushi bars .............................................................................................................. 97 4.5.3 Non-sushi restaurants ............................................................................................ 98 4.5.4 Grocery stores ..................................................................................................... 100 4.5.5 Ambiguity of labeling regulation ........................................................................ 102 4.6 Conclusions ................................................................................................................. 106 Chapter 5: Development of a Raman spectroscopic based method for the authentication of finfish fillets in the market ....................................................................................................... 108 5.1 Introduction ................................................................................................................. 108 5.2 Hypotheses .................................................................................................................. 110 5.3 Objectives ................................................................................................................... 110 5.4 Material and Methods ................................................................................................. 111 5.4.1 Raman spectral collection ................................................................................... 112 5.4.2 Preprocessing of Raman spectral data ................................................................ 113 5.4.3 Chemometric analysis ......................................................................................... 113 5.5 Results and Discussion ............................................................................................... 114 5.5.1 Explorative analysis of Raman spectra ............................................................... 114 5.5.2 Classification analysis ......................................................................................... 122 5.5.2.1 Salmonidae v.s. non-Salmonidae classification .............................................. 122 5.5.2.2 Four-class Salmonidae classification .............................................................. 126 5.5.2.3 Seven-class non-Salmonidae classification .................................................... 128 5.6 Conclusion .................................................................................................................. 133 xii Chapter 6: Rapid authentication of pomegranate juice using an instrumental free loop-mediated isothermal amplification assay ................................................................................ 134 6.1 Introduction ................................................................................................................. 134 6.2 Hypotheses .................................................................................................................. 137 6.3 Objectives ................................................................................................................... 137 6.4 Materials and method .................................................................................................. 138 6.4.1 Materials ............................................................................................................. 138 6.4.2 Preparation of fruit juice samples ....................................................................... 138 6.4.3 DNA extraction from fruit juice by using the CTAB method ............................ 139 6.4.4 Fruit juice DNA extraction using commercial kits ............................................. 139 6.4.5 PCR assay for fruit juices of three species of fruit ............................................. 140 6.4.6 Design of LAMP primers .................................................................................... 141 6.4.7 LAMP assay for fruit juice authentication .......................................................... 142 6.4.8 DNA extraction from fruit juice by using paper-based materials ....................... 143 6.4.9 Simple visual analysis of the LAMP results ....................................................... 145 6.4.10 Design for a sample-to-answer microfluidic device ........................................... 145 6.5 Results and Discussion ............................................................................................... 147 6.5.1 Extraction of fruit juice DNA by using conventional methods .......................... 147 6.5.2 LAMP for fruit juice authentication ................................................................... 150 6.5.3 Simplified fruit juice DNA extraction by using a paper-based device ............... 153 6.5.4 Simplified visual analysis of LAMP reaction products ...................................... 158 6.5.5 Instrumental free sample-to-answer paper-microfluidics ................................... 161 6.6 Conclusion .................................................................................................................. 164 Chapter 7: Conclusion .............................................................................................................. 165 xiii 7.1 Main findings .............................................................................................................. 165 7.2 Future research directions ........................................................................................... 171 Bibliography .............................................................................................................................. 175 Appendix A The composition and characteristics of fish samples collected in Metro Vancouver ............................................................................................................................... 192 xiv List of Tables Table 2.1 Peak assignment of FT-IR and Raman spectra 107,108 ................................................... 22 Table 2.2 The best three models for the 7-class classification of all samples using FT-IR spectra ....................................................................................................................................................... 32 Table 2.3 The best three models for the 7-class classification of all samples using Raman spectra ....................................................................................................................................................... 34 Table 2.4 The best three models for the 3-class classification of all samples using FT-IR spectra ....................................................................................................................................................... 38 Table 2.5 The optimized models for the 3-class classification of FT-IR and Raman spectral data ....................................................................................................................................................... 39 Table 2.6 The best three models for the 3-class classification of all samples using Raman spectra ....................................................................................................................................................... 40 Table 2.7 The optimized models for the 3-class classification of beef and pork offal using FT-IR spectra ........................................................................................................................................... 43 Table 2.8 The optimized models for the 2-class classification of beef and pork offal using FT-IR spectra ........................................................................................................................................... 44 Table 2.9 Partial least squares regression (PLSR) models for the quantification of offal using Raman and FT-IR spectroscopies. ................................................................................................ 47 Table 4.1 Primers for fish barcode and mini-barcode DNA regions 179,187 .................................. 84 Table 4.2 Details of mislabeled species for fish collected from sushi bars, restaurants, and grocery stores (NIFL represent a species not on the Canadian Food Inspection Agency Fish List). Common names not on CFIA list are from US Food and Drug Administration Fish List. Samples xv with ID containing only digits were purchased from sushi bars, while samples starting with R and G were purchased from restaurants and grocery stores, respectively. ................................... 89 Table 4.3 Results of generalized linear regression model with binary distribution for the analysis of properties related to fishes having significant impact on the mislabeling/fraud practices (α =0.05) .............................................................................................................................................. 97 Table 5.1 Biological species composition of fish samples used for the construction of the fish Raman spectral library ................................................................................................................ 111 Table 5.2 Assignments of major peaks in Raman spectra 108,214 ................................................. 116 Table 5.3 The optimized models for the 2-class classification based on Raman spectral data collected using the benchtop Raman spectrometer and the portable Raman spectrometer ........ 124 Table 5.4 The optimized models for the 4-class Salmonidae classification based on Raman spectral data collected using the benchtop Raman spectrometer and the portable Raman spectrometer ................................................................................................................................ 125 Table 5.5 The optimized models for the 7-class non-Salmonidae classification of Raman spectral data collected using the benchtop Raman spectrometer and the portable Raman spectrometer 130 Table 5.6 Calibration model confusion matrices of the 7-class non-Salmonidae classifiers ...... 132 Table 6.1 List of PCR primers for species-specific SCAR markers ........................................... 141 Table 6.2 Universal primers for plant 18S rDNA ....................................................................... 147 Table 6.3 Optimized LAMP primers for grape, apple and pomegranate .................................... 151 Table 7.1 Comparison of the properties of four types of analytical techniques being applied in this thesis study. .......................................................................................................................... 171 xvi List of Figures Figure 1.1 The food integrity matrix demonstrates the definition and relationship between the four types of food-related issues 1. .................................................................................................. 2 Figure 1.2 Number of citation (A) and number of publication (B) of research papers with the topic of food fraud or food adulteration (Data from Web of Science). .......................................... 3 Figure 2.1 Averaged Raman (A) and FT-IR (B) spectra of each type of meat and offal samples. From the bottom to the top: beef meat, beef honey comb tripe, beef liver, beef omasum, pork heart, pork kidney and pork liver. ................................................................................................. 24 Figure 2.2 Averaged Raman (A) and FT-IR (B) spectra of each type of beef meat and offal with the band representing the standard deviation. From the bottom to the top: beef meat, beef honey comb tripe, beef liver, beef omasum, pork heart, pork kidney and pork liver. ............................. 25 Figure 2.3 Score plots of principal component analysis of Raman (A) and FT-IR (B) original spectra. Each symbol represents one type of the sample: star, beef meat; diamond, beef honey comb tripe; cross-mark, beef liver; circle, beef omasum; triangular, pork heart; dot, pork kidney; and asteroid, pork liver. Symbols with blue colors represent tissue samples from beef, while red color symbols are samples from pork tissues. .............................................................................. 27 Figure 2.4 Score plots of principal component analysis of 2nd derivative Raman (A) and FT-IR (B) spectra. Each symbol represents one type of the sample: star, beef meat; diamond, beef honey comb tripe; cross-mark, beef liver; circle, beef omasum; triangular, pork heart; dot, pork kidney; asteroid, pork liver. .......................................................................................................... 29 Figure 2.5 Illustration of the flow chart for the analysis of new ground beef meat samples. ....... 53 Figure 2.6 An example of the “spectrum-to-answer” Matlab gui interface for the authenticity analysis of ground beef meat. ....................................................................................................... 53 xvii Figure 3.1 1H solution NMR spectra of paprika powder extracted by DMSO-d6 (bottom), acetone-d6 (middle), and acetonitrile-d3 (top). ............................................................................ 63 Figure 3.2 Schematic illustration of experimental design. NMR, nuclear magnetic resonance; HR MAS, high resolution magic angle spinning. ................................................................................ 64 Figure 3.3 1H solution NMR spectra of paprika powder spiked with Sudan I at various concentrations (0, 20, 50, 100, 250, and 500 mg kg-1). ................................................................ 65 Figure 3.4 1H solution NMR spectra of paprika powder spiked with Sudan I at 100 mg kg-1 (bottom, black) and standard Sudan I ACN solution at 40 mg L-1 (top, blue). The shift of the peaks was because of the different solvent used for these two spectra. ....................................... 66 Figure 3.5 Linear regression model for quantitative analysis of Sudan I in paprika powder by 1H solution NMR spectrometer. ......................................................................................................... 67 Figure 3.6 1H HR MAS SSNMR spectra of paprika powder spiked with Sudan I at various concentrations (0, 225, 675, 1350, 1800, and 2250 mg kg-1). ...................................................... 69 Figure 3.7 Linear regression model for quantitative analysis of Sudan I in paprika powder by 1H HR MAS SSNMR spectrometer. .................................................................................................. 70 Figure 3.8 1H solution NMR spectra of 100 mg L-1 Sudan I standard solution in ACN without being filtered (bottom, black) and 100 mg L-1 Sudan I standard solution in ACN after being filtered by 0.45 µm nylon syringe filter (top, blue). Approximately 40% Sudan I lost during the filtration. ........................................................................................................................................ 73 Figure 4.1 Categorization of samples collected in Metro Vancouver based on geographical locations (A), source of the samples (B) and major species of samples (C). Snapper in (C) include samples labeled as real snappers (Lutjanus spp.) and Pacific snappers (Sebastes spp.) and other in (C) include fish species with less than three samples collected as per labeled on the xviii package or menu (i.e. amberjack, bigeye, char, herring, monkfish, pollock, pompano, sardine, scad, toothfish, basa, lingcod, seabream). ..................................................................................... 88 Figure 4.2 Number of mislabeled products analyzed based on source of samples (A) and the major species of samples (B). Snapper in (B) include samples labeled as real snappers (Lutjanus spp.) and Pacific snapper (Sebastes spp.) and other in (B) include fish species with less than three samples collected as per labeled on the package or menu (i.e. amberjack, bigeye, char, herring, monkfish, pollock, pompano, sardine, scad, toothfish, basa, lingcod, seabream). ....................... 95 Figure 5.1 Average of normalized Raman spectra of different species of fish collected using the bench-top confocal micro-Raman spectrometer (A) and the portable Raman spectrometer (B). ..................................................................................................................................................... 115 Figure 5.2 Average of normalized Raman spectra of 11 species of fish collected by using benchtop Raman spectrometer (A) and portable Raman spectrometer (B) with the band representing the standard deviation. ........................................................................................... 119 Figure 5.3 Principal component analysis (PCA) score plots for spectra collected by using benchtop Raman spectrometer (A) and portable Raman spectrometer (B). PCA was conducted on the wavenumber region of 1800-400 cm-1 for the spectra collected by both Raman spectrometers. ............................................................................................................................. 120 Figure 5.4 Principle component analysis loading plot for spectra collected by using benchtop Raman spectrometer (A) and portable Raman spectrometer (B). PCA was conducted using the wavenumber region of 1800-400 cm-1 for spectra collected by both Raman spectrometers. .... 121 Figure 6.1 Assembly of vertical filtration device for DNA extraction using Fusion 5 filter paper. ..................................................................................................................................................... 144 Figure 6.2 Design of the hybrid paper/polymer microfluidic device for the authentication of pomegranate juice. (A) Layers 1-6 were made with Whatman No. 1 filter paper, except that the xix blue region on layer 4 was a piece of Fusion 5 paper with a diameter of 5 mm for DNA extraction. The paper was blocked by wax printing to define the hydrophobic areas (shown in black) and to guide the liquid wicking. Layer 7 was made with PDMS for LAMP reaction and the reaction wells (shown as circles) are of a diameter of 3 mm. The green areas denote the path of DNA eluted from layer 4 to layer 7, while the white areas demonstrated the path of water from the loading layer (layer 1) to the negative control LAMP reaction wells (white circles on layer 7). The arrows indicated the directions of the folding of each layer during the analysis. (B) Schematic illustration of the portable heating pad powered by a portable charger and equipped with a temperature controller. (C) Layer-by-layer view of the hybrid polymer-paper microfluidic device. ......................................................................................................................................... 146 Figure 6.3 Electrophoresis agarose gel analysis of 18S primers PCR products using DNA extracted by CTAB method (A) and Qiagen kit (B). Lanes in panel A: 1, DNA extracted from grape juice; 2, DNA extracted from apple juice; 3, DNA extracted from pomegranate juice; 4, water as a negative control; 5, 100-bp DNA ladder. Lanes in panel B: 1-2, DNA extracted from grape juice; 3-4, DNA extracted from apple juice; 5-6, DNA extracted from pomegranate juice; 7, 100-bp DNA ladder. ................................................................................................................ 148 Figure 6.4 Electrophoresis agarose gel analysis of SCAR primers PCR products using DNA extracted from different fruit juices by using the Qiagen kit. Lanes 1-4 were amplified by using the grape SCAR primers: 1, grape DNA; 2, apple DNA; 3, pomegranate DNA; 4, water as a negative control (NC). Lanes 6-9 were amplified by using the apple SCAR primers: 6, grape DNA; 7, apple DNA; 8, pomegranate DNA; 9, water as an NC. Lanes 11-14 were amplified by using the pomegranate SCAR primers: 11, grape DNA; 12, apple DNA; 13, pomegranate DNA; 14, water as an NC. Lanes 5 and 10: 100-bp DNA ladders. ....................................................... 149 xx Figure 6.5 Specificity (A, B and C) and sensitivity (D, E and F) tests of LAMP primers for grape (A and D), apple (B and E), and pomegranate (C and F). Lanes in panel A, B, and C: 1, 100-bp DNA ladder; 2, grape; 3, apple; 4, pomegranate; 5, negative control. Lanes in panel D, E, and F: L, 100-bp ladder; 10-fold serially diluted grape, apple and pomegranate DNA (initial concentration of DNA in samples ~ 5 ng/µl); NC, negative control. ......................................... 153 Figure 6.6 Electrophoresis agarose gel analysis of SCAR primers PCR products for DNA extracted from different fruit juices by using the Fusion 5 method. Different sets of SCAR primers were used for each panel: A, grape SCAR primer; B, apple SCAR primer; and C, pomegranate SCAR primer. Lanes in each panel were: 1, the 100-bp DNA ladder; 2, grape juice; 3, apple juice; 4, pomegranate juice; and 5, negative control. Each set of primers was only able to amplify the DNA extracted from one type of fruit juice by using the Fusion 5 method. ........... 156 Figure 6.7 Gel electrophoresis analysis of LAMP products by using different LAMP primers for DNA extracted from different fruit juices with the Fusion 5 method. Different sets of LAMP primers were used for each panel: A, grape primers; B, apple primers; and C, pomegranate primers. Lanes in each panel were: 1, the 100-bp DNA ladder; 2, grape juice; 3, apple juice; 4, pomegranate juice; and 5, negative control. Each set of primers was only able to amplify the DNA extracted from one type of fruit juice by using the Fusion 5 method. .............................. 157 Figure 6.8 LAMP reactions monitored by HNB colorimetric method (A) and calcein/Mn2+ fluorometric method (B). The positive sample with HNB changed color from purple to blue, while the positive sample with calcein/Mn2+ showed strong green fluorescent signals. The color of HNB was visualized under sunlight, while the fluorescent signals of calcein were illuminated by a portable UV laser at 365 nm. The image was recorded by using the camera on an iPhone 6. ..................................................................................................................................................... 159 xxi Figure 6.9 Specificity analysis of LAMP primers for grape (A), apple (B), and pomegranate (C) monitored by using the fluorometric method of calcein/Mn2+. Samples in each panel are 1, grape; 2, apple; 3, pomegranate; and 4, negative control. All the samples were extracted by using the Fusion 5 paper-based DNA extraction method. The fluorescent signals were illuminated by using a portable UV laser at 365 nm and recorded by using the camera on an iPhone 6. .......... 160 Figure 6.10 Calcein/Mn2+ based visualization of LAMP results for the authentication of pomegranate juice and identification of the adulterants. DNA extraction was conducted by using the Fusion 5 method. Each panel demonstrates the LAMP results of one set of primers (A) grape, (B) apple, and (C) pomegranate. Samples in each panel are 1, grape juice; 2, apple juice; 3, pomegranate juice; 4, pomegranate juice and grape juice (1:1 v/v); 5, pomegranate juice and apple juice (1:1 v/v); 6, pomegranate juice, grape juice and apple juice (1:1:1 v/v); and 7, negative control. .......................................................................................................................... 160 Figure 6.11 LAMP reactions in PDMS reaction reservoirs monitored by the fluorometric calcein/Mn2+ method. Positive marks denote samples with template DNA and NC represent negative control that contained no template DNA. ..................................................................... 163 Figure 6.12 To examine the efficacy of DNA elution from the layer 4, different regions on the layer 5 and layer 6 were excised by using a Harris micro-punch. The red circles labeled with different numbers represent the increased distance from the Fusion 5 paper for DNA extraction. ..................................................................................................................................................... 163 xxii List of Abbreviations ATR Attenuate total reflectance BOLD Barcode of Life Database CAN Acetonitrile CART Classification and regression tree CCD Charge coupled device CFIA Canadian Food Inspection Agency CTAB Cetyltrimethylammonium bromide CV Cross-validation DAD Photodiode array detection DF Degree of freedom DMSO Dimethyl sulfoxide EDTA Ethylenediaminetetraacetic acid EU European Union Europol European Criminal Police Organization FDA Food and Drug Administration FDCA Food Drug and Cosmetic Act FSMA Food Safety Modernization Act FT-IR Fourier transformed-infrared FTA Flinder Technology Associates HATR Horizontal attenuated total reflectance HCA Hierarchical clustering analysis xxiii HPLC High performance liquid chromatography HR-MAS High-resolution magi-angle spinning INTERPOL International Criminal Police Organization IR Infrared ITS Internal transcribed spacers IUCN International Union for Conservation of Nature IUU Illegal, unreported and unregulated KNN k-nearest neighbor LAMP Loop-mediated isothermal amplification LDA Linear discriminant analysis LOC Lab-on-a-chip LOD Limit of detection LOQ Limit of quantification matK Maturase K MS Mass spectrometry NC Negative control NMR Nuclear magnetic resonance PC Principal component PCA Principal component analysis PCR Polymerase chain reaction PDMS Polydimethylsiloxane elastomer PES Polyethersulfone PLSR Partial least-squares regression xxiv PVP Polyvinylpyrrolidone rbcL Ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit RMSEC Root mean squares error of calibration RMSECV Root mean squares error of cross-validation RMSEP Root mean squares error of prediction RPA Recombinase polymerase amplification RSD Relative standard deviation SCAR Sequence characterized amplification regions SERS Surface enhanced Raman spectroscopy SIMCA Soft independent modeling of class analogy SSNMR Solid-state nuclear magnetic resonance SVM Support vector machine TRU Traceable resource unit US United States UV Ultraviolet xxv Acknowledgements First, I would like to express my most heartfelt gratitude to my supervisor Dr. Xiaonan Lu, who provided me the chance of being his first Master student and encouraged and persuaded me to further my study and pursue a PhD degree. Dr. Lu’s enthusiasm and prudent attitude towards research are both admirable and inspiring. Being extremely supportive, patient and encouraging, and as a caring listener, he is the best supervisor and mentor one can ever have! Secondly, I want to express my sincere appreciation to my committee members Drs. Rickey Yada, Corey Nislow and Jie Xu for their dedication both in time and knowledge. Gratitude also goes out to Drs. Shenlin Wang and Robert Hanner for their collaborations and technical supports in times of need, especially for allowing me to visit and study in their labs for a few months. Working in labs outside the food science discipline has opened my mind and helped me to establish a more comprehensive research philosophy. A big thank you also goes to all my lab mates and friends at UBC and in Vancouver, with whom we spent lots of happy moments and had many coffee and meals together. Particularly, I would like to thank Jinsong Feng and Shaolong Feng for their company, help, inspiration and encouragement during the past five years, especially when we first started living and studying in a foreign country. I am grateful of having such sincere and grateful friends during my years at UBC. xxvi I also want to express my gratitude to UBC and Mitacs for their financial support through the Four-Year Doctoral Fellowship and the Globalink Research Award. Last but not the least, the most special thank you goes to my families. Thank my parents for constantly encouraging me to study abroad and provided me with freedoms to choose the life I want to live. Thank for their never-ending support both emotionally and financially. Although we are eight-thousand kilometers apart, they are always the first ones I want to share any good news with and the ones I knew I can talk to when I am in any sort of troubles. I also want to express my sincere appreciation to my husband, Weihang Liu. We started dating right before I started my PhD program and started a family right before I finished my PhD program. During the emotional ups and downs in my PhD journey, he has always been there to support me, comfort me, and tolerate my bad tempers. He had made the toughest three years in my life till now less suffering and stressful. xxvii Dedication I dedicate this work to my beloved parents and husband without whom I would not be pursuing a graduate degree abroad and the PhD journey would not be full of joys and loves. I hope you are proud of me as much as I am proud of you. 1 Chapter 1: Introduction 1.1 Food fraud Food fraud, defined as economically motivated adulteration of foods, includes “deliberate and intentional substitution, addition, tampering or misrepresentation of food, food ingredients, or food packaging; or false or misleading statements made for food products for economic gain” 1. Such deceptive actions have existed ever since people started to process and harvest food products. As early as ancient Rome and Athens, laws were enacted to punish manufacturers who processed the adulterated foods for additional economic gain 2. Back then, selective food commodities, such as olive oil and wine, were susceptible to adulteration by other counterfeit components. Nevertheless, food fraud issues appear to be more severe and unpredictable lately based on the increased frequency of exposure and the advanced methods of fraudsters. The scandal of infant formula powder contaminated by melamine in 2008 and horsemeat-adulterated “100% beef” products in 2013 not only aroused panic in China and Europe where the adulterations occurred, but also outranged various parties (i.e. government, genuine food industries, and consumers) worldwide. Recently, through the seven coordinated OPSON operations of Interpol (International Criminal Police Organization) and Europol (European Criminal Police Organization), a total of 30,009 tones and 38.203 millions liters of fake and illicit foods and beverages have been seized, respectively 3. The annual cost of food industry caused by food fraud has been estimated to be 30 to 40 billion US dollars. 2 Figure 1.1 The food integrity matrix demonstrates the definition and relationship between the four types of food-related issues 1. Food fraud is one of the major factors that jeopardize food integrity. The differences between food fraud and the other three food integrity factors (i.e. food safety, food quality, and food defense) lie in the motivations and consequences of the issue (Figure 1.1). As an intentional practice, food fraud primarily aims at extracting economic profits. These cases are difficult to discover by the public and can be easily disguised without the public awareness because the differences between genuine foods and adulterated foods are not easily detectable. Although these four factors are separate matters and impact the food integrity differently, they all compromise the food integrity and can produce significant amount of uncertainties to the other factors and thus cause “side-effects” of food fraud. For example, undeclared extraneous chemicals and low-quality ingredients added to reduce the processing cost could be poisonous to humans, compromise food quality, and pose potential health detriment to allergic and susceptive populations. Moreover, food fraud cases weaken consumer’s confidence and trust in government and food industries and hinder consumers from making informed decisions based on religious beliefs (e.g. halal food being adulterated by pork meat). Recently, the increased frequency and “side-effects” of food fraud issues have concerned the researchers worldwide, leading to the exponential increase in the published research articles and number of citations of studies focusing on food fraud and food adulteration (Figure 1.2). During the past decades, various prevention strategies 3 have been proposed, including establishment of reliable traceability systems, a globally recognized approach to prevent food fraud 4. Figure 1.2 Number of citation (A) and number of publication (B) of research papers with the topic of food fraud or food adulteration (Data from Web of Science). 1.2 Traceability and prevention systems Traceability of food products is defined as “the ability to access any or all information relating to that which is under consideration, throughout its entire life cycle, by means of recorded identification” 5. The three essential components for a traceability system are 1) a coding system to link a traceable resource unit (TRU) with a unique identifier code; 2) an information recording system to register attributes of interest of the TRU; and 3) an information sharing system to enable the documenting transformations and connections between TRUs along the food supply and processing chain 6. Although various traceability systems have been adopted by the food industries to enable product recalls, the inherent disadvantages of those electronic traceability systems are the lack of verification and validation steps of the information being posted into the systems 6. In addition, incorporating a traceability system into the food supply chain required the time and money commitment of different stakeholders from farm-to-fork 7. Therefore, the operating cost for large-scale traceability systems 4 within the supply and processing chain is generally high. With the development of the decentralized blockchain technique, the reliability of information registered in the traceability system can be better guaranteed 8. However, challenges of up-scaling the traceability systems based on blockchain technique, including sophisticated hardware required to store large-volume information and effective verification of the input information at each node, have also been impeding the usage of this tool in food traceability and authenticity 9. Although well-established traceability systems and other prevention systems have been contributing to promoting food authenticity and supply chain integrity, routine inspections and regular analysis of the supply chain and food products are also indispensable. Analytical tools for food fraud become especially significant when the accountability of the traceability systems is questionable. Based upon the characteristics of the adulterants or fraudulent practices, analytical techniques for food authentication can be empirically categorized into two classes: analysis based upon the differences in chemical composition and based upon the variation in DNA sequence. Details of these two categories are presented in the following sections. 1.3 Detection methods based on characterizing the chemical compositions 1.3.1 Traditional methods The presence of adulterants or modification of the percentage of ingredients could result in significant changes in the chemical composition of foods, such as the addition of melamine in dairy products, mixing less expensive fruit juice with the more expensive ones, and substitution of honey by corn syrup. Numerous food fraud detection techniques have been developed using target-specific liquid or gas chromatographic based methods 10-15. Although providing sufficient sensitivity to the target compound(s), these chromatography-based methods require complex sample preparation, tedious experimental 5 procedures, as well as highly trained personnel to operate sophisticated instruments. Moreover, due to the selectivity of the sample preparation and detection approaches, these target-specific techniques can impair the discovery of novel deceptive practices involving alternative exotic materials. These characteristics of traditional methods impede their applications in frequent routine food authentication that requires rapid, accurate, user-friendly, and/or cost-effective techniques. Therefore, alternative analytical techniques revealing comprehensive chemical characteristics and/or requiring less/no-sample preparation are more ideal for rapid screening of fraudulent foods and thus are more suitable for routine and frequent analysis of food products demanded by inspection agencies and food industries. 1.3.2 Techniques to acquire “fingerprint-like” spectra Instead of targeting for specific compounds for differentiation, spectroscopic fingerprinting techniques provide comprehensive information of the overall chemical composition of food samples, which is unique to different food matrices. Thus, differentiation of the genuine and adulterated food products can be achieved based on the variations in the spectral information 16. Infrared (IR) spectroscopy, Raman spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy can provide chemical compositional information by collecting vibrational or spinning signals of functional groups or nucleus in molecules present in samples. As non/less-destructive analytical techniques, these spectroscopic methods have attracted increased attention due to their performance in detecting fraudulent foods 17. IR spectrometers produce the laser energy absorption or transmittance spectra and the technique can be divided into near-, mid- and far-IR spectroscopies based upon the spectral region being collected (near-IR: 12,500 cm-1 to 4,000 cm-1; mid-IR: 4,000 cm-1 to 4,00 cm-1; and far-IR: 400 cm-1 to 10 cm-1) 18. 6 Compared to the other two IR spectroscopies, mid-IR spectroscopy provides more useful and distinctive vibrational information of chemicals, and thus, is commonly used to reveal chemical compositional differences among similar samples. Along with the development of Fourier transformed-IR (FT-IR) spectroscopy equipped with attenuated total reflectance (ATR) accessories, the instrumentation has become more user-friendly and less sensitive to water interference, resulting in increased applicability in food analysis 19. ATR-FT-IR spectroscopy has been applied to differentiate genuine beef meatball from pork adulterated beef meatball 20, to reveal palm oil mixed in extra virgin olive oil 21, to detect milk adulterated with whey, urea and synthetic milk components 22, and to authenticate geographical origins of Anatolian honey 23. Raman spectroscopy is complementary to IR spectroscopy, which collects scattering signals generated from the vibrational functional groups 24. Unlike IR spectroscopy that requires chemicals to change their permanent dipole moment during vibration, Raman spectroscopy only requires changes in polarizability during vibrations, and therefore, provides more information and thus higher resolution about chemical composition 25. Raman spectroscopy has been applied to quantify margarine in butter 26, detect chili powders adulterated by Sudan dye 27, examine the designation of origin of wines 28, and differentiate green coffees of various geographical origins 29. Collecting spinning signals of each atoms, NMR spectroscopy can provide higher molecular resolution compared to the other two vibrational spectroscopic methods discussed above. As technologies advance, NMR spectrometers have become smaller in size and more affordable for high-throughput analysis 30. Therefore, the applications of NMR spectroscopy have been generalized from the structural analysis of pure compounds 31 to metabolic compositional fingerprinting of complex biological samples 32. Recently, extensive studies have been performed to validate the feasibility of NMR spectroscopy for 7 the detection of food fraud, such as discrimination of geographical origins of Atlantic salmon 33 and authentication of wine made with various grape species 34. Extensive research has been conducted using traditional solution NMR spectroscopy that requires liquid samples or solvents extracts from samples, which is not compatible with the non-destructive and high-throughput detection favored by food industries. Therefore, with the assistance of magic angle spinning, solid-state NMR (SSNMR) spectroscopy that is commonly used to interpret intermolecular interactions in solid or semi-solid matrices 35 has also exhibited great potential for the detection of food fraud 36-38. To analyze the complex fingerprint-like spectral information, chemometric models are generally employed. Based upon multivariate statistical analysis, chemometric analysis treats each wavenumber or chemical shift of the spectra as one variable and use different algorithms to achieve classification and quantification analysis 39. Commonly used chemometric analysis include unsupervised clustering analysis [e.g. principal component analysis (PCA) and hierarchical clustering analysis (HCA)], supervised classification analysis [e.g. linear discriminant analysis (LDA) and soft independent modeling of class analogy (SIMCA)], and supervised quantification analysis [e.g. partial least squares regression (PLSR)] 40. Appropriate selection of chemometric models is essential to develop a sensitive and accurate spectroscopic based method. 1.4 Detection methods based on characterizing the DNA sequence 1.4.1 Traditional polymerase chain reaction (PCR)-based methods The chemical composition of agri-food products are susceptible to change by many factors such as processing methods, cultivars, geographical origins, and conditions of the plants or animals before being processed. Therefore, food authentication based on chemical composition requires a comprehensive database, otherwise false positive or false negative results can be frequently observed. 8 However, creating libraries that takes all of the possible factors into consideration is time consuming, if not impossible. Thus, various PCR-based methods that do not rely on such databases have been well established for the detection of adulterants containing biological species different from the genuine food products 41. Based on the specificity of the primers used, PCR-based food authentication methods can be categorized into two classes: species-specific PCR and universal PCR. For species-specific PCR, primers are designed specifically for different biological species. The reaction can be monitored in real-time by the addition of intercalating dyes during DNA amplification 42 or the amplicons can be visualized on electrophoresis gels based on the size of DNA after the reaction 43. In contrast, PCR using universal primers to identify different biological species is commonly followed by a sequencing method 44-46 or a high-resolution melting analysis 47,48 that reveals the difference in the sequence of nucleotides or the composition of four types of nucleotides. Although PCR-based methods have been widely applied to authenticate various food commodities, these methods require costly thermocyclers and usually take a few hours to complete the reaction. Therefore, more rapid and cost-effective methods would facilitate food authentication. 1.4.2 Isothermal amplification Isothermal amplification methods are similar to PCR in that they can amplify designated DNA sequence(s) using a set of well-designed primers. However, by using different enzymes (e.g. Bst polymerase v.s. Taq polymerase), DNA amplification can be achieved with no thermal cycling 49. Representative isothermal amplification techniques include loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), rolling circle amplification and strand 9 displacement amplification. Among the established isothermal amplification techniques, LAMP has demonstrated superior performances in regard to their time- and cost-efficiency, robustness, simplicity and convenience 49,50. Developed by Notomi and others 51, LAMP is well-characterized. Able to amplify DNA at 62-66°C using 2-3 pairs of primers, LAMP is believed to have high specificity and sensitivity, and thus has been extensively applied to food science areas including food safety surveillance and food authentication 52-57. Besides eliminating the need for an expensive thermal cycler for DNA amplification, the end-point detection of LAMP amplicons is simple, making it more favored for routine food fraud screening compared to PCR-based DNA detection methods. End-point analysis of final LAMP products can be achieved by visualizing the formation of magnesium pyrophosphate precipitants 58, monitoring the decrease in pH by pH sensitive dyes 59, detecting the fluorescent signals generated by the complex of double-strand DNA and intercalating dyes 60, and observing the decrease in magnesium ions upon DNA amplification using colorimetric or fluorometric metal chelating reagents 61,62. 1.5 Instrumental free microfluidic lab-on-a-chip platform Lab-on-a-chip (LOC) devices are based on the principle and core techniques of microfluidics and have numerous advantages over the traditional macroscale experiments implemented in the laboratory 63,64. With miniaturized scales, LOC is designed to precisely control and manipulate behaviors of small volume fluids, and thus requires smaller volumes of samples and reagents, as well as generates less waste. Its compact size makes LOC an ideal candidate for high-throughput screening. In addition, micro-sized reaction space results in fast response of reagents towards changes in the environmental condition, such as heating and cooling of micro-chambers/channels. Further, automatic and/or 10 instrument-free analysis can be achieved with delicate design, which could significantly simplify the procedure of the overall analysis. PDMS (polydimethylsiloxane elastomer) and rigid plastic polymers or glass materials have been traditionally used to fabricate microfluidic devices. Although these materials are durable and can achieve highly complicated functions, the fabrication procedures for devices with fine channels and chambers can be complex and costly. In addition, external pressures (e.g. pump and pipette) are necessary to drive the liquids flow in polymers- or glass-based microfluidic devices. Alternatively, paper (e.g. cellulose filter paper and glass fiber) is an emerging material for microfluidic devices due to its low cost, easy fabrication, spontaneous liquid flow, and high recyclability 65. Even though the liquid flow is less controllable on paper-based microfluidic devices and the functions of such devices could be limited by the fragility of paper materials, paper-based microfluidic devices have the potential to achieve functions as complicated as polymer-based devices with technique advancement and smarter design. For example, controlled mixing and separation chemicals using the paper Y-mixer and H-filter 66; time-dependent reagent transportation depends on the pattern of the device 67 and dissolvable barriers 68; paper switch for liquid control utilizing dissolvable bridges 69 and the origami principle 70, and on-chip reagent storage by drying or trapping the reagents on/in specific paper materials 71. These novel designs have enabled paper-based microfluidic devices to integrate a great variety of functional modules to achieve simple sample-to-answer analysis of malaria 72, norovirus 73, and various foodborne pathogens 74. To take advantage of the properties of paper and polymer materials, developing hybrid polymer/paper microfluidic devices is an emerging trend. Combining the cost-effective, easy-fabrication, and autonomous liquid flow properties of paper-based materials with the superior performance of fluid 11 control and reagent storage of polymer-based materials, hybrid polymer/paper LOC devices have been designed to include more complicated functions with little sacrifice to the ease of use 75,76. 1.6 Central hypothesis Based on the advantages and capabilities of these aforementioned techniques, the central hypothesis of my PhD thesis project is that with appropriate experimental design, spectroscopic fingerprinting techniques, isothermal DNA amplification methods and LOC platforms can be applied to monitor food authenticity in a sensitive, accurate, rapid, cost-effective, and/or user-friendly manner, thus reducing economic loss and potential health risks. 12 Chapter 2: Determination of ground beef adulterated with beef and pork offal using Raman spectroscopy and FT-IR spectroscopy 2.1 Introduction Ground or minced meat is an important meat product for food companies and consumers 77 because of its wide usage in food products, such as sausages, burger patties, meatballs, meat fillings and numerous other dishes. However, mincing and grinding change the morphological properties of meat, opening the door for adulteration. Concerns about the safety and integrity of these products has significantly increased since the horsemeat scandal emerged in Europe in January 2013. Similar cases have been noted worldwide, in Russia 78, the United States 79 and China 80. All of these incidents involve the substitution of high-priced or high demand meat products by cheaper and less-consumed meat of other animal species. Extensive studies have been conducted to identify this type of meat adulteration. Techniques including liquid chromatographic based methods, vibrational spectroscopies [e.g. Raman, infrared (IR) and nuclear magnetic resonance spectroscopies], and enzyme-linked immunosorbent assays all exhibited high capability to detecting adulterants from other animals 81-85. Among all of the techniques, DNA barcoding is the most promising candidate with the highest efficiency and accuracy 86. However, DNA barcoding can only differentiate meat of different species and it cannot detect meat adulterated with cheaper meat cuts or the byproducts (e.g. offal and fat trimming) of the same species. According to the “Meat Hygiene Manual of Procedures” enacted by the Canadian Food Inspection Agency, any meat byproduct must be claimed on the label if it is mixed with the processed meat 87. Because of economic benefits, offal may be included in ground meat by unscrupulous manufacturers. Therefore, it is critical to establish methods for the detection of offal in ground meat product. 13 As non-destructive and rapid analytical tools, both Raman spectroscopy and mid-IR spectroscopy have been applied by several research groups for the detection of beef meat mixed with beef offal 83,88-91. Various classification models were successfully applied in these studies for the identification of authentic beef meat and various types of pure offal 83,90 or the differentiation between authentic beef meat from meat samples adulterated by offal 88,89,91. However, no study has yet concluded that spectroscopic techniques are able to identify the specific type of offal in the adulterated beef meat samples. In some published works, partial least squares regression (PLSR) models were constructed to quantify the concentration of each type of offal individually in the adulterated meat samples with high accuracy 83,90,91. However, it was not feasible for these PLSR models to be applied to real samples because the specific type of adulterant in the adulterated beef meat sample was unknown and could not be identified using any classification model constructed in those studies. To accurately quantify the level of adulterant(s), classification model(s) should be able to identify the specific type of adulterant(s) in a sample before predicting the concentration(s) using the corresponding regression model(s); alternatively, a single regression model could be trained to accurately quantify the concentration of the adulterant regardless of its identity. Therefore, to apply the vibrational spectroscopy-based chemometric analysis to samples in the marketplace, further modification and optimization of chemometric analysis and the combination of different chemometric models are required. In addition, as complementary techniques, both Raman spectroscopy and mid-IR spectroscopy have their own benefits and disadvantages. Thus, it is worthwhile to compare their individual performance to attempt to identify the more suitable candidate for this particular application. To the best of our knowledge, this was the first study to compare the performance between Raman spectroscopy and mid-IR spectroscopy for the analysis of beef meat adulterated by various types of 14 offal. Besides three types of beef offal (i.e. beef liver, beef omasum, and beef honey comb tripe), three cuts of pork offal (i.e. pork heart, pork kidney and pork liver) were also included in this study as muscle tissues with a lower cost that could be potentially used for adulteration. Moreover, this was also the first study of investigating the most appropriate sequence for the analysis of the identity and level of offal in adulterated beef meat. The established protocol to detect and quantify the adulterant(s) in ground beef meat is of great importance to the meat industry and governmental laboratories to ensure the safety and integrity of ground beef meat products. 2.2 Hypotheses H1: Raman and FT-IR spectroscopies can detect and quantify offal concentrations in ground beef; H2: One of the spectroscopic methods performs better than another method based upon the discrimination power and quantification accuracy. 2.3 Objectives O1: To collect Raman and FT-IR spectra of authentic ground beef meat, offal, and adulterated ground beef meat samples; O2: To construct chemometric models using the collected Raman and FT-IR spectra for the identification and quantification of offal in authentic ground beef; O3: Base upon the parameters of chemometric models, to compare the performance of these two spectroscopic methods in the discrimination of authentic and adulterated ground beef. 15 2.4 Experimental design 2.4.1 Preparation of meat and offal samples Beef meat and offal samples were purchased from several local grocery stores in Metro Vancouver (BC, Canada). Samples included three types of beef meat cuts (i.e. beef ribeye, beef flank steak, and beef chuck steak), three types of pork offal (i.e. pork heart, pork liver, and pork kidney), and three types of beef offal (i.e. beef liver, beef omasum, and beef honey comb tripe). Each type of sample had five biological replicates purchased on different days during September 2015 to September 2016 from different grocery stores. After purchase, samples were grounded individually using KitchenAid food processor with a meat grinder accessory set at the highest speed, followed by storage at -20°C until further processing or analysis. 2.4.2 Preparation of adulterated samples To substantially decrease cost, large amount of beef meat is needed to be replaced by offal. However, if the percentage of offal in the product is too high, sensory attributes will change and can be easily identified by consumers. Therefore, samples containing beef meat replaced by offal at 10%, 25%, 33.3%, 50%, and 66.6% (w/w) were individually prepared. Three batches of ground beef meat and offal samples prepared as indicated in section 2.4.1were selected randomly. The adulterated samples were prepared in triplicates by mixing ground beef meat samples with each type of offal sample thoroughly at five different concentrations. In total, 90 adulterated samples were prepared and tested directly or stored at -20°C until further analysis. 16 2.4.3 FT-IR spectral collection Perkin Elmer (Waltham, MA, USA) model spectrum 100 FT-mid-IR spectrometer coupled with a Horizontal Attenuated Total Reflectance (HATR) sensor was used for mid-IR spectral collection. Briefly, beef meat, offal and adulterated samples were deposited directly onto the plate to fully cover the ZnSe crystal sensor without leaving any air bubble between the sample and the crystal top plate. Five FT-IR spectra for each individual replicate were collected at room temperature over the wavenumber region of 4000 to 550 cm-1 with a total of 3451 points and 16 accumulations. Spectra of pure samples and samples with various offal concentrations were collected randomly on different dates. 2.4.4 Raman spectral collection Raman spectra were collected using a confocal micro-Raman spectroscopic system coupled with a 785 nm near-infrared line laser (300-mW incident laser power). The spectrometer (Renishaw, Gloucestershire, UK) has an entrance aperture of 50 μm, a focal length of 300 mm, and is equipped with a 1200 line mm-1 grating. Raman signals were recorded by a 578- by 385-pixel charge coupled device (CCD) array detector, with a pixel size of 22 μm. Each sample was tiled onto a gold microarray chip. Before Raman spectral collection, samples were air dried by nitrogen for 20 min because the strong Raman laser power could evaporate the water on the sample surface and this corresponding change in sample surface morphology impacts microscopic focus. A Leica microscope (Leica Biosystems, Wetzlar, Germany) with a 50× Nikon objective (NA = 0.75, WD = 0.37) was used to focus the incident laser onto the sample for Raman scattering spectral collection and the sample area being illuminated with Raman laser was about 15×30 µm. Five Raman spectra for each individual replicate were collected at room temperature over the wavenumber region of 17 1800 to 400 cm-1 with a total of 1491 points and an exposure time of 10 s. Spectra of pure samples and samples with various offal concentrations were collected randomly on different dates. 2.4.5 Vibrational spectral preprocessing OMNIC software version 7.0 (Thermo-Nicolet, Madison, WI, USA) was applied for baseline correction of FT-IR spectra in the spectral fingerprinting region (i.e. 1800 cm-1-1000 cm-1). Vancouver Raman Algorithm software 92,93 was used to remove the fluorescent signals and improve the signal/noise of Raman spectra by a five-order polynomial fit and a 11-point boxcar smoothing. Outliers (i.e. spectra demonstrated prominent signals of fatty acids) were removed directly after the baseline correction. Second derivative transformation for both Raman and FT-IR spectra was also performed using Matlab 2014a (MathWorks Inc., Natick, MA, USA). Spectra before the second derivatization were denoted as “original spectra” in the following content, while spectra after second derivatization were labeled as “2nd derived spectra”. 2.4.6 Chemometric analyses Several chemometric analytical models were constructed to investigate the vibrational spectral features and the relationship between the spectral signals, type of adulterants and the concentration of adulterants. All models were constructed using Matlab 2014a (MathWorks Inc., Natick, MA, USA) with either self-developed codes or the Classification Toolbox for MATLAB version 4.2 94. Containing hundreds or thousands of variables (i.e. wavenumber), vibrational spectra can be too complex for visual identification of the differences and similarities among samples. Therefore, principal component analysis (PCA), an unsupervised analytical method that represents the most 18 variances of the dataset with the fewest variables 95 was applied to reveal the patterns in spectra of different samples. Although PCA 2-dimensional or 3-dimensional score plots could cluster different samples and is often applied for the purpose of categorization, the adoption of supervised classifiers in such studies for sample classification and identification is conceptually correct 96. In the current study, classifiers including LDA, PCA-discriminant analysis (PCA-DA), partial least squares (PLS)-DA, k-nearest neighbor (KNN) and SIMCA were all tested individually on the FT-IR and Raman spectral datasets using none-scaled, mean-centered or auto-scaled spectral signals. Achieving the same objective, the mechanism and principle of these classifiers to extract the information from the spectral datasets are different and thus suitable for different applications 97. LDA is considered as the first multivariate classification technique and the most frequently applied classifier 98. By taking the linear combination of original variables, LDA transforms the high dimensional dataset into a few canonical variables (the number of canonical variables is the number of classes minus one) that maximize the ratio of inter-class variances to intra-class variances. Thus, the scores of the dataset projected on the new dimensions of canonical variates will be used to define the boundaries between classes. However, LDA can only be applied when the number of original variables is less than the number of the data points in the dataset. Therefore, variables selection is conducted by calculating the Wilks’ λ values for each variable using the following equation: &'()*’ - = ./012345266 7218298580:./012345266 7218298580:;8/0<1345266 7218298580: Equation 2.1 Variables with λ value less than 0.5 indicate that the intra-class variances of these variables were smaller than the inter-class variances. Besides calculating the canonical variables, both PCA and PLS algorithms can be used for DA classifiers. Similar to LDA, both PCA-DA and PLS-DA define new 19 variables by taking a linear combination of the original variables (i.e. principal components of PCA and latent variables of PLS). However, in contrast to optimizing the ratio between inter-class variances and intra-class variances, PCA transforms the dataset into the directions maximizing the variances between each data points 99. Instead of optimizing the variances of the dataset, PLS defines latent variables by treating the class identities as dummy variables and maximizing the covariance between spectral dataset and class identity 94. KNN is another type of the traditional classifier 100. First, multivariate distances (e.g. Euclidean distance and Mahalanobis distance) were calculated between each pair of data points 100. Then, the identity of each sample was defined by the k-closest data points around it. KNN has been commonly applied as a standard reference method for more advanced classifiers because it is based upon a non-linear algorithm, which is in accordance with those advanced classifiers 100. The last classifier applied in this study is a class modeling method, namely SIMCA. SIMCA contains multiple PCA models constructed for each class, respectively. Providing a new sample, the projections of this sample on each PCA model will be calculated. The multivariate distances of this sample to other samples in each PCA model will be used to define the identity 101. To provide the best predictability and to avoid overfitting of the models 96, all the samples were separated into two datasets, namely calibration dataset and prediction dataset. Specifically, the calibration dataset contains four replicates of each type of pure sample and three replicates of adulterated samples at 10%, 33.3% and 50% (w/w) levels (i.e. a total of 450 spectra); and the prediction dataset consists the remaining samples (i.e. a total of 225 spectra). In addition to training the model, a calibration dataset was applied to cross-validate the calibration model using a 10-fold cross-validation algorithm. Briefly, the calibration dataset was randomly divided into 10 groups and one group was treated as “unknown” to evaluate the predictability of the model constructed using the other nine groups 102. This calculation was repeated for 10 times until every single group has been treated as 20 the “unknown” sample. The average of the predictability of each repetition was calculated to better represent the performance of the calibration model. Parameters related to the performance of classification models were recorded, including the sensitivity (i.e. 1-type I error), specificity (i.e. 1-type II error), and precision for each class, as well as the error rate (i.e. 1-averaged sensitivity) and accuracy of the overall model 103,104. The equations for the calculation of each parameter are summarized as follow: *=>*'?'@'?A = 4B11<405: 452668C8<D 62EF5< 8/ 45266 G 0B025 /HE9<1 BC 62EF5< 9<5B/I8/I 0B 45266 G Equation 2.2 *J=K'L'K'?A = 4B11<405: 452668C8<D 62EF5<6 8/ 45266<6 B0M<1 0M2/ G/HE9<1 BC 0B025 62EF5<6 9<5B/I8/I 0B B0M<1 45266<6 Equation 2.3 JN=K'*'O> = 4B11<405: 452668C8<D 62EF5< 8/ 45266 G/HE9<1 BC 255 62EF5<6 452668C8<D 8/0B 45266 G Equation 2.4 =NNON NP?= = 1 − 6HE BC 0M< 6</6808780: BC <24M 45266/HE9<1 BC 45266<6 Equation 2.5 PKKSNPKA = 4B11<405: 452668C8<D 62EF5<6 8/ 255 45266<6/HE9<1 BC 255 62EF5<6 Equation 2.6 PLS regression (PLSR) analysis was also performed to provide tools for quantitative analysis of the concentration of offal in the adulterated beef meat. Being able to transform the two datasets to find their best fit, PLSR significantly reduces the number of variables in the spectral dataset and has been extensively applied in chemometrics to correlate the spectral signals with other feature(s), such as the concentration of an analyte in a complicated sample matrix 105. Involving a supervised dimension-reduction method, PLSR often provides models with better quantification accuracy compared with other regression models, such as PCA regression 106. Consistent with all supervised chemometric analyses, prediction dataset with “unknown samples” used to prevent model overfitting is inevitable. Again, all the samples were separated into two datasets as used in the classification models. R2 and root mean squares error of calibration (RMSEC), cross-validation (RMSECV) and prediction (RMSEP) were individually calculated to evaluate the performance of the calibration models. 21 2.5 Results and Discussion 2.5.1 Exploratory analysis The averaged FT-IR and Raman spectra of different types of pure samples are stacked in Figure 2.1 and the assignment of the peaks is summarized in Table 2.1. Differences in Raman spectra between offal and beef meat can be visualized in the region of 1618~1551 cm-1, 1338~1260 cm-1, and 853.2~715 cm-1, mainly associated with the primary and secondary structures of proteins. In comparison, differences in FT-IR spectra between offal and beef meat are in the region of 1765~1726 cm-1, 1572~1359 cm-1, and 1179~1000 cm-1, contributed by all macromolecules including lipids, proteins, nucleic acids and polysaccharides. Among six types of offal samples, beef liver and pork liver exhibited highly similar FT-IR and Raman spectral pattern; the spectral features between pork heart and pork kidney also could not be differentiated by simple visual inspection. In contrast, both beef omasum and beef honey comb tripe demonstrated some obviously different spectral patterns from the other four types of offal. However, considering the relatively high variances in Raman spectral signals within each type of sample (Figure 2.2), the differences in raw Raman spectra among samples might not be as obvious as indicated in Figure 2.1. 22 Table 2.1 Peak assignment of FT-IR and Raman spectra 107,108 Peak assignment of Raman spectra wavenumber (cm-1) assignment wavenumber (cm-1) assignment 1657 Amide I group 1121 Protein backbone 1618 Vibration C=C, tryptophan 1083 C-N stretching of proteins 1606 Ring C-C stretch of phenyl 1032 C-H in-plane bending 1584 C=C bending of protein 1002 Ring breathing of phenylalanine 1551 Tryptophan ring vibration 931.8 Skeletal C-C 1446 CH2/CH3 bending vibrations 853.2 Ring breathing of tyrosine and proline 1392 C-N stretching 828.4 1338 C-H bending vibration 753.4 Tryptophan 1314 C-H bending vibration 715 Nucleotide peak 1260 Amide III protein secondary 661 C-S stretching of cystine 1207 Tyrosine and phenylalane 640.5 C-C twisting & C-S stretching of protein 23 Peak assignment of Raman spectra 1173 C-H bending of tyrosine 616.8 1156 C-C, C-N stretching 531.5 Cholesterol ester Peak assignment of FT-IR spectra wavenumber (cm-1) assignment wavenumber (cm-1) assignment 1740 C=O stretching of lipid 1236 Phosphodiester stretching mode of nucleic acids 1726 1175 C-H bending of tyrosine 1638 Amide I group 1154 C-O stretching vibration 1548 Amide II group 1118 Symmetric stretching of P-O-C 1463 CH2 bending vibrations 1098 Symmetric stretching of PO2- in nucleic acids 1399 CH3 symmetric bending 1080 1310 Amide III protein secondary 1032 O-CH3 stretching of methoxy groups in polysaccharide 24 Figure 2.1 Averaged Raman (A) and FT-IR (B) spectra of each type of meat and offal samples. From the bottom to the top: beef meat, beef honey comb tripe, beef liver, beef omasum, pork heart, pork kidney and pork liver. 25 Figure 2.2 Averaged Raman (A) and FT-IR (B) spectra of each type of beef meat and offal with the band representing the standard deviation. From the bottom to the top: beef meat, beef honey comb tripe, beef liver, beef omasum, pork heart, pork kidney and pork liver. 26 PCA is an effective tool to interpret the variances between data points in chemometric analysis 109. Performing the linear combination of original variables (i.e. principal components), PCA transforms the axis (i.e. original variables), extracts the variances of the whole dataset, and concentrates the variances in the first several principal components 93. Scores of the two or three newly formed principal components are then used to create 2-D or 3-D score plots to demonstrate the relationship between data points. PCA 2-D score plots of pure beef meat and offal samples were constructed using the original FT-IR and Raman spectra (Figure 2.3). Data points of the same type of sample were tightly clustered and separated from others with a few outliers in the FT-IR spectroscopic-based PCA model. Mahalanobis distances were calculated to demonstrate the segregation of data points and the distances between the centroid of each cluster were higher than 9.8, given the common cut-off value of 3 to identify different clusters 110. In comparison, data points of the same type of sample were located loosely with overlapping between groups in the Raman spectroscopic-based PCA model. This PCA classification result was in accordance with the spectral results shown in Figure 2.1 and Figure 2.2 in that Raman spectra within each type of sample demonstrated greater variances compared with FT-IR spectra. 27 Figure 2.3 Score plots of principal component analysis of Raman (A) and FT-IR (B) original spectra. Each symbol represents one type of the sample: star, beef meat; diamond, beef honey comb tripe; cross-mark, beef liver; circle, beef omasum; triangular, pork heart; dot, pork kidney; and asteroid, pork liver. Symbols with blue colors represent tissue samples from beef, while red color symbols are samples from pork tissues. 28 PCA models using the 2nd derivative transformed Raman and FT-IR spectra were also constructed (Figure 2.4). However, the separation of different samples on the score plot of FT-IR spectra was not as good as that using the original FT-IR spectra. In addition, no clear separation was observed for the PCA model using the 2nd derivative of Raman spectra. The separation of different samples on the score plots indicated the difference in spectra between various types of samples, and thus the datasets of the original FT-IR and Raman spectra demonstrated more obvious differences between each type of sample and similarity within the sample type, and therefore were selected for further analysis. 29 Figure 2.4 Score plots of principal component analysis of 2nd derivative Raman (A) and FT-IR (B) spectra. Each symbol represents one type of the sample: star, beef meat; diamond, beef honey comb tripe; cross-mark, beef liver; circle, beef omasum; triangular, pork heart; dot, pork kidney; asteroid, pork liver. 30 2.5.2 Classification analysis Although spectra of pure offal samples could be separated from beef meat in the FT-IR spectroscopic-based PCA score plot, PCA could not separate adulterated samples containing 10% offal from pure beef meat samples (data not shown). Therefore, analytical models with higher capability for class identification were investigated to differentiate authentic beef meat from adulterated beef meat. Briefly, supervised classification models using different algorithms including LDA 98, PCA-DA 99, PLS-DA 94, KNN 100 and SIMCA 101 were constructed and compared for FT-IR and Raman spectral datasets. Minor changes in the algorithm of each classification analysis were also investigated to optimize the performance. 2.5.2.1 Seven-class classification Each dataset was first divided into seven classes, specifically beef meat and other six classes each containing samples of one type of offal (i.e. each class contains meat adulterated with one type of offal as well as pure offal). Wilks’ λ values were calculated for both FT-IR and Raman spectral datasets to reduce the number of original variables by using Equation 1. For FT-IR spectra, 141 variables had Wilks’ λ values less than 0.5 and were selected for further analysis. In comparison, because the intra-class variances of Raman spectra were larger than that of FT-IR spectra, variables with Wilks’ λ less than 0.75 were selected and 116 variables were used for further analysis of Raman spectra. Variables selected from FT-IR spectra were in the range of 1012~1109 cm-1, 1209~1252 cm-1, 1514~1560 cm-1, 1624~1664 cm-1 and 1724~1772 cm-1, while that for Raman were in the range of 473~486 cm-1, 707~720 cm-1, 1109~1188 cm-1 and 1580~1620 cm-1. These regions on the spectra were in accordance with the regions that demonstrated differences between offal and meat observed by visual inspection. 31 Seven-class classification models were constructed using those aforementioned algorithms and their derivatives for the calibration dataset of both Raman and FT-IR spectra. A 10-fold cross-validation was performed for each classification method on the calibration dataset to avoid overfitting of the model. In addition, prediction of new samples using each calibration model was performed to provide better assessment of the predictability of the calibration model. More than ten models were constructed for FT-IR and Raman spectral datasets, respectively. A good model should have low error rate as well as high accuracy, sensitivity, specificity and precision. Models performing similarly in the calibration, 10-fold cross-validation and prediction have less chance to be over-fitted and thus have higher potential to analyze new samples with accuracy 39. When the performance was similar, the model offering the highest sensitivity and specificity for the beef meat group was selected because the clear separation between beef meat and adulterated beef meat is of the utmost importance that can protect the profit of both consumers and the producers. The best three classification models for FT-IR spectra were LDA, PCA-DA using the auto-scaled spectral dataset with 13 principal components, and PLS-DA using non-scaled spectra with 16 latent variables. Model parameters are summarized in Table 2.2. Being used for the prediction of new samples, all three models resulted in accuracy values greater than 80% and the averaged misclassification rate for each class was less than 20%. Among these three models, the LDA provided the highest specificity, sensitivity and precision for recognizing authentic beef meat and thus regarded as the best model. The parameters of the best three classification models for Raman spectra are summarized in Table 2.3. The model with the best performance was PLS-DA using auto-scaled spectra with 10 latent variables, but the prediction error rate was 43%, which was much higher than that of the models for FT-IR spectra. Taken together, FT-IR spectroscopy demonstrated a higher capability in identifying the seven types of samples than Raman spectroscopy. 32 Table 2.2 The best three models for the 7-class classification of all samples using FT-IR spectra DA calibration PCA-DA calibration PLS-DA calibration class # spectra specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision meat 60 100% 100% 100% 100% 93% 100% 100% 97% 100% beef honey comb 65 100% 97% 100% 96% 51% 70% 97% 68% 80% beef liver 65 100% 100% 100% 100% 94% 100% 100% 100% 100% beef omasum 65 99% 100% 97% 92% 72% 60% 96% 72% 75% pork heart 65 99% 100% 97% 98% 97% 90% 96% 98% 83% pork kidney 65 100% 97% 100% 93% 95% 72% 99% 92% 95% pork liver 65 100% 100% 100% 100% 77% 98% 98% 95% 91% DA CV PCA-DA CV PLS-CA 10 fold CV class # spectra specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision meat 60 100% 100% 100% 100% 93% 100% 100% 93% 100% beef honey comb 65 97% 74% 81% 96% 48% 69% 97% 62% 78% beef liver 65 100% 97% 100% 100% 94% 100% 100% 100% 100% beef omasum 65 96% 83% 77% 91% 74% 59% 95% 69% 70% pork heart 65 99% 96% 93% 98% 94% 90% 95% 98% 79% 33 DA CV PCA-DA CV PLS-CA 10 fold CV class # spectra specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision pork kidney 65 99% 94% 92% 93% 95% 72% 99% 92% 92% pork liver 65 99% 95% 96% 99% 75% 94% 98% 94% 91% DA prediction PCA-DA prediction PLS-DA prediction class # spectra specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision meat 60 100% 100% 100% 100% 93% 100% 100% 60% 90% beef honey comb 65 91% 77% 60% 92% 80% 64% 94% 57% 65% beef liver 65 100% 100% 100% 100% 100% 100% 100% 100% 100% beef omasum 65 95% 54% 66% 96% 66% 74% 92% 71% 63% pork heart 65 98% 71% 89% 97% 83% 83% 96% 94% 82% pork kidney 65 95% 86% 75% 96% 80% 80% 96% 86% 79% pork liver 65 98% 86% 91% 99% 86% 97% 99% 86% 97% 34 Table 2.3 The best three models for the 7-class classification of all samples using Raman spectra DA calibration PLS-DA calibration PCA-DA calibration class # spectra specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision meat 60 98% 88% 81% 94% 75% 58% 97% 65% 68% beef honey comb 65 94% 90% 72% 90% 53% 48% 84% 67% 42% beef liver 65 100% 85% 100% 100% 93% 98% 100% 75% 98% beef omasum 65 96% 70% 78% 95% 33% 54% 96% 35% 60% pork heart 65 96% 92% 81% 89% 67% 52% 88% 68% 51% pork kidney 65 97% 82% 84% 93% 48% 56% 92% 57% 56% pork liver 65 100% 83% 98% 98% 87% 88% 99% 70% 95% DA 10-fold CV PLS-DA CV PCA-DA 10-fold CV class # spectra specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision meat 60 94% 60% 55% 94% 72% 57% 96% 60% 65% beef honey comb 65 86% 55% 40% 90% 48% 47% 83% 67% 40% beef liver 65 99% 70% 95% 99% 92% 96% 100% 73% 98% beef omasum 65 90% 42% 42% 95% 37% 56% 96% 33% 59% pork heart 65 88% 52% 43% 89% 63% 51% 87% 65% 48% 35 DA 10-fold CV PLS-DA CV PCA-DA 10-fold CV class # spectra specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision pork kidney 65 91% 42% 44% 92% 45% 50% 91% 50% 50% pork liver 65 97% 53% 78% 97% 87% 84% 100% 70% 98% DA prediction PLS-DA prediction PCA-DA prediction class # spectra specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision meat 60 89% 21% 13% 89% 71% 33% 92% 36% 25% beef honey comb 65 87% 33% 31% 93% 10% 21% 83% 30% 25% beef liver 65 98% 93% 90% 100% 97% 100% 100% 93% 100% beef omasum 65 85% 33% 29% 90% 37% 41% 90% 27% 33% pork heart 65 92% 59% 57% 85% 62% 43% 85% 55% 39% pork kidney 65 93% 37% 50% 95% 37% 58% 93% 40% 52% pork liver 65 95% 43% 62% 96% 83% 78% 99% 67% 95% 36 Although the LDA model for FT-IR spectra provided 100% accuracy in identifying the authentic beef meat in the prediction dataset, there was a prominent increase in the overall error rate in the prediction model than that of the calibration model (i.e. from 1% to 18%), suggesting overfitting in this FT-IR LDA model. Moreover, the misclassification rate was high among different offal samples. Therefore, classification models separating samples into only three classes (i.e. authentic beef meat, samples with beef offal and samples with pork offal) were studied for a more accurate determination of offal adulteration. 2.5.2.2 Three-class classification After dividing each dataset into three classes (i.e. authentic beef meat, samples with beef offal and samples with pork offal), Wilks’ λ values were calculated again for the two calibration datasets of FT-IR and Raman spectra. Wilks’ λ values for Raman spectral calibration dataset were all above 0.75, indicating the large intra-class variation. A total of 89 variables with Wilks’ λ values less than 0.85 were selected for Raman spectral datasets to conduct the following classification analysis. The selected Raman spectra regions were 710~724 cm-1, 849~859 cm-1, 1201~1214 cm-1, 1399~1412 cm-1, and 1772~1800 cm-1. Wilks’ λ values for FT-IR spectral calibration dataset were all above 0.5, and thus the selection criteria was increased to 0.75 with 65 variables in the wavenumber regions of 1168~1184 cm-1, 1454~1471 cm-1, 1545~1573 cm-1, and 1722~1778 cm-1 being selected. Variables that contributed to the inter-class variances were associated with proteins and nucleic acids. The best three models for 3-class classification analysis of FT-IR spectra were LDA, PCA-DA using auto-scaled spectra with 14 principal components, and PLS-DA using mean-centered spectra with 14 latent variables. The parameters of the three classification models are summarized in Table 2.4. Based on the parameters of beef meat class, the DA model had the best performance ( 37 Table 2.5) with low overall error rate as well as 100% specificity and sensitivity for the beef meat class. In addition, this model had an extremely low error rate in identifying new samples (i.e. 4%) and little change occurred in the error rate and accuracy values in calibration, 10-fold cross-validation and prediction. Thus, this LDA model had a high potential for application of market samples. The best three models for 3-class classification analysis of Raman spectra were PCA-DA using non-scaled data with 19 principal components, PLS-DA using mean-centered data with 8 latent variables, and PLS-DA using mean-centered data with 17 latent variables. Again, none of these models provided more than 80% confidence in predicting the new samples into the correct classes ( Table 2.6). Even for the best model, which was the PLS-DA using mean-centered data with 8 latent variables ( Table 2.5), the averaged error rate for identifying new samples of each class was about 31%. Therefore, none of the model constructed using Raman spectral data can be applied for accurate determination of offal in beef meat. In this 3-class classification analysis, FT-IR spectra provided a higher accuracy in predicting new samples compared with Raman spectra. Therefore, FT-IR spectroscopy is more promising for the differentiation of beef meat from beef and pork offal. 38 Table 2.4 The best three models for the 3-class classification of all samples using FT-IR spectra DA calibration PCA-DA calibration PLS-DA calibration class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision beef meat 100% 100% 100% 100% 93% 100% 99% 100% 91% beef offal 100% 97% 100% 97% 89% 96% 99% 96% 98% pork offal 98% 100% 97% 91% 100% 90% 96% 99% 96% DA 10-fold CV PCA-DA 10-fold CV PLS-DA 10-fold CV class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision beef meat 100% 100% 100% 100% 93% 100% 100% 93% 100% beef offal 98% 94% 98% 97% 88% 96% 98% 95% 98% pork offal 95% 98% 95% 89% 96% 88% 96% 99% 95% DA prediction PCA-DA prediction PLS-DA prediction class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision beef meat 100% 100% 100% 100% 80% 100% 96% 93% 61% beef offal 92% 96% 91% 95% 90% 94% 96% 91% 95% pork offal 97% 90% 96% 91% 96% 90% 94% 90% 93% 39 Table 2.5 The optimized models for the 3-class classification of FT-IR and Raman spectral data DA model for FT-IR calibration: error rate 1%, accuracy 99% 10-fold CV: error rate 3%, accuracy 97% Prediction: error rate 4%, accuracy 94% class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision beef meat 100% 100% 100% 100% 100% 100% 100% 100% 100% with beef offal 100% 97% 100% 98% 94% 98% 92% 96% 91% with pork offal 98% 100% 97% 95% 98% 95% 97% 90% 96% PLS-DA 8 LVs model for Raman calibration: error rate 24%, accuracy 77% 10-fold CV: error rate 31%%, accuracy 70% Prediction: error rate 31%, accuracy 65% class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision beef meat 84% 63% 30% 84% 63% 31% 74% 64% 16% with beef offal 87% 68% 81% 86% 66% 79% 85% 57% 77% with pork offal 89% 78% 85% 85% 76% 80% 88% 66% 82% 40 Table 2.6 The best three models for the 3-class classification of all samples using Raman spectra PCA-DA calibration PLS-DA 8 LVs calibration PLS-DA 17 LVs calibration class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision beef meat 99% 53% 84% 84% 63% 30% 86% 72% 37% beef offal 83% 78% 79% 87% 68% 81% 90% 77% 86% pork offal 81% 86% 79% 89% 78% 85% 91% 78% 88% PCA-DA 10-fold CV PLS-DA 8 LVs 10-fold CV PLS-DA 17 LVs 10-fold CV class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision beef meat 98% 53% 78% 84% 63% 31% 83% 68% 31% beef offal 80% 76% 76% 86% 66% 79% 85% 68% 79% pork offal 80% 83% 77% 85% 76% 80% 87% 72% 82% PCA-DA prediction PLS-DA 8 LVs prediction PLS-DA 17 LVs prediction class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision beef meat 97% 14% 25% 74% 64% 16% 78% 79% 22% beef offal 76% 73% 73% 85% 57% 77% 84% 63% 78% pork offal 78% 80% 76% 88% 66% 82% 89% 65% 84% 41 2.5.2.3 Identification of different types of beef and pork offal After classifying samples into three classes, models that could identify either each type of beef offal or each type of pork offal were constructed. Because Raman spectroscopy could not be used to differentiate authentic beef meat samples from adulterated samples, the identification of each type of offal in the sample was not conducted. Therefore, only FT-IR spectra were investigated in this section. Based upon Wilks’ λ values < 0.5, 124 and 41 variables of beef offal datasets and pork offal datasets were selected, respectively, and used for further model construction. The selected wavenumber regions were 1002~1240 cm-1, 1700~1714 cm-1, and 1764~1795 cm-1 for beef offal and 1105~1182 cm-1 for pork offal. SIMCA using mean-centered data with 2, 6 and 3 principal components for each class was the best model for the identification of the specific type of beef offal. LDA using non-scaled spectra had the best performance in recognizing the type of pork offal (Table 2.7). Both models provided over 80% accuracy in predicting the identity of adulterant in new samples. The SIMCA model for beef offal had 100% specificity and sensitivity in recognizing beef liver. All the misclassification occurred between beef honey comb tripe and beef omasum classes. Indeed, beef honey comb tripe and beef omasum are different parts of beef stomach and thus the chemical compositions could be similar. Therefore, SIMCA model was constructed again with beef honey comb tripe and beef omasum treated as one single class. The results of this 2-class beef offal SIMCA model using mean-centered spectra with 4 principal components are summarized in 42 Table 2.8. All of the samples in the calibration dataset and prediction dataset were classified correctly. Thus, FT-IR spectroscopy could be applied to differentiate adulterated beef meat samples containing beef tripe from those containing beef liver when the adulteration level was above 10%. LDA model for pork offal had high accuracy in calibration and 10-fold cross-validation, but the accuracy substantially decreased in prediction, indicating the potential of model overfitting. The misclassification rate between pork heart and pork kidney classes was higher than that between any other combinations. Thus, a 2-class LDA model was constructed with pork heart and pork kidney treated as one single class. The error rate of the model in predicting new samples was decreased from 16% to 9% in this 2-class LDA model (Table 2.8).43 Table 2.7 The optimized models for the 3-class classification of beef and pork offal using FT-IR spectra SIMCA model for beef offal using FT-IR spectra calibration: error rate 15%, accuracy 82% 10-fold CV: error rate 17%, accuracy 83% Prediction: error rate 20%, accuracy 80% class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision beef honey comb tripe 89% 75% 78% 88% 74% 75% 94% 51% 82% beef liver 100% 100% 100% 100% 100% 100% 100% 100% 100% beef omasum 88% 78% 76% 87% 75% 74% 76% 89% 65% LDA model for pork offal using FT-IR spectra calibration: error rate 2%, accuracy 98% 10-fold CV: error rate 5%, accuracy 95% Prediction: error rate 16%, accuracy 84% class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision pork heart 98% 98% 97% 98% 95% 95% 96% 71% 89% pork kidney 99% 97% 98% 98% 94% 95% 84% 91% 74% pork liver 100% 100% 100% 98% 97% 95% 96% 89% 91% 44 Table 2.8 The optimized models for the 2-class classification of beef and pork offal using FT-IR spectra SIMCA model for 2-class beef offal using FT-IR spectra calibration: error rate 0, accuracy 100% 10-fold CV: error rate 1%, accuracy 99% Prediction: error rate 0, accuracy 100% class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision beef stomach 100% 100% 100% 98% 100% 99% 100% 100% 100% beef liver 100% 100% 100% 100% 98% 100% 100% 100% 100% LDA model for 2-class pork offal using FT-IR spectra calibration: error rate 0, accuracy 100% 10-fold CV: error rate 3%, accuracy 97% Prediction: error rate 9%, accuracy 92% class specificity sensitivity precision specificity sensitivity precision specificity sensitivity precision pork heart & kidney 100% 100% 100% 97% 98% 98% 86% 96% 93% pork liver 100% 100% 100% 98% 97% 95% 96% 86% 91% 45 2.5.3 Quantitative analysis 2.5.3.1 Quantitative analysis using Raman spectroscopy Based upon the classification results, Raman spectroscopy could not be used to differentiate beef meat samples adulterated with different types of offal. PLSR models constructed for each type of offal could not be applied for the analysis of market samples because of the lack of knowledge of the identity of the adulterant. Therefore, no prior knowledge can be applied for the selection of the specific PLSR model and only one PLSR model for all six types of offal was developed with the aim of determining the level of the adulterant regardless of its identity. Because of the obvious variation in the collected spectra, PLSR model with 20 latent variables only retained 92% of the variance in the Raman calibration dataset. According to the 5-fold cross-validation model, PLSR calibration model with 7 latent variables had the lowest chance to lead to model overfitting. However, with 7 latent variables, correlation of determination (i.e. R2) of the calibration model was only 0.56, yet that for prediction was 0.32. PLSR model with 15 latent variables was constructed for Raman spectral calibration dataset, which provided high R2 value and low root mean squares error (RMSE) value (Table 2.9). However, obvious model overfitting was observed in predicting new samples with extensive increase in RMSE and decrease in R2. Therefore, Raman spectroscopy is not feasible to quantify offal in ground beef meat with high accuracy. 2.5.3.2 Quantitative analysis using FT-IR spectroscopy To provide a straightforward quantification tool for adulterants in ground beef meat, an FT-IR spectroscopic-based PLSR model was constructed to determine the level of adulterant regardless of its type. In contrast to the Raman spectroscopic-based PLSR model, 10 latent variables 46 extracted more than 95% of the total variances of FT-IR spectra, which was in accordance with the high reproducibility of FT-IR spectra. PLSR model for all adulterated samples with 11 latent variables was constructed and the performance of this model was further evaluated using 5-fold cross-validation and prediction dataset. R2 and RMSE of calibration and 5-fold cross-validation was 0.96 and 0.07, respectively (Table 2.9), indicating high accuracy in quantifying the offal content in ground beef meat. However, R2 and RMSE of prediction was 0.75 and 0.11, respectively, demonstrating some extent of overfitting of the PLSR model. Therefore, quantification of adulterant in ground beef meat without the predetermination of the type of adulterant was not achievable. 47 Table 2.9 Partial least squares regression (PLSR) models for the quantification of offal using Raman and FT-IR spectroscopies. model Na nb factors RMSECc R2 RMSECVd R2-CV RMSEPe R2-P Raman spectroscopy quantification of offal 400 195 15 0.11 0.9 0.16 0.8 0.16 0.56 FT-IR spectroscopy quantification of offal 425 180 11 0.07 0.96 0.07 0.96 0.11 0.75 quantification of beef offal 245 90 9 0.05 0.98 0.06 0.97 0.06 0.97 quantification of pork offal 245 90 8 0.07 0.96 0.06 0.96 0.14 0.58 quantification of beef honey comb tripe 75 30 6 0.03 0.99 0.04 0.98 0.05 0.96 quantification of beef liver 75 30 6 0.03 0.99 0.03 0.99 0.03 0.95 quantification of beef omasum 75 30 5 0.04 0.99 0.04 0.96 0.03 0.95 quantification of pork heart 75 30 10 0.04 0.99 0.07 0.96 0.1 0.45 48 model Na nb factors RMSECc R2 RMSECVd R2-CV RMSEPe R2-P quantification of pork kidney 75 30 9 0.03 0.99 0.04 0.98 0.07 0.94 quantification of pork liver 75 30 6 0.03 0.99 0.04 0.99 0.07 0.81 quantification of beef honey comb tripe & omasum 135 60 5 0.04 0.99 0.04 0.98 0.04 0.95 quantification of pork heart & kidney 135 60 9 0.05 0.98 0.06 0.97 0.14 0.67 aN, number of spectra for calibration. bn, number of spectra for prediction. cRMSEC, root mean squares error of calibration. dRMSECV, root mean squares error of cross-validation (9-fold). eRMSEP, root mean squares error of prediction. 49 All the datasets for the PLSR model were then divided into two parts, namely datasets containing beef offal and datasets containing pork offal. PLSR models were constructed separately for beef and pork offal using 9 latent variables and 8 latent variables, respectively (Table 2.9). Both models demonstrated high accuracy in calibration and 5-fold CV models (R2>0.95 and RMSE<0.07). However, only the PLSR model for beef offal had the capability in predicting the level of offal in new samples with high accuracy (high R2-P and low RMSEP). Besides, the similar R2 and RMSE values in the calibration, 5-fold cross-validation and prediction of the PLSR model indicated a very low potential of model overfitting. Therefore, the PLSR model with 9 latent variables could be used to accurately quantify beef offal in ground beef meat. According to the results of the classification models in section 3.2.3, the specific type of beef offal or pork offal could be identified with acceptable accuracy using the corresponding models. Therefore, PLSR models for each type of offal were constructed and the results are summarized in Table 2.9. PLSR models for beef honey comb tripe, beef liver, beef omasum and pork kidney all had R2>0.94 and RMSE<0.07 even in the prediction of new samples, and thus could all be used to provide accurate estimation of the concentration of specific type of offal in ground beef meat. The PLSR model for pork liver exhibited overfitting when only 6 latent variables were involved in the model, but R2 and RMSE values of prediction were acceptable. However, the PLSR model constructed for pork heart demonstrated obvious overfitting, with R2 decreased from 0.99 in calibration to 0.45 in prediction. Thus, it could not predict the concentration of pork heart in new unknown samples. In section 3.2.3, 2-class classification models that combined beef honey comb tripe with beef omasum and pork heart with pork kidney demonstrated higher prediction accuracy. Therefore, PLSR models combining the two types of beef offal and two types of pork offal were also constructed with the results shown in Table 2.9. The PLSR model for beef honey comb tripe and omasum had similar results to the PLSR models 50 for these two types of offal individually, while the model for pork heart and kidney provided a higher accuracy in predicting the concentration of pork heart, but was worse than the one for pork kidney alone. 2.5.4 Procedure for real sample analysis To determine the authenticity of an unknown sample and/or the identity and level of the offal adulterant in it, several classification and quantification models developed in this study need to be applied consecutively. Based upon the results in the previous sections, we conclude that Raman spectroscopy is not suitable to study the identity and concentration of the adulterant in ground beef meat. Although Zhao and others 91 have concluded that dispersive Raman spectroscopy was able to differentiate authentic beef burgers from beef burgers adulterated with multiple types of offal, identification of specific type of offal and determination of the level of offal was not feasible. In theory, Raman spectroscopy is a complementary technique to FT-IR spectroscopy and has a higher resolution than FT-IR spectroscopy regarding the molecule identification ability. Thus, Raman spectroscopy is expected to have a higher specificity in recognizing samples with different chemical compositions. One reason that Raman spectroscopy lacked the accuracy in the classification and quantification analysis in the current study is that the area of the meat sample being illuminated by Raman laser under the confocal microscope is extremely limited. For samples with high heterogeneity, the reproducibility of Raman spectra can be notably impaired. In future study, a Raman spectrometer with a waveguide fiber can be used to increase the area of samples under illumination in order to improve the reproducibility of Raman spectra as well as the classification and quantification performance. 51 In contrast to Raman spectroscopy, FT-IR spectroscopy demonstrated a high potential for the analysis of authentic and adulterated ground beef meat. Using a combination of the constructed chemometric models, the specific type of adulterant and the concentration could be determined with adequate accuracy. For an unknown sample, the level of the adulterant could not be directly quantified because the PLSR model for all types of offal lacked accuracy in prediction. Therefore, a classification model is required before performing the quantification analysis. Compared with the LDA model classifying samples into seven classes, the LDA model classifying samples into authentic beef meat, beef meat adulterated by beef offal, and beef meat adulterated by pork offal had higher prediction accuracy and thus could be applied first to test the authenticity of a new unknown sample. If the sample was classified into beef offal class, a PLSR model for beef offal can be directly applied to quantify the concentration of the adulterant with high accuracy. Alternatively, SIMCA models could be used to classify the sample into three sub-classes with >80% accuracy or two sub-classes (i.e. beef honey comb tripe and beef liver treated as one class) with >99% accuracy. Then, PLSR models for each individual type of beef offal or the model for combined beef honey comb tripe and beef liver could be applied to quantify the level of adulterant. However, PLSR model for the three types of beef offal together had the highest R2-P and lowest RMSEP. In the real analysis, determining the existence and concentration of the adulterants is usually more important than knowing more details about the type of adulterants. Thus, PLSR model for the quantification of all types of beef offal should be used after the sample is confirmed to contain beef offal. For samples that were classified into pork offal class, PLSR model for all types of pork offal could not be applied to determine the concentration because of its low predictability. However, the LDA model for pork offal provided adequate accuracy (i.e. >84%) in recognizing the specific type of pork offal. 52 After acquiring the identity of the specific type of pork offal in the sample, corresponding PLSR model could be applied to quantify its concentration. However, if the sample was recognized as pork heart, the predicted concentration might not be accurate. Alternatively, LDA model classifying samples into two classes (i.e. pork heart and pork kidney combined) could provide higher accuracy (i.e. >92%) in determining the identity of the sample. Then, PLSR models for pork liver class as well as pork heart and kidney class could be applied accordingly. The accuracy in predicting the concentration of pork heart can be improved (i.e. R2-P increased from 0.45 to 0.67) by reducing the accuracy to quantify pork kidney (i.e. R2-P decreased from 0.94 to 0.67). Taken all the factors into consideration, classifying samples into three classes followed by PLSR model for each type of pork offal is recommended. The flow chart for 1) the differentiation of authentic beef meat from beef meat adulterated with offal and 2) quantification of the adulterant is shown in Figure 2.5. By incorporating and arranging these spectroscopic-based chemometric models based upon the flow chart, a simple spectrum-to-answer interface can be designed using the Matlab gui function (i.e. graphical user interface) to achieve a user-friendly application (Figure 2.6). The lowest concentration of offal in beef meat that can be differentiated from authentic beef meat using classification models was 10%. Therefore, the limit of detection (LOD) of offal in beef meat should be below 10% by using this protocol. 53 Figure 2.5 Illustration of the flow chart for the analysis of new ground beef meat samples. Figure 2.6 An example of the “spectrum-to-answer” Matlab gui interface for the authenticity analysis of ground beef meat. 54 2.6 Conclusion Raman spectroscopy and FT-IR spectroscopy coupled with a variety of chemometric models were applied to detect and quantify offal adulterants in ground beef meat. Confocal Raman spectroscopy lacked the capability of detecting and quantifying offal in ground beef meat. On the contrary, FT-IR spectroscopy with HATR accessory was able to 1) differentiate authentic beef meat from beef meat adulterated with one of the six types of offal, 2) identify the specific type of offal adulterant, and 3) quantify five types of offal in an accurate manner. An optimized protocol for the analysis of ground beef meat using FT-IR spectroscopy was developed with a LOD lower than 10%. This protocol has a strong potential to be applied by governmental laboratory and food industries for the marketplace analysis. By developing user-friendly software including these spectroscopic-based chemometric models, analysis of the authenticity as well as the determination of the adulterant in ground beef meat can be completed in a few minutes. 55 Chapter 3: Determination of Sudan I in paprika powder with solution and solid-state 1H nuclear magnetic resonance spectroscopy 3.1 Introduction Food adulteration issues have been recorded ever since people started to process foods. With the increased complexity and globalization of food supply chain, the incidence rate of food fraud has increased extensively 111-113. Among all types of food adulterations, deliberate addition of illegal chemicals (e.g. melamine in infant formula, formaldehyde in fruits, and Sudan I in paprika powder) into foods to reduce the cost or increase profit could severely damage consumers’ health. Therefore, accurate and rapid determination methods are required to identify food fraud and adulteration and thus guarantee the public health. Conventional techniques to determine the existence of exotic chemicals in foods include chromatographic or immunological-based method coupled with various detection techniques 114-117. Although these methods have high accuracy and sensitivity, they are generally time-consuming and labor-intensive because of complex sample pretreatment procedures, which impede the rapid and accurate determination of food fraud demanded by governmental laboratories and the food industry. In addition, these methods were developed for the determination of specific chemical compound(s) whose appearance or unusual concentration demonstrates the adulteration. However, the deceptive behaviors are dynamic with novel and continuous evolving adulteration methods involving adding chemical(s) that cannot be identified by the established methods. Consumers, government, and even the food industry can be deceived by these fraudulent products 16. Rapid, accurate and untargeted techniques are, therefore, appreciated in the food industry and government to ensure the integrity of agri-food products. 56 Spectroscopic techniques [i.e. Raman spectroscopy, infrared (IR) spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy] are the major fingerprinting techniques that collect signals from all chemical compounds present in a sample, thus can uncover any exotic materials that are not in the authenticated sample matrix, such as a food product. Raman and IR spectroscopies have been extensively investigated to reveal food fraud issues based upon their simple instrumentation, increased accessibility, and the rapid and non-destructive features 118-120. However, these two spectroscopic techniques are not ideal to determine chemicals at low concentrations in food samples because of low sensitivity. In addition, these two techniques respond to the vibration of functional groups in chemicals, but the variety of functional groups in chemicals is limited, overlapping signals of targeted molecules and interferents thus occur frequently. Moreover, functional groups behave differently in absorbing or scattering photons, making it not fully feasible to determine the absolute concentration of several target compounds using one reference chemical. On the contrary, NMR spectroscopy is better in identifying various chemicals because the minor difference in the electron cloud of nuclei can be accurately recorded by NMR spectra 121. Thus, identification of chemicals sharing similar structure can be achieved 122,123. The intensity of NMR spectral signals are only influenced by the number of each type of nuclei present in the sample, thus absolute quantification of several specific chemicals simultaneously can be achieved by applying only one reference chemical 124,125. Although NMR spectroscopy also lacks the sensitivity to determine the chemical(s) at low concentrations based on its principle, its sensitivity can be improved by increasing the number of scans during spectral collection without modifying any hardware. NMR spectroscopy has recently become less expensive and its application has been generalized from analyzing the structure of pure biological samples 126,127 to the determination of chemicals in food 57 products 38,128 and foodomics 129-132. Most of the aforementioned studies were based upon solution NMR spectroscopy that requires liquefied samples, and thus extraction procedures are necessary for solid or semi-solid samples. In contrast, high-resolution magic-angle-spinning (HR-MAS) SSNMR spectroscopy could be an alternative to analyze samples in solid or semi-solid form directly. Quantitative analysis can be achieved by NMR spectroscopy utilizing several strategies, including internal standard, external standard, and linear regression 133. A reference compound is required by both the internal and external standard strategies. Although with one reference compound, concentrations of several targeting compounds could be determined simultaneously, it could be difficult to identify an appropriate reference compound under some circumstances (e.g. overlap of the signals of reference compound and sample matrices, interaction of the reference compound with sample matrices, etc.). Linear regression model correlates the concentration of target molecule with the integral of a specific NMR spectral peak (or a spectral region) and it could be applied to calculate this specific target molecule in new unknown samples. To validate the feasibility of applying both solution NMR and SSNMR spectroscopies in the detection and quantification of hazard chemicals involved in food adulteration issues, the classic model of paprika powder contaminated with Sudan I was selected. Sudan I, a commonly used industrial dye for coloring clothes and wax, has been categorized as a third group genotoxic carcinogen by the International Agency for Research on Cancer 134 and thus it is universally banned for use as a food dye. However, Sudan I abuse in paprika powder and other culinary spice powders with the concentrations of 2.8-3500 mg kg-1 has been frequently reported since 2003 due to the economic gain that can provide a fake color of freshness 135. Although numerous methods have been established for the determination of Sudan I in paprika powder 136,137, traditional methods including the chromatographic and immunological based methods are generally time-consuming and labor intensive. Therefore, the 58 development of the NMR spectroscopic technique for rapid and accurate detection and quantification of Sudan I in paprika powder is of great significance. Because of the difficulty in finding an appropriate reference compound, the potential error induced by reference sample preparation, and only one compound was to be analyzed in this study, linear regression method was adopted for quantitative analysis. To the best of our knowledge, this was the first study to develop and validate NMR spectroscopic techniques for rapid and accurate detection and quantification of Sudan I in paprika powder (i.e. representative food adulteration by addition of illegal chemicals). Moreover, SSNMR spectroscopy was investigated unprecedentedly to determine Sudan I in paprika powder and its performance was compared with solution NMR spectroscopy. 3.2 Hypotheses H1: Solution NMR spectroscopy can determine Sudan I in paprika powder within the commercial adulteration levels; H2: SSNMR spectroscopy can determine Sudan I in paprika powder within the commercial adulteration levels; H3: SSNMR spectroscopy can achieve a more rapid and less labor-involved analysis for Sudan I compared to solution NMR spectroscopy. 59 3.3 Objectives O1: To collect solution NMR spectra of paprika powders adulterated with Sudan I at various concentrations; O2: To collect SSNMR spectra of paprika powders adulterated with Sudan I at various concentrations; O3: To compare the protocol and results of the analytical method developed by using the SSNMR spectrometer with that of the solution NMR spectrometer. 3.4 Material and Methods 3.4.1 Materials Sudan I (³95% purity) was purchased from Sigma Aldrich (Oakville, Ontario). Reagent grade acetonitrile (ACN) and dimethyl sulfoxide (DMSO) was acquired from Fisher Scientific (Toronto, ON). Deuterated solvents including DMSO-d6, ACN-d3, and acetone-d6 were obtained from Cambridge Isotope Laboratories (Tewksbury, MA). Three bags of paprika powder samples of the same brand were purchased from local grocery stores in Vancouver, Canada. Sudan I stock solution at 2500 mg L-1 in ACN and DMSO were prepared using 50 mL volumetric flasks. 3.4.2 Solvent optimization To achieve high extraction efficiency towards Sudan I and low affinity to compounds in paprika powder matrix, the solvent to be used to extract Sudan I from paprika powder was optimized. Three solvents (i.e. ACN-d3, acetone-d6, and DMSO-d6) with high solubility of Sudan I were evaluated. Briefly, 250 mg of unadulterated paprika powder was mixed with 500 µL of the three selected solvents, respectively. After centrifugation at 4500×g for 5 min, 400 µL of the supernatant was transferred to a 60 standard 5-mm solution NMR tube for spectral collection. The spectra were compared between solvents, and the solvent with the fewest peaks in the Sudan I region of the NMR spectrum (i.e. 6.0-9.0 ppm) was selected as the optimum extraction solvent. 3.4.3 Sample preparation Paprika powder samples were determined to be free from Sudan I by high performance liquid chromatography-photodiode array detector (HPLC-DAD) following the same procedures as Fang and coauthors 138. Sudan I stock solution in ACN was diluted to various concentrations with ACN, while the stock solution in DMSO was diluted with DMSO-d6 to reach a final solution containing 50% DMSO-d6. To prepare the samples analyzed by solution NMR, Sudan I was extracted from paprika powder following a procedure similar to that for HPLC-DAD analysis with minor modifications. Briefly, 200 mg of paprika powder samples were spiked with 400 µL of Sudan I ACN solutions at various concentrations. After adding 4 mL ACN, the adulterated samples were vortexed for 30 s, sonicated for 10 min, and centrifuged at 4500×g for 5 min. Each supernatant was collected and vacuum evaporated to dryness, followed by re-dissolved in 500 µL DMSO-d6. An aliquot of 450 µL of the extracted samples were loaded into standard 5-mm NMR tubes. Paprika powder samples spiked with Sudan I at 20, 50, 100, 250, and 500 mg kg-1 were prepared three times on three consecutive days for the determination of the linearity and repeatability. To evaluate the accuracy, paprika powder samples spiked with 50, 75, 125, 200, and 800 mg kg-1 Sudan I were prepared following the same procedures and regarded as unknown samples. 61 For HR-MAS SSNMR spectroscopy, 10 mg samples of paprika powder were transferred directly into standard 4 mm HR-MAS rotors, spiked with 45 µL Sudan I in 50% DMSO-d6 at a series of concentrations, and capped tightly with insert and rotor cap, followed by spectral collection. Paprika powder samples spiked with Sudan I at 225, 675, 1350, 1800, and 2250 mg kg-1 were prepared three times in three consecutive days to determine the linearity and repeatability. To evaluate the accuracy, paprika powder samples with 337.5, 450, 1125, 1575, and 3150 mg kg-1 Sudan I were prepared following the same procedures and treated as unknown samples. 3.4.4 Instrumentation 1H solution NMR spectroscopic experiments were conducted using a Bruker’s spectrometer operating at proton frequency of 600.13 MHz equipped with a TXI probe. The sample temperatures were controlled at 300.0 K. All spectra were collected using a single pulse with a 30° pulse angle, 2.65 s acquisition time, 4 s recycle delay, and a sweep width of 20.62 ppm. The number of scans was 128, requiring ca. 15 min for each spectral collection. 1H HR-MAS SSNMR spectroscopic experiments were performed using a wide-bore Bruker spectrometer operating at proton frequency of 600.25 MHz equipped with a 4.0 mm 1H/X/Y probe. The sample temperatures were controlled at 300.0 K and the samples were spun at 7 kHz. The spectra were collected using a pulse sequence with a 90° pulse angle, 1 s acquisition time, 4 s recycle delay, and a sweep width of 20.02 ppm. The solvent signal was suppressed by pre-saturation at the frequency of DMSO resonance. The number of scans for the experiments was set at 384, with total experimental time of ca. 32 min per sample. 62 3.4.5 Spectral analysis TopSpin 3.2 software (Bruker Biospin GmbH, Rheinstetten, Germany) was applied to preprocess all NMR spectra that were collected using both solution NMR and SSNMR spectrometers. Spectra were phased and baseline corrected manually. After peak assignments, integration was applied to analyze the resonances of Sudan I. The recovery of Sudan I from paprika powder for solution NMR analysis was evaluated by comparing the spectral features of the extracts from contaminated paprika powder samples with the standard Sudan I solution at the same concentration. Assessment of recovery was not required for SSNMR analysis because no sample pretreatment was involved to cause the loss of target chemical. The relative standard deviation (RSD) was calculated based upon Equation 3.1 to evaluate the repeatability of the experimental design. Linear regression models correlating Sudan I concentrations and the integrals of the peak were constructed using Microsoft Excel 2010 (Redmond, WA) and the linearity was represented by the coefficient of determination (R2). The concentrations of the unknown samples were calculated using the established linear regression models, and the accuracy was evaluated using Equation 3.2. The limit of detection (LOD) and limit of quantification (LOQ) were determined as three times the standard deviation of noise and 10 times the standard deviation of noise 139, respectively. RSD = (&'()*(+* *-./('/0) 1-())⁄ × 100 Equation 3.1 Accuracy = (=(>=?>('-* =0)=-)'+('/0) &@/A-* =0)=-)'+('/0)⁄ ) × 100 Equation 3.2 63 3.5 Results and Discussion 3.5.1 Solvent selection To achieve the highest extraction efficiency of Sudan I from paprika powder and avoid simultaneous dissolving of impurities from paprika powder, an appropriate solvent with high solubility of Sudan I and low affinity towards compounds in paprika powder should be selected. ACN, DMSO, and acetone all achieved high solubility of Sudan I, but their different polarity and molecular size could result in various affinities towards other chemicals. Thus, their affinities towards compounds in paprika powder were compared by 1H solution NMR spectrometer, and the results are shown in Figure 3.1. Figure 3.1 1H solution NMR spectra of paprika powder extracted by DMSO-d6 (bottom), acetone-d6 (middle), and acetonitrile-d3 (top). As the NMR resonances of Sudan I appear at the region of ca. 6.0-9.0 ppm, spectra at this specific region in Figure 3.1 were compared. ACN had the least affinity towards compounds in paprika powder followed by acetone, while DMSO had remarkably more peaks within this region, exhibiting the high 64 solubility of paprika powder matrices in DMSO and low solubility in ACN. However, as heat can be generated using the high-speed spinning in HR-MAS experiment, solvents with low boiling point might burst out of the rotor and damage the instrument. Consequently, DMSO-d6 was selected as the solvent to extract Sudan I analyzed using HR MAS (boiling point of ACN, 82°C; acetone, 56°C; and DMSO, 182°C). For the solution NMR experiment, ACN was used to extract Sudan I from paprika powder, but DMSO-d6 was adopted to re-dissolve the products after vacuum evaporation, as different solvents result in shifts in peaks that are related to the target molecules and make the results of the two spectrometers difficult to compare. 3.5.2 Determination of Sudan I by solution NMR spectroscopy Paprika powder samples spiked with various Sudan I concentrations were determined using 1H solution NMR after extraction, vacuum evaporation, and re-dissolving, as demonstrated in Figure 3.2. Figure 3.2 Schematic illustration of experimental design. NMR, nuclear magnetic resonance; HR MAS, high resolution magic angle spinning. 65 Paprika powder samples spiked with Sudan I at five different concentrations (i.e. 20, 50, 100, 250 and 500 mg kg-1) were analyzed using 1H solution NMR spectroscopy in triplicate on three consecutive days. Figure 3.3 shows the comparison of the spectra. The peaks at 8.57, 7.97, 7.88, 7.80, 7.62, 7.56, 7.47, 7.39, and 6.94 ppm are the nine featured Sudan I NMR peaks, denoting each proton in the Sudan I structure. To achieve a lower LOD and LOQ, the integral of the strongest peak at 7.88 ppm was used to quantify Sudan I in paprika powder. Figure 3.3 1H solution NMR spectra of paprika powder spiked with Sudan I at various concentrations (0, 20, 50, 100, 250, and 500 mg kg-1). The recovery of this proposed analysis was determined by comparing the spectra of the extraction from paprika powder sample spiked with Sudan I at 100 mg kg-1 (i.e. 40 mg L-1 Sudan I in the final extraction if 100% recovery is achieved) with the standard Sudan I DMSO-d6 solution at the same concentration. The identical intensities of these two spectra (Figure 3.4) validated the full recovery of Sudan I from paprika powder by the current sample pretreatment procedure. The RSD values of the triplicate samples at five different concentrations were in the range of 0.3%-7.5%, with an average of 66 4.6%. The higher the concentration of Sudan I in paprika powder, the lower the RSD value. The low RSD values represent a good repeatability of this proposed 1H solution NMR spectroscopic method. Figure 3.4 1H solution NMR spectra of paprika powder spiked with Sudan I at 100 mg kg-1 (bottom, black) and standard Sudan I ACN solution at 40 mg L-1 (top, blue). The shift of the peaks was because of the different solvent used for these two spectra. The linear regression model correlating the integrals of the peak at 7.88 ppm and the concentration of Sudan I in paprika powder is shown in Figure 3.5. The high R2 (close to 1) and relatively small intercept of the regression equation represent high correlation and low baseline effect of the spectra at 7.88 ppm. Five paprika powder samples spiked with Sudan I at different concentrations were prepared separately and treated as unknown samples for the determination of the accuracy of this proposed method. The linear regression model (Figure 3.5) was applied to calculate the concentrations of Sudan I in the unknown samples, and the accuracy was in the range of 94%-104% with an average of 98%. The high 67 accuracy demonstrates the feasibility of accurate quantification of Sudan I in paprika powder using 1H solution NMR spectroscopy. Figure 3.5 Linear regression model for quantitative analysis of Sudan I in paprika powder by 1H solution NMR spectrometer. The LOD and LOQ were calculated based upon the IUPAC standard method 139. The integrals of the spectra for the uncontaminated paprika powder samples were determined and the standard deviation was calculated accordingly. The LOD of this proposed method was 6.7 mg kg-1, while the LOQ was 22.5 mg kg-1. Both the LOD and the LOQ are within the range of commercial Sudan I contamination levels (i.e. 2.8-3500 mg kg-1). Because NMR signals respond to the mole number of the chemicals present in the sample, specifically protons, the LOD and LOQ of this solution NMR spectroscopic method under the specific instrumentation are 5.5 and 18.1 nmol, respectively. By increasing the weight of paprika powder for y = 40014x - 59516R² = 0.998305000000100000001500000020000000250000000 100 200 300 400 500Integral of the peak at 7.88 ppm (a.u.)Sudan I concentration in paprika powder (mg kg-1)68 extraction, the mole number of Sudan I contained in the final DMSO solution could be increased accordingly, leading to improved LOD and LOQ reported based upon the weight of paprika powder. Moreover, with an increased number of scans, the intensity of the signals as well as the signal-to-noise ratio could be improved, resulting in higher accuracy for the quantification of low concentration adulteration as well as lower LOD and LOQ. 3.5.3 Determination of Sudan I by SSNMR spectroscopy In general, sample pretreatment in the HR-MAS SSNMR experiment is much simpler than our solution NMR spectroscopic method, resulting in reduced labor (Figure 3.2). To prepare the spiked samples, 10 mg paprika powder was packed into SSNMR rotor and mixed with 45 µl Sudan I DMSO-d6 solution, followed by HR-MAS NMR spectral collection. The volume of the 4-mm rotor is 50 µl, which is about 10 times smaller than the volume of the sample analyzed by solution NMR spectroscopy. To achieve a similar signal-to-noise ratio, the number of scans should be set higher than the one used in solution NMR spectroscopy 140. Thus, spectral collection using SSNMR requires longer time than solution NMR spectroscopic method. Triplicates of five standard paprika samples with various Sudan I concentrations (i.e. 225, 675, 1350, 1800, and 2250 mg kg-1) were analyzed using HR-MAS SSNMR spectroscopy in three consecutive days. Figure 3.6 exhibits the comparison of representative spectra of samples at five different concentrations. More bands were observed in Figure 3.6 compared to Figure 3.3 because of the high affinity of DMSO towards chemicals in paprika powder. However, no signal overlapping was observed at 7.88 ppm on the spectra and thus, the integral of the peak at 7.88 ppm was again used to quantify Sudan I in paprika powder. 69 Figure 3.6 1H HR MAS SSNMR spectra of paprika powder spiked with Sudan I at various concentrations (0, 225, 675, 1350, 1800, and 2250 mg kg-1). RSD of triplicate samples at each concentration was calculated and were within the range of 1.5-6.7%, with an average of 3.9%. The low RSD values demonstrate a good repeatability of this proposed HR MAS SSNMR spectroscopic method. The linear regression model constructed by five standard samples (i.e. 225, 675, 1350, 1800, and 2250 mg kg-1 Sudan I in paprika powder) in triplicate was constructed, and the results are shown in Figure 3.7. The high R2 represents a good linear regression relationship between the concentration of Sudan I in paprika powder and the integral at 7.88 ppm in the spectra, which can be used to accurately quantify the concentration of Sudan I in paprika powder. However, the high intercept showed the interference of the spectral baseline or the compounds from paprika powder dissolved by DMSO, which could be optimized by using other solvents. 70 Figure 3.7 Linear regression model for quantitative analysis of Sudan I in paprika powder by 1H HR MAS SSNMR spectrometer. The accuracy was also determined by five unknown samples spiked with Sudan I at 337.5, 450, 1125, 1575, and 3150 mg kg-1, respectively. The accuracy values were within the range of 98-109% with an average of 105%. The accuracy of SSNMR spectroscopic method was not as good as the accuracy of the solution NMR spectroscopic method, which could be attributed to the small weight of the samples that are analyzed, leading to more experimental errors. The accuracy of this proposed SSNMR spectroscopic method could be improved by using NMR rotor with larger inner diameter, allowing a larger volume with more paprika powder samples to be analyzed. The LOD and LOQ are 128.6 and 313.7 mg kg-1, respectively, for the SSNMR spectroscopic method. Although higher than the values determined using 1H solution NMR spectroscopy, the LOD and LOQ determined using this proposed SSNMR spectroscopic method are still within the commercial Sudan I y = 282.87x - 13248R² = 0.98850100,000200,000300,000400,000500,000600,000700,0000 500 1000 1500 2000 2500Integral of the peak at 7.88 ppm (a.u.)Sudan I concentration in paprika powder (mg kg-1)71 contamination level (i.e. 2.8-3500 mg kg-1), and can be further improved with the increased number of scans. Reported in mole number, the LOD and LOQ of this HR-MAS SSNMR spectroscopic method under the given instrumentation are 5.2 and 17.3 nmol, respectively, which are similar to the ones determined using solution NMR spectroscopy. By using a rotor with larger diameter, the weight of the paprika powder samples analyzed using SSNMR spectroscopy could be increased, leading to increased mole number of Sudan I present in NMR spectrometer and subsequently reduced LOD and LOQ reported based upon the weight of paprika powder. The overall analysis time required for solution NMR and SSNMR spectroscopies were 35 min and 32 min, respectively. Although SSNMR spectroscopy exhibited relatively lower sensitivity compared to solution NMR spectroscopy based upon the LOD and LOQ values reported on the weight of paprika powder, substantial time and labor can be saved by using SSNMR spectroscopy when a large sample set is to be analyzed (e.g. 60 min could be saved to analyze 20 samples). 3.5.4 Comparison of techniques for the determination of Sudan I in food samples Extensive studies have been implemented to improve and optimize the determination of Sudan I in paprika powder, such as chromatographic-based ultraviolet (UV) spectroscopic or mass spectrometric (MS) methods 116,136,141-143, immunological-based colorimetric methods 137,144,145, as well as Raman and infrared spectroscopic methods 27,138,146,147. Amongst these aforementioned techniques, immunological-based colorimetric methods exhibited high sensitivity for the detection of spice contaminated with Sudan I at several part per billions levels 137. 72 However, these methods are generally time-consuming, requiring several hours to complete the labor-intensive sample pretreatment and analytical procedures 144. A modified immunological-based colorimetric method, reported by Oplatowska and others 145, can complete the overall immunological assay for the detection of Sudan I in spice power in ca. 20 min, but the high RSD demonstrated the poor repeatability and accuracy for quantitative analysis. Moreover, the production of appropriate antibody is generally time consuming and expensive, and strict environmental conditions are demanded for the storage and application of immunological-based methods, posing challenges if these methods are to be adopted by government laboratories or the food industry for the analysis of large sample sets. Chromatographic-based UV or MS methods have comparable or even higher sensitivity than immunological assays and exhibited the highest accuracy amongst these techniques 141,143. However, these chromatographic based methods are also time-consuming and labor intensive 116,142, and do not address the rapid analysis required by food industries and government laboratories. In addition, filtration procedures, commonly used to remove macromolecules and undissolved food matrices that might interfere with the chromatographic analysis or cause damage to the instrument 141,142, could potentially induce a loss of Sudan I and thus the underestimation of the concentration of Sudan I due to the adsorption by the filter (Figure 3.8). 73 Figure 3.8 1H solution NMR spectra of 100 mg L-1 Sudan I standard solution in ACN without being filtered (bottom, black) and 100 mg L-1 Sudan I standard solution in ACN after being filtered by 0.45 µm nylon syringe filter (top, blue). Approximately 40% Sudan I lost during the filtration. Raman and infrared spectroscopic methods are the most rapid ones. According to Haughey and others 27, no sample pretreatment is required to analyze the Sudan I contaminated chili powder, and the spectral collection only took a few minutes. Moreover, the linear regression models demonstrated high potential for accurate quantification of Sudan I in chili powder samples. However, the LOD for the near-infrared and Raman spectroscopies were 2500 and 8800 mg kg-1, respectively, based on the weight of chili powder, which are much higher than the commercial level of Sudan I dye in spice powders and thus the methods are not suitable for food industry and government laboratory. Surface enhanced Raman spectroscopy (SERS), a derivative of the conventional Raman spectroscopy, has been applied for the determination of Sudan I in chili powder to reduce the LOD to 0.6 mg kg-1 with the 74 assistance of well-designed noble metallic nanostructures 146. However, extensive sample pretreatment is required to remove the majority of food components (i.e. liquid-liquid extraction combined with solid phase extraction) because the SERS substrate non-selectively enhanced the Raman scattering signals of all organic matters present in a sample 95. With an added separation element, such as molecularly imprinted polymers, more efficient extraction and separation of Sudan I can be achieved and the analysis time has been reduced to less than 15 min 138; however, the identification of the enhanced Raman signals for Sudan I might be problematic and the reproducibility of the SERS spectra are not ideal. Compared to the other well-established methods, NMR spectroscopic methods are still in their infancy for the determination of chemical hazards in foods. 1H solution NMR spectroscopy has been applied to differentiate four types of Sudan dyes in spice powders at high concentrations 123,148, but quantification of Sudan I in food samples was not conducted in any previous study. Our current study is also the first time that SSNMR spectroscopy has been applied for the detection and quantification of Sudan I in food matrices. In general, both of these two spectroscopic methods exhibited extraordinary performance in their repeatability, with RSD values similar to those for chromatographic-based methods 143. The high R2 and accuracy for the quantification of unknown samples validated the outstanding quantification capability. In addition, simple sample pretreatment for NMR spectroscopies results in improved recovery of the target molecules during analysis and substantial time and labor have been saved (i.e. 35 min for solution NMR spectroscopy and 32 min for SSNMR spectroscopy). The weaknesses of NMR spectroscopic techniques are the relatively high instrumental costs and the relatively low sensitivity for the detection of trace level compounds. Because of the simple and rapid sample pretreatment and analysis, the costs for the analysis of each sample using NMR spectroscopy are affordable; with increased amount of samples extracted, increased probe size of SSNMR spectrometer or increased 75 number of scans, an improved sensitivity could be achieved. It is worth mentioning that more than 100 mg kg-1 Sudan I is required to provide a change in the color freshness of paprika powder 143, making these NMR spectroscopic methods adequate to identify the intentional addition of Sudan I into paprika powder for commercial benefits. Finally, being a fingerprinting technique analogous to infrared and Raman spectroscopies, NMR spectroscopy can be applied as a untargeted method to identify exotic materials present in the food system and thus reveal novel types of food adulteration and fraud issues. Although the price for NMR spectrometers are relatively high (c.a. three to four times the price as for Raman spectrometer), the cost for the analysis of each sample is affordable (c.a. $6-30). With the advance in the magnetic materials and instrumental hardware, the cost for conventional NMR spectrometers could be reduced. In addition, benchtop NMR spectrometer could have improved sensitivity to be applied for the determination of chemicals at low concentrations. Further studies could be conducted to develop one simple method for the simultaneous detection and quantification of various chemical hazards present in food matrices to substitute the comprehensive multiple tests, resulting in less labor, time and money involvement. 3.6 Conclusion Both the solution NMR and SSNMR spectroscopies can be used to rapidly and accurately quantify Sudan I in paprika powder. The average accuracy for the quantification of Sudan I in unknown samples using solution NMR spectroscopy was 98%, while the accuracy of SSNMR spectroscopy was 105%. Based upon the weight of paprika powder, the LOD and LOQ were 6.7 and 22.5 mg kg-1 for solution NMR spectroscopy, and 128.6 and 313.7 mg kg-1 for SSNMR spectroscopy. The LOD and LOQ were 5.5 and 18.1 nmol for solution NMR spectroscopy and 5.2 and 17.3 nmol for SSNMR spectroscopy, if calculated based upon the mole number of Sudan I in NMR spectrometers. LOD, LOQ, and accuracy can be further improved by increasing either the weight of paprika powder or number of scans. The 76 overall analysis time was 35 min and 32 min, respectively, reducing half of the analysis time required by traditional analyses. As a fingerprinting technique, NMR could identify exotic chemicals in foods to reveal novel types of adulteration, and thus ensure the integrity and safety of food system. 77 Chapter 4: A Market survey study using DNA barcoding revealed fraudulent labeling in fish products 4.1 Introduction Fish is one of the major protein foods with an annual consumption of over 100 million tonnes worldwide 149. Canada had an annual per capita fish consumption of 22.5 kg during 2013 to 2015 and ranks 21st in fish harvesting by weight globally 149. As a leading fishing nation, fish species harvested/produced in Canada include salmon, herring, hake, redfish and haddock 150. More than 900 species of fish are available in the market 151. Canada is among the top ten fish exporters in the world with 85% of its harvest being exported 149, but it is also heavily dependent upon seafood imports which constitute approximately 80% of the seafood available in Canadian markets 152. Interestingly, fish imported into Canada have often been harvested or farmed domestically in Canada but processed elsewhere, commonly in China. In a typical fish supply chain, four or even more independent countries could be responsible for the different steps in fish processing, including harvesting/farming, primary processing (e.g. freezing, filleting and gutting), secondary processing (e.g. canning and breading), and packaging. Finally, the packaged fish products could be sold domestically or exported to other countries and could include the country where the fish was harvested in the first place. Within such a complex and fragmented supply chain, effective and efficient tracing and tracking of products is challenging, and implementation difficult 153. One of the consequences associated with such complicated seafood supply chains is the high mislabeling rate (intentional and unintentional practices) of fish products around the world, including the United States (US) 112, China 154, Italy 155, the United Kingdom 156, and South Africa 157. Intentional substitutions of high quality fish with less expensive fish, or selling fish from illegal, unreported and unregulated 78 fishing (IUU) operations are common. Additionally, unintentional mislabeling due to the misidentification of fish species or ambiguous labeling regulations were also recognized. Regardless of the motivations for mislabeling, lack of integrity in global fish markets lead to: 1) economic damage to consumers and responsible fish industries; 2) diminished consumer trust in governmental agencies and the food industry; 3) potential health threats to susceptible populations from mislabeling (e.g. histamine forming scombroid fish substituted for non-scombroid fish); 4) the creation of a legal market for products of IUU fishing; and 5) impeding consumers from making informed decisions to support sustainable fisheries 158. The high prevalence of fish mislabeling can be attributed in part to the complexity of the supply chain, but the inability to monitor and detect species substitution has provided opportunities for unscrupulous marketers 159. Other factors, such as the inability to visually identify fish like rockfish to the species level makes identification difficult, even for fisherman. Furthermore, the removal of species-specific features such as the head, fins, skin and scales, during fish processing greatly complicates reliable visual identification. Lastly, the differences in vernacular and common local names of fishes, having the same common name for different fishes (e.g. redfish, snapper, salmon, trout, and pollock) in the domestic and international markets, and lack of knowledge or appropriate use of the proper scientific and internationally recognized names for fishes have created discrepancies in the standardization of labeling in the international fish trade 160. Having thousands of different species and sharing similar morphological characteristics among species, fish are unique compared to the other animal based food sources and have inherent properties favored by unscrupulous industries. Therefore, to protect consumers and the integrity of the fish industry, steps should be taken to reduce seafood mislabeling and the resulting consumer fraud. 79 Over the past decade, several significant amendments to government standards have been implemented. For example, the European Union (EU) requires all the fish products to be labeled with sufficient details to enable traceability (i.e. scientific name, common market name, wild/farmed, harvesting/production areas and harvesting/production methods) 161. In the US, the Food and Drug Administration (FDA) has adopted a DNA barcoding method to replace their previous fish identification analytical method based on isoelectric focusing 162. These changes have been made to improve the integrity of fish marketing. In addition, massive media coverage on fish fraud and mislabeling issues has aroused the attention of consumers and put pressure on fish processors, distributors, restaurants and retailers to accurately label the fish being sold. According to Mariani and others 163, media pressure played an important role in reducing cod mislabeling rate from 35% to 0% in grocery stores in Dublin, Ireland between 2009 to 2012, indicating that actions taken by EU were effective. A transnational study conducted in Western Europe from 2013 to 2014 unveiled a significant improvement in the EU fish market with less than a 5% mislabeling rate 164, although the situation in some Southern European countries was not as promising as this due to some cultural variations (e.g. different fish list recognized by each country) 165. However, less encouraging results were observed in an Oceana nationwide study conducted in the US from 2010 to 2012 with an average mislabeling rate of 33% 112. In more recent studies that analyzed commonly consumed sushi species in Los Angeles 166, salmon products in grocery stores and restaurants 167, and sushi and wholesale/retails samples in three US metropolitan areas 168, mislabeling rates were 47%, 43% and 16.3%, respectively. Compared with the lower fish mislabeling rate in the EU countries, mislabeling in US markets is attributed to the less strict enforcement of labeling regulations 164. The labeling regulations for fish products in Canada require no detailed information regarding the identity of the fish themselves (e.g. geographic origins, wild or farmed, and catching or farming 80 methods), while the US requires the addition of labeling the fish as wild caught or farmed. Labeling the seafood product with a common name listed by US FDA or Canadian Food Inspection Agency (CFIA) and the country of origin (i.e. country where the fish was last transformed) is acceptable in the corresponding country. However, inconsistencies between the use of common name of fishes accepted by the US and Canada are not unusual, even though the US is the largest importer and exporter of Canada’s seafood 152,169. Such discrepancy has resulted in confusion in regional markets since Pacific rockfish (Sebastes sp.) can be legally sold as Pacific red snapper in Washington, Oregon and California – though not in Canada – causing confusion with the more expensive Gulf of Mexico red snapper (Lutjanus sp.) 170. Although the latest Food Safety Modernization Act (FSMA) issued by the US FDA has included a chapter for protecting food against intentional adulteration 171, food fraud (i.e. economically motivated adulteration) is not covered by the act 172 unless the species substitution would result in adverse health impacts, such as substitution of a scombroid (histamine forming species) for one that does not form histamine, or in cases where species substitution would result in allergenic reactions. The US Food Drug and Cosmetic Act (FDCA), including provisions in FSMA, require that fish be labeled as an allergen and species name be included on product labels under the amendment to the FDCA in the Food Allergy Labeling and Consumer Protection Act (FALCPA) in 2004. Section 403 of the FDCA also prohibits misbranding of food, and this includes species substitution. However, based only on a generic common name and country of origin provided on the label, mislabeling of seafood are difficult to control. Similarly in Canada, fish industries are monitored according to the “Fish Inspection Program Sampling Procedures”. This standard explicitly states that the fish will be subjected to sensory, chemical indicator, package integrity and net content analyses, container integrity analysis, and/or microbiology and chemistry analyses 173. The focus of the fish inspection program is to eliminate physical, chemical and biological safety concerns instead of monitoring the authenticity or legality of the fish industry. Although the “Consumer Packaging and Labelling Act” is intended to 81 provide consumers with truthful and not misleading labels, with limited information provided on the label and insufficient inspection, effective control of fish mislabeling is challenging. In the US, dozens of articles have been published investigating the integrity of seafood markets 112,167,174-177, but only three research articles could be identified to date analyzing the mislabeling rates of Canadian seafood markets in peer-reviewed journals 178-180, in addition to one formal report generated by Oceana Canada 181. Moreover, the majority of the samples (n = 719) collected in those studies was from the Eastern Canada in Ontario and Montreal or the US, while only 64 samples were collected from the West Coast of Canada in Vancouver. In addition, most of the market survey studies conducted in the US and Canada used DNA barcoding method, identifying different fish based upon the sequence of a 650-bp mitochondrial COI DNA fragment, also believed to be the DNA fingerprint of a biological species 182. For some processed food products, much of the DNA has been degraded into sequences of less than 300-400 bp; therefore, studies for definitive identification of fish usually exclude highly processed food samples (e.g. canned or dried fish) due to a failed DNA amplification step 179. To circumvent this drawback of DNA barcoding, an alternative barcoding method, recognized as mini-barcoding, has been developed 183. By designing universal primers focusing on a shorter DNA sequence, fish identity in highly processed food products could also be determined 184. Therefore, to better understand the integrity of the seafood market on the west coast of Canada and given that the last survey conducted in Vancouver was nearly a decade ago, we conducted a more comprehensive survey study with a larger sample size focusing on Metro Vancouver. Less than three-hundred fish samples were collected from grocery stores, sushi bars and non-sushi restaurants located in multiple Metro Vancouver cities. To the best of our knowledge, this was the first seafood market 82 survey study focused specifically on Metro Vancouver by applying both DNA barcoding and DNA mini-barcoding methods. 4.2 Hypotheses H1: As a city by the Pacific Ocean, the mislabeling rate including the intentional fraud and unintentional misidentification should be lower compared with the situation in Eastern Canada in Ontario and Montreal; H2: The compliance of labeling regulations for fish products is improved compared to the small-scale study conducted ~10 years ago in Vancouver; H3: Improvement in the current governmental regulations and industry practices minimizes the chances for finfish mislabeling. 4.3 Objectives O1: To collect finfish samples in Metro Vancouver area and identify their biological species by using the standard DNA barcoding method; O2: To analyze the matching of the labeled species and the real biological species identified by the DNA barcoding method to determine the mislabeling rate; O3: To analyze the potential motivations and cause behind each mislabeled case and to investigate the intervention strategies for finfish mislabeling. 83 4.4 Material and Methods 4.4.1 Collection of fish samples Fish species that are of high regional significance or had high mislabeling rates in North America 178,179,185,186 were selected as target species for the current study. Grocery stores, sushi bars and non-sushi restaurants in Metro Vancouver, specifically Vancouver, Burnaby, Coquitlam, Surrey, Langley and North Vancouver, were selected randomly or based on the availability of the fish of interest. A total of 285 samples were acquired between September 2017 and February 2018. Samples purchased from sushi bars or non-sushi restaurants were ordered for take-out and the identity of fish species was doubly confirmed with staff in the restaurants. Samples were transferred to the lab immediately and stored at 4°C and sub-sampled within 24 hours. Sub-sampling was conducted by cutting a small piece of muscle tissue (c.a. 1 mm cube) from the center of the sample and storing this in 95% ethanol at -20°C until DNA extraction. Cross-contamination was prevented by treating tools with ELIMINase (Decon Laboratories, USA) between samples to eliminate DNA and DNase residues carried over from the previous sample. Tissue from each sample of at least 1 cm3 tissue was covered with plastic wrap, individually, and stored at -20°C as a backup. 4.4.2 Total DNA extraction Total DNA extraction was performed using Qiagen DNeasy Blood & Tissue kits (Qiagen Inc., Mississauga, ON, Canada) following the instructions of the manufacturer with minor modifications. Briefly, fish muscle tissue stored in 95% ethanol were mixed with 180 µL of ATL lysis buffer and 20 µL of proteinase K and incubated at 56°C overnight with constant shaking at 220 rpm. DNA extraction was then performed according to the instruction manual and was recovered with 100 µL of elution buffer. 84 4.4.3 Amplification of the DNA barcode regions FISH-BOL recommended M13-tailed fish-cocktail primers targeting at a 652-bp region on the mitochondrial COI gene 179 are listed in Table 1. The PCR reaction mixtures contained 1.25 µL of 10× Platinum Taq PCR Buffer, 0.625 µL of 50 mM MgSO4, 0.0625 µL of 10 mM dNTPs, 0.125 µL of each forward and reverse primers working solutions, 0.05 µL of Platinum Taq polymerase, 3 µL of fish DNA, and 7.26 µL of sterile water to achieve a final volume of 12.5 µL. Forward primer working solution contains 5 µM of VF2_t1 and 5 µM of Fish F2_t1, while reverse primer working solution contains 5 µM of FR1d_t1 and 5 µM Fish R2_t1. The PCR thermal cycling program consisted of an initial denaturation at 95°C for 4 min, 5 cycles of denaturation at 95°C for 30 s, annealing at 50°C for 30 s, and elongation at 72°C for 1 min, and 35 cycles of denaturation at 95°C for 30 s, annealing at 54°C for 30 s, and elongation at 72°C for 1 min. PCR was concluded by a final elongation at 72°C for 5 min and held at 4°C before being examined using 1% agarose gel electrophoresis stained with Red Safe DNA dye. Samples that could not be amplified using fish-cocktail primers were subjected to amplification using mammal-cocktail primers and mini-barcode primers (Table 4.1). Table 4.1 Primers for fish barcode and mini-barcode DNA regions 179,187 Name Primer sequence 5’®3’ Reference M13-tailed Fish-Cocktail DNA-barcode primers 171 VF2_t1 TGTAAAACGACGGCCAGTCAACCAACCACAAAGACATTGGCAC FishF2_t1 TGTAAAACGACGGCCAGTCGACTAATCATAAAGATAT85 Name Primer sequence 5’®3’ Reference CGGCAC FishR2_t1 CAGGAAACAGCTATGACACTTCAGGGTGACCGAAGAATCAGAA FR1d_t1 CAGGAAACAGCTATGACACCTCAGGGTGTCCGAARAAYCARAA M13-tailed Mammal-Cocktail DNA-barcode primers 171 LepF1_t1 TGTAAAACGACGGCCAGTATTCAACCAATCATAAAGATATTGG VF1_t1 TGTAAAACGACGGCCAGTTCTCAACCAACCACAAAGACATTGG VF1d_t1 TGTAAAACGACGGCCAGTTCTCAACCAACCACAARGAYATYGG VF1i_t1 TGTAAAACGACGGCCAGTTCTCAACCAACCAIAAIGAIATIGG LepR1_t1 CAGGAAACAGCTATGACTAAACTTCTGGATGTCCAAAAAATCA VR1d_t1 CAGGAAACAGCTATGACTAGACTTCTGGGTGGCCRAARAAYCA VR1_t1 CAGGAAACAGCTATGACTAGACTTCTGGGTGGCCAAAGAATCA VR1i_t1 CAGGAAACAGCTATGACTAGACTTCTGGGTGICCIAAIAAICA 86 Name Primer sequence 5’®3’ Reference Mini-M13 tailed DNA mini-barcode primers 179 UniMiniBar_F TGTAAAACGACGGCCAGTTCCACTAATCACAARGATATTGGTAC UniMiniVar_R CAGGAAACAGCTATGACGAAAATCATAATGAAGGCATGAGC M13 primers for sequencing the DNA-barcode amplicons 171 M13F TGTAAAACGACGGCCAGT M13R CAGGAAACAGCTATGAC 4.4.4 DNA sequencing analysis Amplicons from successful PCR were purified (QIAquick PCR purification kit, Qiagen Inc., Mississauga, ON, Canada) following the manufacturer’s instructions and then sent to UBC Sequencing and Bioinformatics Consortium (NAPS) for Sanger sequencing. The trace files acquired from Sanger sequencing were analyzed using CodonCode Aligner 7.1.2 (CodonCode Corporation, Dedham, MA) to obtain DNA sequences. DNA sequences with a length of >550 bp for DNA barcode and >100 bp for mini-barcode were searched on Barcode of Life Database (BOLD, http://www.boldsystems.org/index.php/IDS_IdentificationRequest). The species identified with a sequence similarity of >98% was considered as a match for a particular fish sample. Fish were identified by comparing the name labeled on the product in the market with the acceptable common names on the CFIA Fish List 151 corresponding to the scientific name retrieved after the analysis on BOLD. For fish species absent in the CFIA Fish list, the FDA Seafood List 188 was referred to. Samples 87 were considered to be mislabeled if the market names did not match the accepted common names for a specific fish. The motivations of the mislabeling practices were analyzed case-by-case. 4.4.5 Statistical analysis To analyze whether sampling source, product form, appearance, flesh color or if the fish had been raw or cooked could impact the mislabeling practice, a generalized linear regression model with binary distribution was constructed using MATLAB 2017b (Mathworks, Natick, Massachusetts, USA). Properties were recognized as being significant if a p<0.05 was acquired. 4.5 Results and Discussion 4.5.1 Overall results The number and composition of samples collected in Metro Vancouver area are summarized in Figure 4.1 and Appendix A. Among the 285 samples tested, 272 samples were successfully sequenced using Sanger sequencing technique for their DNA barcode, while 9 samples were analyzed for their DNA mini-barcode. There were four samples that failed both DNA barcoding and mini-barcoding methods. All the detailed information about the samples (specimen) and the sequences has been submitted to the BOLD system under a project entitled FISHV. Although various universal primers for DNA mini-barcodes have been designed to analyze highly processed foods, relatively unsatisfactory success rate with DNA amplification (~70%) has commonly been reported 189,190. Three possible reasons for the failure of mini-barcoding for those samples are 1) DNA in the sample has been denatured into even smaller fragments leaving an insufficient amount of DNA template, 2) PCR inhibitors (e.g. lipids) presented in the samples hindered the DNA amplification, and 3) Sanger sequencing technique is less suitable for directly sequencing of DNA fragments shorter than 250 bp 191. To analyze highly denatured 88 DNA in processed foods, quantitative PCR using species specific primers or probes targeting <100-bp DNA region might be more appropriate 192. Figure 4.1 Categorization of samples collected in Metro Vancouver based on geographical locations (A), source of the samples (B) and major species of samples (C). Snapper in (C) include samples labeled as real snappers (Lutjanus spp.) and Pacific snappers (Sebastes spp.) and other in (C) include fish species with less than three samples collected as per labeled on the package or menu (i.e. amberjack, bigeye, char, herring, monkfish, pollock, pompano, sardine, scad, toothfish, 89 basa, lingcod, seabream). DNA Sanger sequencing results with >550 bp nucleotides for DNA barcode region and >100 bp for DNA mini-barcode region were compared with the DNA database on BOLD. Although a definitive species level identification was not achieved for some of the samples, determination of the authenticity or mislabeling of samples was impacted slightly. The labeling accuracy of each fish sample was determined according to the CFIA fish list. All fish on the market should be labeled with a CFIA accepted common name instead of using vernaculars for the corresponding biological species determined by their DNA barcodes, and biological species that are not included in the CFIA fish list should not enter Canadian fish markets 193. Any fish for which the DNA sequence did not match the CFIA list were considered to be mislabeled. A total of 25% of the samples tested were identified as mislabeled and the detailed information for theses samples is summarized in Figure 4.2 and Table 4.2. This indicates that there has been no significant improvement in the integrity of fish labeling in the Vancouver retail and restaurant markets over the past decade and since the last study 179. For the mislabeled samples, 89% of them had unacceptable common names for a CFIA listed fish species; while 8 samples were identified as biological species that are not allowed for sale in Canada unless a further update of the CFIA fish list is released. Table 4.2 Details of mislabeled species for fish collected from sushi bars, restaurants, and grocery stores (NIFL represent a species not on the Canadian Food Inspection Agency Fish List). Common names not on CFIA list are from US Food and Drug Administration Fish List. Samples with ID containing only digits were purchased from sushi bars, while samples starting with R and G were purchased from restaurants and grocery stores, respectively. Sample ID City Labeled name DNA identified scientific name Common name on Canadian Food Inspection Agency list 12 Vancouver red snapper Oreochromis niloticus tilapia 90 Sample ID City Labeled name DNA identified scientific name Common name on Canadian Food Inspection Agency list 20 Vancouver red snapper Oreochromis niloticus tilapia 25 Vancouver bigeye tuna Thunnus thynnus or T. orientalis Atlantic or Pacific bluefin tuna 45 Vancouver Tai Oreochromis niloticus tilapia 49 Vancouver Tai Oreochromis mossambicus tilapia 56 Vancouver Tai Oreochromis niloticus tilapia 60 Vancouver Tai Oreochromis niloticus tilapia 67 Vancouver Tai Oreochromis niloticus tilapia 69 Vancouver white tuna Lepidocybium flavobrunneum snake mackerel/escolar 71 Vancouver Hamachi (yellowtail) Seriola dumerili greater amberjack 73 Vancouver Tai Oreochromis niloticus tilapia 74 Vancouver Tai Oreochromis niloticus tilapia 76 Vancouver Tai Oreochromis niloticus tilapia 78 Vancouver Tai Oreochromis niloticus tilapia 80 Vancouver Hamachi (yellowtail) Seriola quinqueradiata Japanese amberjack 83 Vancouver Tai Oreochromis niloticus tilapia 91 Vancouver snapper Oreochromis niloticus tilapia 95 Vancouver Yellowtail Seriola quinqeradiata Japanese amberjack 91 Sample ID City Labeled name DNA identified scientific name Common name on Canadian Food Inspection Agency list 96 Vancouver red snapper Oreochromis aureus blue tilapia 99 Vancouver snapper Oreochromis niloticus tilapia 102 Vancouver seabream Pagrus major silver seabream 104 Vancouver Yellowtail Seriola quinqeradiata Japanese amberjack 109 Vancouver red snapper Dicentrarchus labrax silver seabream 111 Vancouver Yellowtail Seriola quinqeradiata Japanese amberjack R6 Vancouver king salmon (white) Oncorhynchus keta chum salmon R11 Vancouver snapper Sebastes entomelas Pacific snapper/wido rockfish R17 Vancouver snapper Sebastes brevispinis Pacific snapper/silvergray rockfish R20 Surrey snapper Sebastes entomelas Pacific snapper/wido rockfish R21 Surrey pink salmon oncorhynchus nerka sockeye salmon R23 Vancouver steelhead salmon Oncorhynchus mykiss steelhead salmon/trout R25 Vancouver halibut Reinhardtius hippoglossoides greenland turbot R30 Vancouver red snapper Thunnus alalunga albacore tuna R35 North Vancouver red snapper Sebastes entomelas Pacific snapper/wido rockfish 92 Sample ID City Labeled name DNA identified scientific name Common name on Canadian Food Inspection Agency list R42 Vancouver Ahi tuna Thunnus atlanticus or T. maccoyii blackfin tuna or Southern bluefin tuna R44 Vancouver sea bass Dissostichus mawsoni Antarctic toothfish R45 Vancouver cod Pangasius hypophthalmus sutchi catfish R46 Burnaby cod Anoplopoma fimbria black cod/sablefish R47 Burnaby sole Pangasianodon hypophthalmus sutchi catfish R49 Vancouver snapper Pangasius hypophthalmus sutchi catfish R50 Burnaby cod Pangasius hypophthalmus sutchi catfish R52 Richmond halibut Reinhardtius hippoglossoides greenland turbot R53 Vancouver halibut Pangasianodon hypophthalmus sutchi catfish R75 Vancouver Atlantic halibut Hippoglossus stenolepis Pacific halibut R76 Vancouver sockeye salmon Oncorhynchus keta chum salmon R79 Vancouver Pacific halibut Melanogrammus aeglefinus haddock R80 Vancouver Pacific cod Gadus morhua Atlantic cod 93 Sample ID City Labeled name DNA identified scientific name Common name on Canadian Food Inspection Agency list R83 Vancouver snapper Sebastes mystinus or S. entomelas blue rockfish or widow rockfish R85 Vancouver cod Pangasianodon hypophthalmus NIFL (striped catfish) G24 Vancouver Pacific salmon oncorhynchus gorbuscha pink salmon G30 Coquitlam sockeye salmon Oncorhynchus gorbuscha pink salmon G41 Coquitlam sole Lepidopsetta polyxystra NIFL (Northern rock sole) G42 Vancouver sole Lepidopsetta polyxystra NIFL (Northern rock sole) G47 Richmond red snapper Lutjanus erythropterus or L. malabaricus NIFL (crimson snapper) or malabar snapper G58 Vancouver Snapper Sebastes entomelas Pacific snapper/wido rockfish G65 Vancouver Chilean seabass Dissostichus eleginoides Patagonian toothfish G67 Vancouver snapper Sebastes alutus redfish/rockfish/Pacific Ocean perch G71 Vancouver salmon Oncorhynchus nerka sockeye salmon 94 Sample ID City Labeled name DNA identified scientific name Common name on Canadian Food Inspection Agency list G74 Richmond basa Oreochromis niloticus tilapia G78 Vancouver snapper Sebastes alutus redfish/rockfish/Pacific Ocean perch G82 Vancouver snapper Sebastes entomelas Pacific snapper/wido rockfish G86 Vancouver yellowtail scad Atule mate NIFL (yellowtail scad) G87 Vancouver red big eye Priacanthus tayenus NIFL (purple spotted bigeye) G90 Richmond menhaden Clupea pallasii Pacific herring G91 Richmond wild salmon Salmo salar Atlantic salmon G93 Vancouver golden pompano Trachinotus ovatus NIFL (pompano) G119 Vancouver Chilean seabass Dissostichus eleginoides Patagonian toothfish G120 Vancouver Pacific snapper Sebastes alutus Pacific Ocean perch G122 Vancouver Atlantic halibut Hippoglossus stenolepis Pacific halibut G124 Vancouver snapper Sebastes zacentrus sharpchin rockfish G129 Vancouver snapper Sebastes brevispinis Pacific snapper/silvergray rockfish 95 Figure 4.2 Number of mislabeled products analyzed based on source of samples (A) and the major species of samples (B). Snapper in (B) include samples labeled as real snappers (Lutjanus spp.) and Pacific snapper (Sebastes spp.) and other in (B) include fish species with less than three samples collected as per labeled on the package or menu (i.e. amberjack, bigeye, char, herring, monkfish, pollock, pompano, sardine, scad, toothfish, basa, lingcod, seabream). To investigate if other properties could impact mislabeling, samples were categorized based on five parameters, namely, raw/cooked, source of samples (i.e. sushi bar, grocery store and non-sushi restaurant), appearance (e.g. plain fillet, seasoned, and covered with breading or other ingredients), product form (e.g. whole fish, fillet, chunk, and chopped), and color of the flesh [e.g. red and light color (pink/white)]. A generalized linear regression model with binomial distribution, a logistic analysis, was constructed to analyze the significance of these five factors on mislabeling practices and the results are summarized in 96 Table 4.3. Providing a significance level of α = 0.05, the product form and color of the flesh had a statistically significant impact on the mislabeling issues, while no significant difference in mislabeling rate was observed for samples collected from different sources. Products composed of fish muscle of a lighter color flesh and chopped muscle tissue were more prone to illicit practices compared to red or fillet samples, and this aligns with our common sense that fish with light color flesh is less distinctive than fish with colorful flesh, such as sockeye salmon, although we did observe mislabeling of salmon products in this study. Fish products with extensive removal of morphological attributes were more prone to species substitution. However, whole fish also had a higher mislabeling rate compared with fish fillets, which could be attributed to a bias in our sampling method – a much smaller number of whole fish samples with targeting species were collected compared to the other forms. In addition, the impact of raw/cooked and the appearance attributes of fish products was not statistically significant. Again, this could be explained by the bias in our sampling method. Raw fish samples and breaded samples were collected mainly from sushi bars and grocery stores with a focus on particular species that have been historically associated with mislabeling/fraud incidences, and this would make the significance of these two factors in the analysis less reliable. Nevertheless, the level of species substitution and motivations for mislabeling was different depending upon the sources from which the samples were purchased. 97 Table 4.3 Results of generalized linear regression model with binary distribution for the analysis of properties related to fishes having significant impact on the mislabeling/fraud practices (B = C. CE) F-stat DF1 DF2 p-Value intercept 4.69 1 271 0.021 source 0.52 2 271 0.59 raw/cooked 1.64 1 271 0.22 appearance 3.08 2 271 0.061 product form 3.45 3 271 0.020 color of the flesh 20.70 1 291 2.62 × 10-5 4.5.2 Sushi bars A total of 107 sushi samples purchased from 39 sushi bars were successfully sequenced. The mislabeling rate of 22% was the lowest compared with that of non-sushi restaurants and grocery stores. The mislabeling rate in sushi bars was much lower in Metro Vancouver compared to previous studies conducted in Eastern Canada, US and Europe 112,179,194. Located on the coast of northern Pacific Ocean, Metro Vancouver has an easy access to abundant resources of high quality sushi fish, and this could contribute to the high degree of fish authenticity in sushi bars in this community. However, the level of integrity of sushi market in Vancouver is still not satisfactory. Although substitution of Pacific wild salmon by Atlantic farmed salmon was not observed in Metro Vancouver, fraudulent snapper and red snapper marketing practices were prevalent (i.e. 100%), which conforms with the high mislabeling rate in the previous studies 112,179. All but one of the snapper and red snapper sushi tested in this study were identified as tilapia, a much cheaper fish species compared with snappers, indicating intentional substitution for economic gain. One of the red snapper samples was identified as silver seabream. The intention behind this mislabeling case could be the confusion 98 between snapper (Tai) and genuine snapper (Madai) in sushi culture. Tai and Madai are known as snapper/red snapper and genuine snapper (silver seabream) in sushi culture, respectively, although both Tai and Madai are not acceptable common names in Canada for those fish species 151. Besides, two tuna, one seabream and five yellowtail samples were mislabeled. Yellowtail is a common vernacular for Seriola spp, but it is not the approved market name in either Canada or the US. The case of bigeye tuna substituted by Bluefin tuna might indicate a by-catch or IUU fishing issue; while labeling escolar as white tuna is an evident food fraud incident with potential health detriment to susceptible consumers 195 since escolar contains high amounts of indigestible wax esters called gemplylotoxin, a strong purgative that can lead to diarrhea, cramps, nausea or headache 196. Mislabeling of seabream was also suspected to be an unintentional practice since there is a high possibility that the owner of the sushi bar failed to recognize the necessity of the term “silver” and the importance of following the CFIA fish list strictly. According to CFIA, yellowtail is the name for a flounder species (i.e. Limanda ferruginea), while the biological species of the Yellowtail samples were Japanese amberjack (Seriola quinqueradiata) and greater amberjack (S. dumerili). Mislabeling involving the misuse of the name amberjack and Yellowtail has been reported by Willette and others 166 and is primarily because of the discrepancies in cultures between Japan and Canada and the common use of the term Yellowtail and Hamachi for Seriola spp.. Yellowtail indeed refers to the Seriola quinqueradiata and S. dumerili in Japan, while amberjack is the only acceptable common name for these species in Canada. 4.5.3 Non-sushi restaurants A total of 82 samples purchased from 47 non-sushi restaurants were successfully sequenced with a success rate of 98%. A mislabeling rate of 28% was identified, ranking this as the highest among the three sources. Again, all the snappers were mislabeled with inappropriate names according to CFIA. Compared with sushi bars, the species of substitution for snappers in non-sushi restaurants were more 99 diverse. Replacement of red snapper by albacore tuna and rockfish (Pacific snappers) as well as snapper by basa was a common fraud. Labeling Pacific snapper as snapper is tied to an ambiguous fish naming system across the Pacific Northwest. Twenty-one species of fish have an accepted market/common name as snapper according to the CFIA fish list, while another eighteen species of fish have a common name including the word “snapper”. However, according to CFIA those eighteen species should not be sold under the “umbrella” term “snapper”. More discussion regarding this is provided in the Section 4.5.5. Mislabeling incidents for salmon, halibut, seabass, cod and sole were also detected. In contrast to the previous studies 179, no Atlantic salmon was sold as Pacific salmon in the restaurant samples that we tested. Substitutions between different Pacific salmons were identified: chum salmon labeled as king and sockeye salmon, as well as sockeye salmon sold as pink salmon. While it is uncertain if the substitution of pink salmon by sockeye salmon indicates an intentional behavior, the substitution of chum salmon for king and sockeye salmon is a highly possible fraud since the price of king and sockeye salmon are around 1.5-2 times that of chum salmon 197. A common problem observed in many markets is the naming of steelhead trout. Although Oncorhynchus mykiss is an anadramous or ocean-going fish that behaves similarly to salmon, it does not have an accepted common name ‘salmon’ but instead the common name of steelhead salmon or trout. Similar incidents also happened with the misuse of the name cod: black cod (sablefish) being sold as cod in one of our sample. To interpret the motivation of this type of mislabeling practices is challenging since black cod is a very valuable fish species and apparently different from true cod. Possibly the market did not understand the value of adding the term ‘black’ to their marketing and label designation 198. A more understandable incident of mislabeling involved the substitution of cod (3 samples) with the cheaper catfish (i.e. basa), an obvious intentional fraud, and one catfish species (i.e. striped catfish) has not been included in the CFIA fish 100 list. Another mislabeled cod involves the misidentification of the geographical origin of the fish, which could be an intentional mislabeling practice or an unintentional misidentification due to human error of the staff in the restaurant. Misidentification of geographical origin of halibut was also uncovered in one of our halibut samples (i.e. Atlantic halibut labeled as Pacific halibut). Although this type of mislabeling might not damage consumer economic benefit, it could involve selling more vulnerable species to consumers without notification, hindering consumers from making informed decision for wildlife conservation. Besides, Greenland turbot (i.e. Reinhardtius hippoglossoides) was sold as halibut in two of our samples. Although Greenland halibut is a vernacular for Reinhardtius hippoglossoides in many countries including Canada, it is not an acceptable market name in Canada, and thus those products should be labeled as turbot. Comparably, Chilean seabass is the vernacular name for Antarctic toothfish in many countries 188, but is not an accepted name in Canada. The last two mislabeled halibut samples were substituted with sutchi catfish and haddock, an intentional fraud involving the substitution of a lower value and more readily available species for a high value species. Sutchi catfish, a universally available aquaculture species with a light-color tissue and pleasant taste, were identified as substitutes for a wide variety of high-end fish in our study, including halibut, snapper, sole and cod. The only incident of tuna mislabeling, although the specific species was unresolved using DNA barcoding, might indicate another intentionally fraudulent practice or IUU fishing. 4.5.4 Grocery stores A total of 92 samples purchased from 26 grocery stores were sequenced and a success rate of 99% was achieved. A mislabeling rate of 24% was uncovered for fish in grocery stores, ranking the second among the three sampling sources. Coincidently, snapper and red snapper had the highest mislabeling rate (i.e. 73%). The apparent slightly improved labeling situation of snappers in grocery store could be attributed to the acknowledged lack of specificity of DNA barcoding towards the Sebastes genus. Two 101 of the snapper samples were identified as Sebastes mystinus or S. entomerlas with the same veracity. Although S. mystinus is not a species being included in CFIA Fish List, samples were correctly labeled if they are S. entomerlas. Therefore, these two samples were identified as properly labelled samples in this current study. For the mislabeled snapper samples, although the species substitution in grocery stores are all in the same genus as Pacific snappers (Sebastes spp.) and real snappers (Lutjanus spp.) and they all possess a name with the term “snapper”, these fish should be labelled appropriately according to the CFIA fish list using their specific names instead of being labeled with an “umbrella” term snapper. Atlantic salmon sold as wild salmon and pink salmon labeled as sockeye salmon might represent food fraud incidents, while the other two mislabeled salmon products are due to the noncompliance with the “Labelling of Pacific Salmon in Canada” requirement 199. The specific species of Pacific salmon should be clearly listed on the label. The misuse of the term Chilean seabass for Patagonian toothfish were also identified in two grocery store samples. Mislabeling of tilapia as basa and Pacific herring as menhaden was detected. However, the motivations behind these were difficult to understand. Although menhaden is not a name included in CFIA fish list, this common name is used for Ethmidium maculatum in the US, a species of fish belonging to the same family as herring (i.e. Clupeidae). In addition, Ethmidium maculatum does not possess an acceptable English name or French name in the CFIA fish list, posing a gap in labeling standards. Therefore, this mislabeling incident might be an unintentional behavior. Similar to the case of restaurant samples, misidentification of geographical origin of one halibut sample was also detected in the grocery sample, although this time a less concerned species (Pacific halibut) was sold as Atlantic halibut which was endangered according to International Union for Conservation of Nature (IUCN) Red List 200. 102 Beside substituting a product with another fish included in the CFIA fish list, there are also several samples collected from grocery stores that were identified as species outside the list, including Trachinotus ovatus, Priacanthus tayenus, Lepidopsetta polyxystra, Atule mate, and Sebastes serranoides. Although Priacanthus spp., Trachinotus spp. and Sebastes spp. are in the CFIA fish list, general common names of bigeye, pompano and rockfish are assigned. According to the FDA fish list, only Priacanthus macracanthus can be labeled as red bigeye while no fish in CFIA has a common name of golden pompano. The genus Atule and species Lepidopsetta polyxystra are not on the CFIA fish list, while according to the FDA fish list these samples were labeled correctly. Therefore, these mislabeling incidents demonstrate some gaps in the CFIA fish list and in harmonization between countries. 4.5.5 Ambiguity of labeling regulation Apart from several obvious economically motivated mislabeled products (e.g. tilapia and albacore tuna for red snapper and snapper, chum salmon for king salmon, and basa for cod, sole and halibut), other mislabeling incidents tend to be associated with complicated fish naming systems and non-harmonized fish labeling requirements among countries. Snappers are one of the fish groups having a notorious naming system. Strictly speaking, snappers refer to 112 species in 17 genera of the Lutjanidae family 160, primarily harvested in tropical and sub-tropical areas. Nevertheless, “snapper” has been used as the vernacular name, and thus to some extent accepted by local regulatory agencies, for many species outside the family of Lutjanidae. For example, snappers commonly refer to seabream in the Sparidae family in New Zealand 160 and include rockfish species in Sebastes and Sebastolobus genus of Scorpaenidae family in Canada 151. Among the mislabeled snapper and red snapper samples tested in this current study, 15 out of 23 samples were found as rockfish in the 103 Sebastes genus. In Canadian fish markets, there are 14 rockfish species with a common name containing “snapper” in Scorpaenidae family. Only one of them can be labeled as snapper or red snapper (Sebastes ruberrimus), while the others are accepted as Pacific snapper. Therefore, it is challenging to determine whether such mislabeling is intentional or merely because of a confusing use of a common name. Noting that real red snapper (Lutjanus campechanus) and some snappers in the Lutjanidae family are depleted due to overfishing, the status of some rockfish (Pacific snapper) species is also not healthy. Although the Ocean Wise program managed by the Vancouver Aquarium has updated their assessment on ground fish stocks and recommended four species of rockfish (i.e. yellowtail, yellowmouth, silvergray, and canary rockfish) caught by mid-water trawl as sustainable sources 201, only one of the substitutions identified in our study belong to those four species. Consequently, labeling rockfish as snappers could not only cause overestimation of real snapper stock status and impact consumers’ perception on this group of fish, but also open the door for replacing fish with another species that has conservation concerns. To avoid such type of mislabeling, requiring greater detail on the type of fish, including the specific scientific name on the label or menu could be effective. Mislabeling induced by discrepancies in the vernacular among countries is also ubiquitous. Labeling amberjack as yellowtail in sushi restaurants was identified in our study and has been reported previously 166. In the context of sushi, Yellowtail (i.e. Hamachi) refers to amberjack. However, in Canada, yellowtail refers to yellowtail flounder. To best maintain the authenticity of the sushi culture but to not mislead consumers about the identity of a fish, updating the CFIA fish list to include Yellowtail as an accepted common name for amberjack or require the addition of specific scientific name is recommended. Inconsistency in common names also resulted in the mislabeling of Patagonian and Antarctic toothfish. 104 For the five samples that were species not included in the CFIA fish list, three of them were correctly labeled according to the FDA fish list, while two were labeled correctly with the generic name. The US, as the major importing and exporting country of Canadian seafood, has 1880 species included in their fish list 188, while the Canadian fish list contains only 919 individual Taxonomic Serial Numbers 151. Although some species are covered by the genus name (e.g. Priacanthus spp.), there are genus that are accepted by the US but not by Canada (e.g. Atule), potentially hindering smooth and transparent trading between these two countries. Moreover, the accepted common names for many fish species are not uniform between countries, rendering problems in assigning the correct labels to be approved by the customs in each country 160. This scenario is even worse when trading between countries with different official languages. To tackle such problems, harmonizing the accepted common names between trading countries and requiring the use of scientific names on label would provide a solution. Labeling products with scientific name not only aids in ensuring the precision of labeling, but also contributes to the conservation of vulnerable species. Fourteen species and 15 species of fish in the Scombridae family can be collectively labeled as tuna in Canada and the US, respectively. However, they differ substantially in their stock status, vulnerability to overfishing, market values, and nutritional values. According to the IUCN Red List 200, Southern bluefin tuna (Thunnus maccoyii) is critically endangered, followed by Atlantic bluefin tuna being endangered and bigeye tuna and Pacific bluefin tuna being vulnerable, while albacore and yellowfin tuna are near threatened. Lumping all the tuna species under the single “umbrella” term of tuna could create loopholes for IUU fishing of species with unhealthy stock, pose challenges in stock status evaluation, and diminish the effectiveness of fishery management. Consumer preference for tuna species varies remarkably and thus impacts the market price for the different tuna species, with bluefin tunas being the most favored species and the most 105 expensive ones. Furthermore, various tuna species differ in their level of heavy metal content and there are consumption advisories for some tunas for susceptible populations (e.g. pregnant women and children). According to FDA, fresh/frozen bigeye tuna have the highest mercury level, followed by fresh/frozen albacore and yellowfin tuna and canned albacore tuna, while fresh/frozen and canned skipjack tuna had the lowest mercury level 202. Another study conducted by Lowenstein and others 185 concluded that bluefin tuna had a similar or even higher level of mercury compared with bigeye tuna and suggested the consumption of bluefin tuna should also be avoided by certain vulnerable populations 185. Consequently, using a collective name for the different tuna species could impede people from making informed decisions based on their dietary restrictions and conservation risks of fish. Similarly, in the case of halibut, use of the scientific name or regional classification for Pacific halibut and Atlantic halibut would be helpful. Requiring the unique scientific name could make the protection of Atlantic halibut more effective. Another factor associated with the little improvement in the fish market integrity in Vancouver and Canada is lack of adequate traceability. According to CFIA, labeling the country of origin for imported products where the last transformation took place is compulsory 199. However, providing the geographic location where the fish was harvested or produced is voluntary. Having complex and fragmented fish supply and production chains that consist of transit and processing in several different countries makes regulatory compliance with the labeling requirements in the country to which the fish is destined, difficult. Labeling a product without sufficient information on the processing and/or trading histories inevitably increases challenges in tracing and tracking of fish for either IUU purposes or for species identification. For example, for a fish caught in Canada, shipped to China for gutting and filleting, breaded in the US and sold in Canada should be labeled as a product of the US. However, every single node in the supply chain offers opportunities for mislabeling and fraudulent practices. To 106 ensure the integrity of products which have traveled around the world, detailed information on processing and trading histories is mandatory. Labeling fish products with information of their geographical origins is of great significance, because harvested or produced at different locations, the same fish species could have significant difference in nutritional values, level of contaminations, and/or conservation risk levels 203. In addition, labeling the product with catching methods provides another dimension for understanding and tracing the fish back to the source and thus can help eliminate IUU fishing products from appearing in fish market in Canada, although this does not mean that the IUU fish would not be present in some other markets. 4.6 Conclusions The identity of 281 fish products purchased from sushi bars, non-sushi restaurants and grocery stores in Metro Vancouver area was successfully analyzed using DNA barcoding and DNA mini-barcoding methods. An average mislabeling rate of 25% was observed for products collected from the three sources, which was comparable to the mislabeling level in 2010. Mislabeling rate in sushi bars, non-sushi restaurants and grocery stores were 22%, 28%, and 24%, respectively. Snappers were the fish group with the highest mislabeling rate. Based on the type of substitution found, both intentional fraud, unintentional misidentification and/or unintentional misuse of vernacular names were observed. Irrespective of motivations, mislabeling of fish products could jeopardize consumer’s health, damage the economic interest of consumers as well as the entire fishing industry, and weaken the effectiveness of fish management and conservation measures. To improve the integrity of the fish market in Canada, several actions are recommended. First, the CFIA fish list should be harmonized with major trading countries to facilitate trade and avoid mislabeling. Secondly, mandatory labeling with scientific name can reduce the opportunity of mislabeling associated with historical use of confusing and ambiguous vernacular names and mistranslation. Using vague generic names to cover multiple species with 107 notable differences in fishing pressure and stock health, nutrition and contamination levels, and market values should be avoided. Again, the regulation should require the use of scientific names across the value chain. Last but not the least, including the information of where the fish was farmed or caught, the processing history and the fishing/farming methods used enables the effective boat-to-table tracing and tracking of fish products and reduces the potential for fraud. 108 Chapter 5: Development of a Raman spectroscopic method for the authentication of finfish fillets in the market 5.1 Introduction As discussed in the section 4.1, fish mislabeling is a rampant global issue. Although having the longest coastline and one of the largest ocean bodies, Canada is not immune to this issue. The most recent survey study in Metro Vancouver described in the previous chapter identified fish mislabeling cases caused by various motivations, including the intentional fraud to sell cheap fish as superior species and to sneak illegal, unregulated and unreported harvests into the seafood market, as well as unintentional mislabeling due to misidentification, gaps in culture and vernaculars among countries or regions, and ambiguous government regulations. Regardless of the motivations behind the mislabeling activities, mislabeled seafood products can cause harm in several aspects from economics, public health to environmental sustainability. Amendment in the regulation and enhancement of seafood traceability can help prevent seafood fraud to a certain extent, but reliable analytical tools for seafood authentication are also needed. One of the most widely applied fish authentication techniques over the past decade is DNA barcoding 204. Using a set of universal polymerase chain reaction (PCR) primers, a ~650 bp mitochondrial COI DNA sequence can be amplified. After Sanger sequencing, the DNA sequence of the sample will be compared with a DNA barcode database [e.g. Barcode of Life Database (BOLD: http://www.boldsystems.org/index.php)] to reveal the biological identity of the sample. Authentication of the sample can then be achieved by comparing the label with the identified species based on the barcode DNA sequence. DNA barcoding is a powerful tool, which provides high accuracy, sensitivity and specificity. However, the turnaround time from sample to result can be several days even by using 109 a fully automatic procedure, which could delay the response to public concerns regarding the questionable products. Detection methods targeting at the chemical composition of fish, such as liquid chromatography-mass spectrometry and immunological based methods, have also been well studied 205-208. However, these analyses generally involve time-consuming and labor-intensive sample preparation and/or require highly trained personnel to control the complicated instruments. Thus, a more user friendly and rapid analytical method is necessary for finfish authentication. As introduced in section 1.3.2, Raman spectroscopy fingerprints complex food matrices in a non/less-destructive, rapid, accurate and user-friendly manner. It has been confirmed to effectively differentiate a great variety of closely related food products, including beef and horse meat 82, honey and coffee of different botanical and/or geographical origins 29,209, vegetable oils of various botanical sources and processing methods 120,210, genuine and adulterated coconut water 211, as well as butter and margarine 26. However, only limited studies have been published focusing on the applications of Raman spectroscopy in the seafood sector and the majority of which was related to quality analysis, including to quantify the functional components (e.g. carotenoid) 212 and to differentiate fresh fish fillets from frozen-thawed ones 213. The one study conducted by Rašković and others concluded that Raman spectroscopy was feasible to differentiate 12 species of fish fillets 214. Although the results are promising, several defects in the experimental design have to be acknowledged. Firstly, by collecting only one fish sample of each species from the market, the sample size was too small to be representative. Secondly, given the high prevalence of fish mislabeling, without authenticating the fish species using a standard method, the species labeled in the package might be misleading and could lead to erroneous conclusion. Lastly, the spectral dataset was only examined using the hierarchical clustering analysis without a cross-validation or prediction procedure to eliminate the concerns for 110 model overfitting. An overfitted model has little value in the real-world application to analyze the new unknown samples. Therefore, the objective of this study was to comprehensively verify the feasibility of using Raman spectroscopy and chemometrics for fish species identification. Specifically, multiple biological replicates were available for each fish species and their exact biological identities were analyzed by using the golden-standard DNA barcoding method. Besides, various unsupervised and supervised chemometric models were constructed to differentiate the fish species. The stability and robustness of the models were investigated by using a cross-validation approach and their discrimination powers were compared. To compare the performance of different Raman spectrometers, both a bench-top confocal micro-Raman system and a wave-guided portable Raman spectrometer were utilized to collect Raman spectra of fish samples. To the best of our knowledge, this is the first research to thoroughly and logically examine the feasibility of Raman spectroscopy for finfish species identification. 5.2 Hypotheses H: Both the bench-top confocal micro-Raman spectrometer and the portable Raman spectrometer combined with chemometrics can be used for species identification of raw finfish samples. 5.3 Objectives O1: To establish a Raman spectral database for fish by using a confocal micro-Raman spectrometer and a portable Raman spectrometer; O2: To construct classification chemometric and machine learning models that can identify fish species based on the Raman spectral features. 111 5.4 Material and Methods 5.4.1 Collection of fish samples Fish samples studied in this chapter were selected from the samples collected for the market survey study recorded in the previous chapter (i.e. section 4.4.1). Samples were selected based on three criteria: 1) raw samples, 2) sufficient amount of samples left for Raman spectral collection (i.e. >1 cm2 surface area), and 3) at least three biological replicates. A total of 98 finfish fillets belonging to eleven biological species (Table 5.1) were selected and subjected to Raman spectral collection and used to construct Vancouver fish Raman spectral library. The exact biological species of all fish samples involved in this Raman spectroscopic study had been identified using DNA barcoding and the real biological species were adopted for the construction of Raman spectral library, instead of using the labeled species on the products package/restaurant menu. Table 5.1 Biological species composition of fish samples used for the construction of the fish Raman spectral library species # of biological replicates Atlantic salmon 4 pink salmon 4 sockeye salmon 22 spring salmon 2 albacore tuna 19 cod 4 halibut 4 mackerel 15 Snappers* 8 sole 6 112 species # of biological replicates tilapia 10 *Snappers refer to the fish in the real snapper genus Lutjanus and the Pacific snapper genus Sebastes 5.4.2 Raman spectral collection An EZRaman-I Series portable Raman system (Enwave Optronics, Inc., Irvine, CA, USA) and a bench-top confocal micro-Raman system were applied to collect Raman spectra of fish samples separately. A piece of at least 1 cm square sample was cut and deposited onto a glass microscope slide for Raman spectral collection. The portable Raman spectrometer system is equipped with a 785-nm diode laser with a maximum laser power of 282 mW. An optical fiber probe is used to guide the laser wave onto the samples. The best focus of laser on samples is controlled by the addition of a contact lens tube, which maintains a ~7 mm distance between the laser and samples that are in contact with the lens tube. Raman scattering signals were generated by illuminating fish samples using a 282-mW laser power over a 50-s integration time. The Raman scattering signals over the wavenumber range of 2189-100 cm-1 were recorded with a signal resolution of 1 cm-1. At least three Raman spectra for each piece of fish sample were collected. The bench-top confocal micro-Raman spectroscopic system built in our lab is equipped with a 785-nm line laser (300 mW incident laser power). The spectrometer (Renishaw, Gloucestershire, UK) has an entrance aperture of 50 μm and a focal length of 300 mm and is matched with a 1200 line mm-1 grating. A 578- by 385-pixel charge coupled device (CCD) array detector with a pixel size of 22 μm was used to record Raman scattering signals. A Leica microscope (Leica Biosystems, Wetzlar, Germany) with a 113 50× Nikon objective (NA = 0.75, WD = 0.37) was used to focus the incident laser onto the sample for Raman scattering spectral collection. At least three Raman spectra for each individual sample were collected at room temperature over the wavenumber region of 3200 to 200 cm-1 (3666 data points and 0.82 cm-1 signal resolution) with an exposure time of 10 s. 5.4.3 Preprocessing of Raman spectral data The Raman spectra collected by the portable Raman spectrometer were auto-baseline corrected and 3-point boxcar smoothed using the built-in program in the Raman spectroscopic system, while the Raman spectra recorded by bench-top Raman spectrometer were baseline corrected and smoothed using a 6-order polynomial fitting and a 7-point boxcar smoothing by Vancouver Raman Algorithm 215. All spectra were normalized to the intensity of the peak at 1000 cm-1, which is attributed to the presence of L-phenylalanine 216. 5.4.4 Chemometric analysis To explore the spectral features, the unsupervised principal component analysis (PCA) as a data mining tool was used. Meanwhile, supervised classification tools including discriminative analysis (DA) based on PCA and partial least squares (PLS), soft independent modelling of class analogy (SIMCA), k-nearest neighbor (KNN), support vector machine (SVM), and classification and regression tree (CART) algorithms were all tested and compared for their discrimination power in identifying fish species based on Raman spectral information. The detailed principle and procedures for chemometric analysis can be referred to Section 2.4.6. To examine the robustness and avoid overfitting of the classification models, all the classifiers were cross-validated using a leave 20% out Monte Carlo cross-validation (CV) with 100 iterations approach to test the robustness of the classifiers and to identify potential overfitting. The 114 best classification models were determined by comparing their classification accuracy, error rate, sensitivity, specificity and precision of the calibration models and the cross-validation models 217. 5.5 Results and Discussion 5.5.1 Explorative analysis of Raman spectra Raman spectra for 98 samples of raw fish fillets belonging to 11 biological species/groups were collected using both the benchtop confocal micro-Raman spectrometer and the portable wave-guided Raman spectrometer. The average of normalized Raman spectra of different species of fish collected by both spectrometers demonstrated some noticeable differences in their chemical compositions (Figure 5.1). The Raman spectra of the same fish species collected by using the two different Raman spectrometers are mostly similar, suggesting a high inter-instrument conformance. 115 Figure 5.1 Average of normalized Raman spectra of different species of fish collected using the bench-top confocal micro-Raman spectrometer (A) and the portable Raman spectrometer (B). N=5 The major differences in the Raman spectral information among different species of fish are predominately associated with the variations in the composition of proteins and lipids (Table 5.2). The 116 two distinct peaks in the Raman spectra of four types of salmon fish at the wavenumbers of 1156 cm-1 and 1517 cm-1 can be attributed to the accumulation of carotenoids, which provides salmon with the unique reddish/orangish color of the flesh. Table 5.2 Assignments of major peaks in Raman spectra 108,214 Raman shift (cm-1) Peak assignment 495 glycogen 523 s-s disulfide stretching in proteins 608 cholesterol 644 C-C twisting mode of tyrosine 717 choline group 760 symmetric breathing of tryptophan 828 proline, hydroxyproline, tyrosine, v2 PO2- stretch of nucleic acids 852.5 ring breathing mode of tyrosine and C-C stretch of proline ring, Glycogen 874.2 C-C stretching, hypro (collagen assignment) 896.8 phosphodiester, deoxyribose 933 Proline, hydroxyproline, n(C-C) skeletal of collagen backbone 973 ρ(CH3), δ(CCH) olefinic (protein assignment) 1003 ring breathing vibration of phenylalanine 1046 glycogen or proline 1079 C-C or C-O stretching mode of phospholipids, C-C or PO2 stretch (nucleic acid), tryptophan 1125 n(C-C) skeletal of acyl backbone in protein and lipid 117 Raman shift (cm-1) Peak assignment 1156 C=C stretch mode of carotenoids 1208 n(C-C6H5), tryptophan, phenylalanine (protein assignment) 1271 Typical phospholipids, Amide III band in proteins, C=C groups in unsaturated fatty acids 1300 Fatty acid 1316 Guanine (B, Z-marker) 1338 Tryptophan, CH2/CH3 wagging, twisting and/or bending mode of collagens and lipids 1397 CH2 deformation 1447 CH2 deformation (protein vibration), a marker for protein concentration 1517 b-carotene accumulation (C-C stretch mode) 1553 tryptophan ring vibration 1655 amide I group of protein, C=C of lipids in normal tissues 1745 phospholipids, n(C=O) 2329(minor) Region of the OH-NH-CH stretching vibrations 2573(minor) Region of the OH-NH-CH stretching vibrations 2729 Region of the OH-NH-CH stretching vibrations 2868 CH3 and CH2 stretching vibrations 2935 C-H stretching 3015 n=CH of lipids 3064 ν=CH of lipids 118 However, fish species differentiation is difficult by only using the visual examination of the spectral information because of the complex patterns of each spectra. In addition, comparing the averages of spectra can over-simplify this classification problem because the variations between the biological and technical replicates of the same biological species can be overlooked. The standard deviation of Raman spectra for each type of fish are demonstrated in Figure 5.2. Mackerel Raman spectra exhibited the largest within group variation among other fish species in the datasets collected by both Raman spectrometers, while the other fish species demonstrated within group variations to certain extent. In general, Raman spectra collected by using benchtop Raman spectrometer exhibited slightly higher within group variation compared with that of portable Raman spectrometer. To further investigate the Raman spectral features, PCA is commonly applied in chemometrics as a data mining tool. Regardless of the type of Raman spectrometer, overlapping of the clusters of different fish species was observed (Figure 5.3). Indicated by the loadings of the first two PCs (Figure 5.4), the major variations among spectra are associated with the peaks at 1655 cm-1, 1557 cm-1, 1447/1439 cm-1, 1338 cm-1, 1271 cm-1 and 1156 cm-1 contributed by lipid, amide I, unsaturated fatty acids, and carotenoids, separately. Except for carotenoids, the other peaks associated with lipids and proteins present in all fish species and their concentrations had weak correlation with the species of fish. Theoretically, peaks for carotenoids should contribute to the separation of the four salmon species from the other 7 species on the PCA score plots. However, no clear boundary between these two groups was defined in the plots. The majority of the variances in the spectral datasets were contributed by the variations between the replicates within each species of fish (i.e. intra-species). Such apparent intra-species variations disturbed the clustering of fish species based on Raman spectral features using the unsupervised PCA, and thus more advanced and rigorous supervised classification algorithms were applied to study the feasibility of Raman spectroscopy for fish species specification. 119 Figure 5.2 Average of normalized Raman spectra of 11 species of fish collected by using benchtop Raman spectrometer (A) and portable Raman spectrometer (B) with the band representing the standard deviation. 120 Figure 5.3 Principal component analysis (PCA) score plots for spectra collected by using benchtop Raman spectrometer (A) and portable Raman spectrometer (B). PCA was conducted on the wavenumber region of 1800-400 cm-1 for the spectra collected by both Raman spectrometers. 121 Figure 5.4 Principal component analysis loading plot for spectra collected by using benchtop Raman spectrometer (A) and portable Raman spectrometer (B). PCA was conducted using the wavenumber region of 1800-400 cm-1 for spectra collected by both Raman spectrometers. 122 5.5.2 Classification analysis In multivariate statistical analysis and machine learning, one common approach to improve the performance of a classifier for solving a multiclass classification problem is to utilize a hierarchical classification principle 218. For example, for a two-hierarchy classification analysis, all the different classes (e.g. nine classes) are grouped into several clusters (e.g. three clusters each contains three classes) at first and the first-hierarchy classification model is constructed to differentiate the clusters of classes (i.e. a three-class classification question). Then, the second-hierarchy classification models will be developed for the classification of classes in every clusters, accordingly (i.e. three classifiers each solves a three-class classification question). By grouping the classes into clusters, the number of classes to be resolved by each classifier is smaller compared to that of the direct multiclass classifier, and thus, more accurate and reliable classifiers can be developed. Therefore, in this current study, a hierarchical classification approach is employed to solve the 11-fish species (i.e. classes) question. 5.5.2.1 Salmonidae vs non-Salmonidae classification The most prominent difference between these 11 species of fish is the color of the fillets. Because of the presence of carotenoids, the four types of salmons (i.e. Salmonidae) have fillets of orangish to reddish in color, while the rest of the seven species (i.e. non-Salmonidae) possess fillets of light pink to white color. Although these two groups of fish can be differentiated visually, classification models were still constructed to verify that the differences in the chemical composition can be captured by Raman spectrometers. 123 To obtain the best classifier, a variety of classification algorithms were investigated (i.e. PCA-DA, PLS-DA, CART, SVM, SIMCA, and KNN) and their performance were compared. Among the different types of classifiers, PLS-DA with 13 latent variables using the non-scaled spectral dataset provided >98% accuracy, sensitivity and specificity in both the calibration model and the cross-validation model constructed for benchtop Raman spectral data (Table 5.3). In the meanwhile, a 100% accuracy, sensitivity and specificity was achieved in the SVM models using 22 support vectors and cost value of 1. The little difference in the performance between the calibration models and the cross-validation models eliminated the concerns for model overfitting and confirmed the robustness of the two calibration models. These results demonstrated an outstanding discrimination power to differentiate the non-Salmonidae from Salmonidae by using Raman spectra collected by both Raman spectrometers.124 Table 5.3 The optimized models for the 2-class classification based on Raman spectral data collected using the benchtop Raman spectrometer and the portable Raman spectrometer PLS-DA model with 13 latent variables for benchtop Raman spectrometer calibration: error rate 0, accuracy 100% Leave 20% out CV: error rate 1%, accuracy 99% class # of spectra specificity sensitivity precision specificity sensitivity precision Salmonidae 92 100% 100% 100% 99% 99% 98% non-Salmonidae 206 100% 100% 100% 99% 99% 100% SVM model with 22 support vectors for portable Raman spectrometer calibration: error rate 0, accuracy 100% Leave 20% out CV: error rate 0, accuracy 100% class # of spectra specificity sensitivity precision specificity sensitivity precision Salmonidae 99 100% 100% 100% 100% 100% 100% non-Salmonidae 198 100% 100% 100% 100% 100% 100% 125 Table 5.4 The optimized models for the 4-class Salmonidae classification based on Raman spectral data collected using the benchtop Raman spectrometer and the portable Raman spectrometer PCA-DA model with 14 principal components for benchtop Raman spectrometer calibration: error rate 6%, accuracy 96% Leave 20% out CV: error rate 18%, accuracy 88% class # of spectra specificity sensitivity precision specificity sensitivity precision Atlantic salmon 14 99% 86% 92% 95% 78% 73% Pink salmon 11 99% 91% 91% 97% 70% 76% Sockeye salmon 60 97% 98% 98% 98% 94% 99% Spring salmon 7 97% 100% 88% 96% 87% 60% PLS-DA model with 14 latent variables for portable Raman spectrometer calibration: error rate 0, accuracy 99% Leave 20% out CV: error rate 6%, accuracy 88% class # of spectra specificity sensitivity precision specificity sensitivity precision Atlantic salmon 12 100% 100% 100% 99% 100% 92% Pink salmon 11 100% 100% 100% 99% 91% 91% Sockeye salmon 71 100% 99% 100% 100% 85% 100% Spring salmon 5 99% 100% 83% 89% 100% 33% 126 5.5.2.2 Four-class Salmonidae classification Classifiers were constructed and compared for the identification of the four Salmonidae fish and the optimized results are summarized in Table 5.4. The PCA-DA models constructed using 14 PCs for the benchtop Raman spectral dataset produced similar overall accuracies to the PLS-DA models constructed using 14 latent variables for the portable Raman spectral dataset. However, the overall error rates of the models for the benchtop Raman spectral dataset were greater compared to that for the portable Raman spectral dataset. These apparent inconsistencies were caused by the definition of the parameters evaluated for these classifiers and the heterogeneity of the numbers of spectra in each fish species. As explained in the Section 2.4.6, the specificity and sensitivity reflect the type II and type I error of each individual class, respectively. The precision is the interplay of type I and type II errors of each class; the error rate is related to the overall average of the sensitivity of each class; and the accuracy is the percentage of the overall accurately classified samples, regardless of the classes. For a multi-class classification problem with highly heterogeneous classes, the error rate provides a much comprehensive evaluation of the discrimination power of the classifier because the accuracy was calculated without taking the number of replicates in each class into consideration. For example, for a two-class classification problem with heterogeneous samples (e.g. A class with four samples and B class with 36 samples), the misidentification of samples belong to A class could cause more distinct decrease in the sensitivity and precision of A class, while the sensitivity of B class is more immune to the misidentification of its samples. Given a 90% accuracy, if the four misidentified samples all belong to B class, the sensitivity of A and B are still >89% and the error rate is 94%. However, if all the misidentified samples belong to A class, the sensitivity of A will be 0 and the error rate is 50%. The objective of this study was to 127 provide the best classifiers for the identification of different fish species and thus classifiers with extremely low sensitivity for any fish species should be avoided. Thus, classifiers with lower error rates are more appropriate in this application. Therefore, the classifier constructed based on portable Raman spectral dataset exhibited an overall better performance in fish species identification compared to the ones constructed using the benchtop Raman spectral dataset. In addition, the distinct increase in the error rate in the CV model constructed for benchtop Raman spectrometer compared to its calibration model indicated an overfitting of the calibration model. The reason for benchtop Raman spectrometer to exhibit less classification power could mainly be attributed to the confocal feature of the Raman spectroscopic system applied in this study. As explained in the Section 2.5.4, this bench-top micro-Raman spectroscopic system generated a laser spot too small to collect representative Raman spectra from heterogeneous samples. Although in this fish study, laser spot was only focused on the lean muscle areas of the sample and an “average of three spectra” approach was used to generate more representative spectra data, this benchtop Raman spectrometer system was still less suitable compared to the portable Raman spectrometer with regard to the classification of less homogeneous food samples. Therefore, further studies should be conducted to increase the number of spectral accumulation and the area for spectral mapping for heterogeneous food samples if a confocal micro-Raman spectroscopic system is the only Raman spectrometer available at hand. 128 5.5.2.3 Seven-class non-Salmonidae classification The performance of the optimized classifiers for the 7-class non-Salmonidae group are summarized in Table 5.5. PCA-DA models constructed using 19 principal components and KNN models developed using Euclidean distance with Kvalue=1 provided the best classification performance for benchtop and portable Raman spectral datasets, respectively. Similar to the results in Section 1.5.2.2, the accuracies of the calibration models constructed using spectral datasets of both spectrometers were comparable, while the error rate of the cross-validation model for the benchtop Raman spectral dataset was higher than that of its corresponding calibration model and the models for the portable Raman spectral dataset. The higher error rate of the cross-validation model of the benchtop Raman spectrometer indicated a model overfitting and thus spectra collected by portable Raman spectrometer exhibited a better overall performance for fish species differentiation. According to the latest seafood market survey in Vancouver, snappers (i.e. true snapper in the Lutjanus genus and Pacific snapper in the Sebastes genus) are the most susceptible fish toward intentional fraud in sushi venues and their most common replacement is tilapia. Therefore, to construct classifiers with sufficient discrimination power towards these two groups is of particular interest. The discrimination power of the classifiers towards these two groups can be further examined using the confusion matrices (i.e. a matrix comparing the real identity of each sample with their calculated identity using the classifier 217) of the classification models (Table 5.6). In both calibration models, no sample belongs to the tilapia group was identified as snappers, while only one snapper sample in each of the model was misclassified as tilapia. This result confirms the high potential of using Raman spectroscopy to reveal the substitution of tilapia for snappers in sushi stores. For most of the fish species/group, these two classifiers 129 provided adequate discrimination power, as indicated by >80% precision values in the majority of classes. However, discrimination power of these classifiers needs to be further improved to provide a more reliable species identification tool for the seafood sector. Future studies to improve the reliability of Raman spectroscopic-based method for fish species identification should look to: 1) to increase and balance the number of biological replicates and number of Raman spectra in each species; 2) to include more commercially available fish species collected from Metro Vancouver area and even across Canada; and 3) to develop more robust and accurate classifiers by using advanced machine learning approaches. 130 Table 5.5 The optimized models for the 7-class non-Salmonidae classification of Raman spectral data collected using the benchtop Raman spectrometer and the portable Raman spectrometer PCA-DA model with 19 principal components for benchtop Raman spectrometer calibration: error rate 12%, accuracy 96% 5-fold CV: error rate 24%, accuracy 78% class # of spectra specificity sensitivity precision specificity sensitivity precision Albacore tuna 47 100% 100% 100% 99% 100% 98% Cod 18 99% 72% 93% 99% 69% 86% Halibut 15 98% 73% 79% 97% 78% 68% Mackerel 47 99% 100% 98% 99% 97% 98% Snappers* 23 91% 74% 52% 92% 54% 46% Sole 23 98% 57% 81% 96% 36% 50% Tilapia 33 99% 97% 94% 97% 95% 88% KNN model with K value of 1 and Euclidean distance for portable Raman spectrometer calibration: error rate 13%, accuracy 91% 5-fold CV: error rate 13%, accuracy 91% class # of spectra specificity sensitivity precision specificity sensitivity precision Albacore tuna 57 100% 96% 100% 100% 96% 100% 131 KNN model with K value of 1 and Euclidean distance for portable Raman spectrometer calibration: error rate 13%, accuracy 91% 5-fold CV: error rate 13%, accuracy 91% class # of spectra specificity sensitivity precision specificity sensitivity precision Cod 13 98% 92% 75% 97% 92% 71% Halibut 11 98% 82% 75% 98% 91% 77% Mackerel 47 99% 100% 98% 99% 100% 98% Snappers* 20 98% 80% 84% 100% 100% 100% Sole 17 98% 59% 77% 99% 59% 83% Tilapia 33 98% 97% 91% 98% 94% 91% *Snappers include the real snappers in the genus of Lutjanus and the Pacific snappers in the genus of Sebastes 132 Table 5.6 Calibration model confusion matrices of the 7-class non-Salmonidae classifiers PCA-DA model with 19 principal components for benchtop Raman spectrometer Predicted species Real species Albacore tuna Cod Halibut Mackerel Snappers* Sole Tilapia Albacore tuna 47 0 0 0 0 0 0 Cod 0 14 1 0 3 0 0 Halibut 0 0 14 0 1 0 0 Mackerel 1 0 0 47 0 0 0 Snappers* 0 1 1 0 16 4 1 Sole 0 0 1 0 8 13 1 Tilapia 0 0 0 0 0 0 33 KNN model with K value of 1 and Euclidean distance for portable Raman spectrometer Predicted species Real species Albacore tuna Cod Halibut Mackerel Snappers* Sole Tilapia Albacore tuna 55 0 0 1 0 0 1 Cod 0 13 0 0 0 0 0 Halibut 0 1 8 0 0 2 0 Mackerel 1 0 0 46 0 0 0 Snappers* 0 1 1 0 17 0 1 Sole 0 2 1 0 2 11 1 Tilapia 0 0 1 0 0 0 32 *Snappers include the real snappers in the genus of Lutjanus and the Pacific snappers in the genus of Sebastes 133 5.6 Conclusion A benchtop confocal micro-Raman system and a portable wave-guided Raman spectrometer were both studied regarding their feasibility for the identification of 11 species/group of finfish. Supervised chemometric classification models were constructed based on a hierarchical classification principle to solve this 11-class classification problem. The first-level classifiers constructed using datasets collected by both Raman spectrometers provided a close to 100% accuracy in dividing the samples into groups of Salmonidae and non-Salmonidae fish. The second-level classifiers developed using the portable Raman spectral dataset exhibited a better discrimination power (i.e. <6% and <13% error rate for the Salmonidae and non-Salmonidae fish identification, respectively) compared with classifiers built with the benchtop Raman spectral dataset (i.e. <18% and <24% error rate for the Salmonidae and non-Salmonidae fish identification, respectively). Nevertheless, both Raman spectrometers demonstrated promising potentials to be applied for a rapid identification of fish species, especially for the differentiation of the most commonly adulterated fish (i.e. Snappers) from its common adulterant (i.e. Tilapia). After increasing and balancing the number of biological replicates and number of Raman spectra in the fish spectral datasets, classifiers with improved classification power and robustness can be developed. With these classifiers, Raman spectroscopy has a high potential to be adopted by the inspection agencies and the food industry to rapidly and accurately identify the real biological species of fish fillets and thus protect seafood products from mislabeling and ensure the authenticity and integrity of the seafood markets. 134 Chapter 6: Rapid authentication of pomegranate juice using an instrumental free loop-mediated isothermal amplification assay 6.1 Introduction As one of the super fruit juices, pomegranate juice has received a global reputation because of its high antioxidant capacity and potential health benefits 219. Along with the increase in the consumption of 100% pure pomegranate juice 220 and the generally high cost of pomegranate juice manufacture, incentives exist to develop adulterated products by adding other less expensive fruit juices, such as apple, grape, and pear juices 221. In addition, bitterness derived from the high content of tannins in pomegranates can be reduced by substitution, resulting in a higher acceptance of pomegranate juice especially among new consumers. To protect consumer’s rights and ensure the implementation of food labeling laws, accurate and efficient determination techniques are highly required to disclose the adulteration of 100% pure pomegranate juice added with fruit juices of lower producing costs. Efforts are ongoing to develop novel techniques for the authentication of pomegranate juice. International Multidimensional Authenticity Specifications algorithm has been developed based on various chemical properties (e.g. six anthocyanins, punicalagins, sugar concentration, and compositions of several sugars and organic acids) to authenticate commercially available pomegranate juice 222. Fourier-transformed infrared spectroscopy coupled with chemometric models was applied to detect and quantify pomegranate juice mixed with grape juice 223. In addition, polyphenolic contents and amino acids composition determined by liquid chromatographic based methods and electrokinetic chromatography-laser induced fluorescent 135 spectroscopy, respectively, have been used as the chemical indicators for pomegranate juice authentication 224,225. As a plant-based agri-food product, chemical composition of fruit juice can be easily affected by other factors such as plant age, cultivars, geographical origins, cultivation methods, and processing procedures, leading to a large within-species variation. Besides, food grade additives can be applied to compensate the differences in chemical compositions between authentic and fraudulent juice products. Therefore, the accuracy of the authentication methods targeting at the chemical compositions could be easily compromised for the analysis of plant-based agri-food products. In contrast, DNA sequence of different fruit species has much smaller within-species variations and applying DNA to disguise the fake foods is unlikely to occur due to the high cost of DNA extraction. Thus, authentication techniques based on DNA sequence are more reliable for the differentiation of fruit juice produced from a different species of fruit. Fruit juice authentication methods based on polymerase chain reaction (PCR) have been studied by a few research groups and species-specific PCR primers have been designed to identify different fruit species 226,227. However, PCR-based methods take >5 h to complete from DNA extraction to PCR amplicon visualization, which are time-consuming and labor-intensive, and they involve sophisticated instruments and highly trained personnel. These traits make PCR-based authentication of fruit juices less applicable or affordable for inspection agencies and food industries where frequent routine analysis is demanded. As an advanced DNA amplification method, loop-mediated isothermal amplification (LAMP) eliminates the requirement of sophisticated instruments for thermocycling and provides improved sensitive and time-efficiency compared with the traditional PCR and qPCR 228,229. Besides, using a unique DNA amplification mechanism, the four or six primers used in LAMP also increase the overall specificity of the 136 reaction 51. Moreover, with the aid of colorimetric and/or fluorometric dyes, the results of LAMP can be visualized directly without conducting the electrophoresis gel assay, which could further reduce the time, labor and cost of the analysis. Therefore, LAMP could be an ideal alternative for PCR-based methods for rapid authentication of fruit juice products. To further simplify the overall authentication procedures for fruit juice based on LAMP reaction, microfluidic lab-on-a-chip (LOC) platforms could be adopted to integrate the DNA extraction, LAMP reaction, and LAMP result readouts onto a single device. As introduced in the Section 1.5, polymer-based materials are commonly used for LOC devices, providing the superior performance in fluid control, reagent storage and material durability. However, polymer-based LOC cannot achieve an autonomous flow without the assistance of external pressures such as from a pump, a syringe, or microvalves 64. Paper-based materials are another widely accepted option for LOC devices because of their ease of fabrication and cost-effectiveness. More importantly, the large surface/volume ratio of porous paper-based materials is ideal for chemical immobilization and the internal capillary force can transport reagents to the designated zones on chip without requiring external forces. In addition, paper-based materials, including Flinder Technology Associates (FTA) card 230 and Whatman Fusion 5 231, and polyethersulfone (PES)232 filter paper, have also demonstrated outstanding capabilities in extracting DNA from the complex sample matrices with significantly reduced complexity. Therefore, paper-based materials were investigated in the current study to provide a microfluidic platform for the LAMP-based assay for pomegranate juice authentication. Nevertheless, paper-based microfluidic devices are generally more fragile and unsuitable for the reactions in the liquid phase. Therefore, polymer-based materials were also involved to provide physical supports and enable the in-liquid reactions, if demanded. The hybrid polymer/paper microfluidic LOC device is a current research 137 hotspot for point-of-care applications 76,233,234. However, no study has been published to date to develop this type of device for food authentication. 6.2 Hypotheses H1: Species-specific LAMP primers can be designed for pomegranate, apple and grape; H2: Paper-based materials can be applied to extract DNA from fruit juice; H3: Simple visual analysis of LAMP reaction can be achieved; H4: Paper-based microfluidic chip can achieve an instrumental-free sample-to-answer authentication of pure pomegranate juice. 6.3 Objectives O1: To design LAMP primers with high specificity and sensitivity for pomegranate, apple and grape, respectively; O2: To develop a simple protocol for fruit juice DNA extraction by using paper-based materials; O3: To identify the optimized fluorometric or colorimetric method for LAMP reaction analysis; O4: To design a paper-based microfluidic chip for simple sample-to-answer authentication of pomegranate juice in an instrumental-free manner. 138 6.4 Materials and method 6.4.1 Materials Cetyltrimethylammonium bromide (CTAB), NaCl, Ethylenediaminetetraacetic acid (EDTA), and phenol/chloroform/isoamyl alcohol (25:24:1, v/v) were acquired from Sigma-Aldrich (Oakville, Ontario, Canada). Chloroform (ACS grade), octanol, polyvinylpyrrolidone, and guanidine hydrochloride were purchased from Fisher Scientific (Toronto, ON, Canada). Tris-HCl pH 8.0 solution was acquired from Thermo Fisher Scientific (Mississauga, ON, Canada). 6.4.2 Preparation of fruit juice samples Pomegranate, apple and grape whole fruits were purchased from several local grocery stores in Vancouver, BC, Canada. Pure fruit juices were prepared by using a commercially available fruit juicer (Cuisinart® MP150 Fruit and Vegetable Press). The outer surface of the fruits was washed with dish soap and dried with a kitchen towel. Prior to juicing, pomegranates arils (i.e. seeds surrounded by a juice sac) were manually separated from the other parts of the fruits and apples were cut into small pieces, while grapes were juiced directly after washing. Every time before and between the preparation of a different fruit juice sample, the mesh and the inside of the juicer were washed carefully by using dish soaps, rinsed with ethanol and dried with kitchen towel to avoid potential cross-contaminations. All fruit juices were then aliquoted into 50 mL falcon tubes and stored at -20°C until further analysis. Adulterated pomegranate juice samples were prepared by adding the grape juice and/or apple juice into pomegranate juice at the ratio of 1:1 v/v or 1:1:1 v/v. 139 6.4.3 DNA extraction from fruit juice by using the CTAB method The traditional CTAB method for plant DNA extraction 235 was applied for fruit juice DNA extraction with modifications. Briefly, the pellet containing some pulps, DNA and other macromolecules was collected from 3 mL of fruit juice after centrifugation at 14,000 ×g for 5 min at room temperature (i.e. ~18°C). After the addition of 1 mL of CTAB lysis buffer (20 g L-1 CTAB, 1.4 M NaCl, 0.1M Tris-HCl, and 20 mM EDTA, pH 8), the mixture was incubated at 65°C for 30 min with gentle inversion every 5-10 min. The mixture was then cooled down to room temperature (~23°C) on ice, followed by an addition of 1 mL of 24:1 v/v chloroform/octanol. After centrifugation at 14,000 ×g for 10 min, the supernatant was transferred into a new centrifuge tube without disturbing the impurities in the middle and bottom layers. One volume of CTAB precipitation buffer (10 g L-1 CTAB, 50 mM Tris-HCl, and 10 mM EDTA, pH 8) was then added into the supernatant, followed by centrifugation at 14,000 ×g for 10 min at room temperature to collect DNA in the pellet. Proteins and other impurities were further removed by redissolving the pellet in 500 µL of the 1.2 M NaCl and 500 µL of 25:24:1 v/v/v phenol/chloroform/isoamyl alcohol for a liquid/liquid extraction. After centrifugation at 14,000 ×g for 10 min, the supernatant containing DNA was collected. Isopropanol was added at 0.6 volume of the supernatant and incubated at -20°C for 30 min to precipitate DNA. DNA pellet was finally washed with 500 µL of 75% ethanol, redissolved in 60 µL of sterile water, and stored at 4°C for further analysis. 6.4.4 Fruit juice DNA extraction using commercial kits DNA of fruit juices was extracted following the protocol of Qiagen DNeasy plant Mini kit (Qiagen, Mississauga, Canada) with minor modifications. Briefly, 500 µL of fruit juices with 140 some pulps were mixed thoroughly with 600 µL of Buffer AP1. After incubating at 65°C for 10 min with twice gentle inversion, lysates were cooled down to room temperature (~ 23°C). Then, 180 µL of Buffer P3 was mixed with lysates, followed by incubation on ice for 5 min. The following procedures were conducted as described in the manufacturer’s handbook, except that the DNA was eluted by using 60 µL instead of 100 µL of sterile water. The eluents were stored at 4°C and used within three months. 6.4.5 PCR assay for fruit juices of three species of fruit To confirm the successful extraction of fruit juice DNA, PCR with primers targeting at the sequence characterized amplification regions (SCAR) of the three species of fruits 43 was performed. The sequences of SCAR marker primers are listed in Table 6.1. The primers were purchased from Thermo Fisher Scientific (Pleasanton, CA). PCR was performed by mixing 12.5 µL of 2×Taq FroggaMix (Froggabio, Toronto, Canada), 0.25 µL of 100 mM forward and reverse primers, 2 µL of fruit juice DNA, and 10 µL of sterile water to make a final volume of 25 µL. The PCR amplification program included an initial denaturation and activation step at 95°C for 4 min; and 35 cycles of denaturation at 95°C for 30 s, annealing at 60°C or 57°C for 30 s, and elongation at 72°C for 30 s. A final elongation was performed at 72°C for 5 min, followed by storing the amplicons at 4°C before being examined by using electrophoresis with 1.5% agarose gels stained with the RedSafe DNA dye (iNtRON Biotechnology, Kirkland, WA). The gel was imaged by using the Bio-Rad ChemiDoc MP Imaging System (Mississauga, Ontario, Canada). Froggabio 100 bp DNA ladder (Froggabio, Toronto, Canada) was used to identify the size of the amplicons and confirm the success of amplification and DNA extraction. 141 Table 6.1 List of PCR primers for species-specific SCAR markers Fruit juice species SCAR primer sequence (5’®3’) Sizes (bp) Annealing temperature (°C) Punica granatum (pomegranate) F ACATTATGCCAAGGGCTCCC 231 60 R GGTGTAAACTCGCGTATCGC Malus domestica (apple) F TCAAGGACAGAGACTCGAGG 262 57 R GTGATCTGGAAGATGGAAAGG Vitis vinifera (grape) F GATGAGAATATGCCTTGAGGC 144 60 R GCAAATAAGAGAGGGAGATGG 6.4.6 Design of LAMP primers LAMP primers for each species of fruits were designed by using the online software PrimerExplorer V5 (http://primerexplorer.jp/lampv5e/index.html) targeting at different gene regions including Ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL), Maturase K (matK), SCAR, and internal transcribed spacers (ITS). All the three species of fruits have some sub-species or cultivars that are being used for juice making. Therefore, to ensure that the primers can amplify any sub-species or cultivars of pomegranate, apple and grape, all the rbcL, matK, SCAR markers and ITS nucleic acid sequences available on the NCBI nucleotide database (https://www.ncbi.nlm.nih.gov/nucleotide/) were retrieved and aligned by using MEGA software 236 to acquire consensus sequences. Default program was used to design primers for these consensus sequences unless no set of primers could be generated. The specificity of each primer was confirmed in silico by using the Nucleotide BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi) or the Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) against the nucleic acid sequences of the 142 other two fruit species. At least two sets of primers for each gene region were designed for each species of fruit. 6.4.7 LAMP assay for fruit juice authentication LAMP reactions were conducted in a 20-µL system containing 2 µL of the 10× Isothermal Amplification Buffer from NEB (New England Biolabs, Beverly, MA), 1.2 µL of 100 mM MgSO4, 2.8 µL of 10 mM dNTPs, 2 µL of 10× primer mix, 1 µL of Bst 2.0 polymerase, 2 µL of fruit juice DNA or sterile water as the negative control, and 9 µL of sterile water. To optimize the composition of the primer mixture, the 10× primer mix was prepared by mixing 16 mM of FIP and BIP with 1 mM, 2 mM or 4 mM of F3 and B3 to achieve a 16:1, 8:1, or 4:1 ratio between the inner and outer primers. For the set of primers containing loop primers, loop primers were included in the primer mixture at a concentration of 8 mM. The LAMP reaction temperature was also optimized by performing the reaction at temperatures ranging from 62 to 66°C. The LAMP products were examined by using the electrophoresis method with 1.5% agarose gel stained with RedSafe DNA dye. The specificity of each set of primers was examined in vitro by performing LAMP reactions against the DNA extracted from the other two species of fruits. LAMP primers that only amplified the DNA extracted from the designated fruit species were selected and used for further analysis. The sensitivity of the selected primers was tested by using the 10-fold serially diluted DNA extracted from the designated species of the fruits. 143 6.4.8 DNA extraction from fruit juice by using paper-based materials Two types of paper were investigated in this study for their efficacy in DNA extraction from fruit juices. FTA PlantSaver Card (Millipore Sigma, St. Louis, MO, USA) was used following the manufacturer’s instruction with modifications. Briefly, 20 µL of fruit juices or pellets from 20-200 µL of fruit juices were loaded onto the 3-mm FTA cards excised by a Harris micro-punch (Whatman, Florham Park, NJ). To examine the impact of pH on the FTA cards, neutralized fruit juices or pellets were also prepared by titrating with 0.1 M NaOH solution to pH, 7 followed by being loaded onto FTA cards. After drying and incubating at room temperature (i.e. 20°C) for 1-3 h, the FTA cards were washed twice by soaking in 200 µL of the FTA purification reagent (10 mM NaCl, 10 mM Tris-HCl, 1 mM EDTA, 0.5% v/v Triton-X-100, pH 7.5) for 5 min. Then, the cards were further washed twice by soaking in 200 µL of Tris-EDTA buffer (10 mM Tris-HCl, 1mM EDTA, pH 8.0) for 5 min. Finally, the FTA cards were used directly for PCR reactions following the protocol as indicated in section 6.4.5 with modifications. Specifically, the FTA cards were added into the reactions instead of fruit juice DNA or water and the volume was made up with sterile water to a final volume of 25 µL. For Fusion 5 filter paper, a vertical filtration device (Figure 6.1) similar to the one developed by Jangam and others 237 was utilized to extract DNA from fruit juices. Instead of relying on their filtration properties, Fusion 5 filter paper were used as a silica-based material to capture DNA in complex sample matrices. Briefly, 50 µL of fruit juices were mixed with 50 µL of lysis buffer (8 M guanidine hydrochloride, 1% polyvinylpyrrolidone, 50 mM Tris-HCl, 20 mM EDTA, and 21.3 mM Triton-X-100, pH 6.4) in 1.5 mL Eppendorf tubes and incubated at room temperature for 2 min with a constant gentle inversion. The lysed juices were then loaded onto the filtration device. The filtration device consists of several layers of absorption pads to provide sufficient 144 liquid absorbent capacity, one piece of 8-mm Fusion 5, and two pieces of parafilm each containing a 6-mm hole on it. The Fusion 5 punch was sandwiched between the two pieces of parafilm and the two holes and the filter paper were well aligned. This sandwiched structure was then pressed onto the pile of absorption pads. Samples that were loaded onto the device flowed through the filter paper rapidly and were absorbed by the absorption pads underneath the filter paper. Then, the Fusion 5 was washed with 50 µL of washing buffer I (8 M guanidine hydrochloride, 50 mM Tris-HCl, pH 6.4) for once and 50 µL of washing buffer II (10 mM Tris-HCl, 100 mM NaCl, pH 8) for twice. To elute the DNA bond on the Fusion 5 paper, the device was disassembled and the piece of Fusion 5 was deposited into a 1.5 mL Eppendorf tube, followed by incubation with 60 µL of sterile water at room temperature for 1 min. DNA extracted using Fusion 5 filter papers were examined by PCR following the procedures as described in the section 6.4.5. Figure 6.1 Assembly of vertical filtration device for DNA extraction using Fusion 5 filter paper. Once confirmed the success of DNA extraction by using PCR, DNA extracted by using FTA cards and/or Fusion 5 would be tested by LAMP reaction following the protocol as indicated in the section 6.4.7 using the corresponding primers. 145 6.4.9 Simple visual analysis of the LAMP results To simplify the analysis for LAMP results, visual monitoring and detection methods based on colorimetric and fluorometric dyes were investigated. The most commonly used colorimetric dye HNB 61 and fluorometric dye calcein 62 were tested in this study. Specifically, 120 µM HNB was included in the LAMP reaction system for the colorimetric method, while 50 µM calcein and 1 mM manganese ions (Mn2+) were added in LAMP reactions for the fluorometric analysis. Both dyes change color upon the production of pyrophosphate during the LAMP reaction. The results were recorded by using the camera on an iPhone 6 under direct sun light for HNB or under a 365-nm UV lamp for calcein. 6.4.10 Design for a sample-to-answer microfluidic device The device mainly contains three functions, specifically DNA extraction, LAMP amplification, and LAMP results visualization. The component for DNA extraction was predominantly composed of Whatman No. 1 filter paper (Thermo Fisher Scientific, Mississauga, ON, Canada). The microfluidic pattern was designed by using AutoCAD (Autodesk Inc., San Rafael, CA) with a size of 2 cm × 2 cm for each layer. The pattern was then wax printed onto the filter paper (Figure 6.2A) by using a commercial Xerox Phaser 8560N wax printer (Xerox Canada, Toronto, ON). After incubating the filter paper in an oven at 120°C for 50 s, the wax completely melt and blocked the pores inside and underneath the wax-printed areas of the paper due to gravity 238, allowing the formation of delicately designed hydrophobic and hydrophilic areas on the paper. Therefore, liquid can wick along the designated hydrophilic areas of the paper. The PDMS material used for the LAMP reaction zone was manufactured by mixing the curing agent and siloxane at 1:10 w/w ratio 239. The PDMS layer was of a size of 2 cm × 2 cm × 0.5 cm; the six 3-mm holes on the PDMS were produced by using a Harris micro-punch; and the bottom was 146 sealed with a thin layer of PDMS (2 mm) (Figure 6.2B). To allow the DNA to be released into the LAMP reaction wells, each well contained a 3-mm filter paper to absorb the DNA from the previous layer of the device. Figure 6.2 Design of the hybrid paper/polymer microfluidic device for the authentication of pomegranate juice. (A) Layers 1-6 were made with Whatman No. 1 filter paper, except that the blue region on layer 4 was a piece of Fusion 5 paper with a diameter of 5 mm for DNA extraction. The paper was blocked by wax printing to define the hydrophobic areas (shown in black) and to guide the liquid wicking. Layer 7 was made with PDMS for LAMP reaction and the reaction wells (shown as circles) are of a diameter of 3 mm. The green areas denote the path of DNA eluted from layer 4 to layer 7, while the white areas demonstrated the path of water from the loading layer (layer 1) to the negative control LAMP reaction wells (white circles on layer 7). The arrows indicated the directions of the folding of each layer during the analysis. (B) Schematic illustration of the portable heating pad powered by a portable charger and equipped with a temperature controller. (C) Layer-by-layer view of the hybrid polymer-paper microfluidic device. Specifically, the device was first used unfolded for loading the fruit juice lysis onto the Fusion 5 on the layer 4 with an absorption pad attached to the bottom (Figure 6.2A). After washing the Fusion 5, layer 1-3 were folded onto the top of layer 4, while layer 5 and 6 were folded beneath 147 layer 4 according to the directions shown by the arrows. Then, layer 6 was aligned with layer 7, followed by loading sterile water onto the loading zone on layer 1 for DNA elution and to provide samples for negative control in LAMP reactions. Finally, LAMP reagents were added into the reaction wells. The top of each reaction well was sealed with fluorescence-free tapes. The LAMP reactions were initiated by incubating the chip at 63°C for ~1 h by using the portable heating device (Figure 6.2B). A layer-by-layer view of the device is illustrated in Figure 6.2C. 6.5 Results and Discussion 6.5.1 Extraction of fruit juice DNA by using conventional methods To confirm the presence of less fragmented DNA (i.e. DNA fragments >200 bp in length) in fruit juice, both the conventional CTAB method and commercial Qiagen Plant DNeasy kit were applied to extract DNA from fruit juices with minor modifications. Universal PCR primers targeted at the plant 18S rDNA (Table 6.2) were used to confirm the success of DNA extraction. Compared to the Qiagen kit, the modified CTAB method was less compatible with the downstream PCR amplification (Figure 6.3), which might be due to the less-effective removal of sugar and polyphenol interferents from the fruit juice matrices. Considering the straightforward protocol and reduced amount of hazardous chemicals used, Qiagen Plant DNeasy kit outperformed the CTAB method and was thus used for further DNA extraction. Table 6.2 Universal primers for plant 18S rDNA Primer primer sequence (5’®3’) annealing temperature (°C) Reference 18S-F GCCCGTTGCTCTGATGAT 58 240 148 18S-R GGATGTGGTAGCCGTTTCT Figure 6.3 Electrophoresis agarose gel analysis of 18S primers PCR products using DNA extracted by CTAB method (A) and Qiagen kit (B). Lanes in panel A: 1, DNA extracted from grape juice; 2, DNA extracted from apple juice; 3, DNA extracted from pomegranate juice; 4, water as a negative control; 5, 100-bp DNA ladder. Lanes in panel B: 1-2, DNA extracted from grape juice; 3-4, DNA extracted from apple juice; 5-6, DNA extracted from pomegranate juice; 7, 100-bp DNA ladder. To confirm that the fruit juices prepared in the laboratory environment were free from contamination and/or cross-contamination, species-specific SCAR PCR primers (Table 6.1) were used to amplify the DNA extracted by using the Qiagen kit. The electrophoresis gel results demonstrated that the SCAR primers exclusively amplified the DNA being extracted from the corresponding fruit juice (Figure 6.4) and thus the juices were prepared with high purity. (A) (B) 1 2 3 4 5 1 2 3 4 5 6 7 149 Figure 6.4 Electrophoresis agarose gel analysis of SCAR primers PCR products using DNA extracted from different fruit juices by using the Qiagen kit. Lanes 1-4 were amplified by using the grape SCAR primers: 1, grape DNA; 2, apple DNA; 3, pomegranate DNA; 4, water as a negative control (NC). Lanes 6-9 were amplified by using the apple SCAR primers: 6, grape DNA; 7, apple DNA; 8, pomegranate DNA; 9, water as an NC. Lanes 11-14 were amplified by using the pomegranate SCAR primers: 11, grape DNA; 12, apple DNA; 13, pomegranate DNA; 14, water as an NC. Lanes 5 and 10: 100-bp DNA ladders. Although the juice prepared in the lab was not pasteurized or sterilized, pasteurization (e.g. boiling for 10 min) and even autoclaving (e.g. 121°C for 15 min) have been confirmed to only destroy the plant DNA to a certain extent and DNA with > 295 bp in length were successfully amplified by using PCR methods 241,242. Some of the commercial fruit juices have also been subjected to a filtration procedure before bottling to remove the fruit debris and some undesirable macro-biomolecules. However, filter paper with pore size < 2 µm (i.e. Fusion 5 and Grade 602h produced by Whatman, Maidstone, UK) was unable to retain fruit juice DNA in a filtration 150 setting in our preliminary experiments (data not shown). Therefore, DNA sequence-based analysis is highly feasible for the authentication of commercial pure pomegranate juice. 6.5.2 LAMP for fruit juice authentication LAMP primers targeting at different ribosomal DNA and nucleus DNA regions were designed for each species of the fruit. Similar to PCR, LAMP primers designed by using the commercial software could still fail the DNA amplification in vitro. Therefore, the amplification efficacy of LAMP primers for fruit juice DNA was first tested by using the LAMP primers at various ratios of inner to outer primers (i.e. inner vs. outer primers at concentrations of 4:1, 8:1, and 16:1) and in LAMP reactions incubated at different temperatures (i.e. 62, 63, 64, 65, and 66°C). After confirming the usability of LAMP primers, primers were then subjected to specificity tests to further validate their feasibility for pomegranate juice authentication and identification of the common adulterants. After analyzing at least four sets of LAMP primers for each species of fruit, one set of primers for each fruit (Table 6.3) was identified to specifically amplify the DNA extracted from the designated fruit juice (Figure 6.5A, B, and C) at 63°C. The sensitivity of each set of primers was then determined by using a series of 10-fold diluted DNA for the LAMP reaction. Gel electrophoresis results indicated that the limit of detection for the grape and apple LAMP primers was ~100 pg of DNA extracted from fruit juices and that for pomegranate LAMP primers was ~10 pg (Figure 6.5D, E, and F). The limit of detection of LAMP primers designed in this study was comparable to LAMP primers designed for other plants in previous studies, for example 3 pg for herbal tea 54, 256 pg for mushroom 243, and 0.1 pg for saffron 244. 151 Table 6.3 Optimized LAMP primers for grape, apple and pomegranate Name Primer sequence 5’®3’ Targeted DNA Reaction temperature (°C) Ratio of inner primers to outer primers LAMP primers for grape F3 GCATTTTGTCGAAGTCTTTG matK 63 8:1 B3 GGTTTATATGGATCCTTACTGG FIP AGCCAGAATGGATTTTCCTTGATACTCAGACAATCTTATGGTTGTTC BIP GCGCCTCTTCTGATGAATAAATGGACGTAAAAATGACATTGCCAG LAMP primers for apple F3 CGGGCGTACAAACTTACACC ITS 63 4:1 B3 ACGGGATTCTGCAATTCACA FIP AAGACGACGACGCACCCGCTGTTGCGCCAAGGAATCTGA BIP TCAAAACGACTCTCGGCAACGGTCGCATTTCGCTACGTTCTT LF GGAGCGCGCTCTTTCGT LB ATATCTCGGCTCTCGCATCGA 152 Name Primer sequence 5’®3’ Targeted DNA Reaction temperature (°C) Ratio of inner primers to outer primers LAMP primers for pomegranate F3 CGTACAGTGTAAAACCCCGG ITS 63 4:1 B3 AGGACTCGATGGTTCACGG FIP ACTGCACGGTCCCTCAGGCGGAAGGCGCCAAGGATCAG BIP CGTGCGTAACCACAACGACTCTACCAAGTATCGCATTTCGCT LF CGGCGTGCCTCTCCTGTTT LB TCTCGCATCGATGAAGAACGT 153 Figure 6.5 Specificity (A, B and C) and sensitivity (D, E and F) tests of LAMP primers for grape (A and D), apple (B and E), and pomegranate (C and F). Lanes in panel A, B, and C: 1, 100-bp DNA ladder; 2, grape; 3, apple; 4, pomegranate; 5, negative control. Lanes in panel D, E, and F: L, 100-bp ladder; 10-fold serially diluted grape, apple and pomegranate DNA (initial concentration of DNA in samples ~ 5 ng/µl); NC, negative control. 6.5.3 Simplified fruit juice DNA extraction by using a paper-based device Paper-based materials, such as FTA card 230, Whatman Fusion 5 231, and PES 232 filter paper, have been applied to extract DNA from various biological materials. Except the FTA card, the other two types of paper were applied as a filter to retain the large DNA molecules while other 154 impurities could either be removed or remained on paper but will not inhibit the downstream DNA analyses. However, extracting plant DNA in fruit juice using filters is not feasible because long chain fruit DNA are degraded into small pieces during juice processing 241, which flowed through common filter papers with a pore size of ~2 µm (data not shown). FTA card, different from other papers, have reagents imbedded on them to lyse cells, denature proteins, and protect and bind DNA on the paper 230. Therefore, FTA card was investigated in this current study for fruit juice DNA extraction. In addition, as a proprietary mixture of polymers and glass fiber 232, Fusion 5 also have the potential to bind small DNA fragments to glass fiber under appropriate conditions 58, and thus was also investigated in this study. As one of the most extensively applied paper-based materials for DNA extraction 245-247, FTA card was tested in this study for their efficacy for the extraction of fruit juice DNA. Several protocols for loading the fruit juice samples onto the FTA cards were investigated. However, the fruit juice DNA extraction by using FTA cards was unsuccessful for any juice loading method although FTA cards have been successfully applied to extract DNA from plant tissues by many research groups 248-250. Unlike the leaf and root tissues, fruit tissues of plants usually possess a higher amount of water, monosaccharides, and soluble polyphenols. In addition, fruit juice DNA is highly fragmented after the physical damage of grounding and squeezing process. For the direct fruit juice loading method, the water content and fragmented DNA might flow through the FTA cards without being retained on/in the cards. Besides, water content, sugar and other soluble impurities in fruit juice could dilute and inactivate the cell lysis reagents and DNA binding reagents on/in the FTA cards, hindering their efficacy for DNA extraction and retention from fruit juice matrices. Moreover, the high amounts of soluble polyphenols in fruit juice might 155 interfere with the subsequent DNA amplification by using PCR. Based on our results, FTA cards were unsuitable for the DNA extraction from fruit juices. Another commonly used paper-based DNA extraction material is Fusion 5, a well-designed combination of five types of paper materials 231,251,252. The previous usage of Fusion 5 for DNA extraction predominantly relies on its function as a filter paper. However, with a pore size of 2 µm, Fusion 5 failed to retain the fragmented fruit juice DNA. Therefore, the DNA binding features of the silica component in Fusion 5 was explored. Silica-based materials can bind to DNA under high ionic strength conditions with the assistance of chaotropic salts, such as guanidium HCl and guanidium thiocyanate 253. Therefore, the lysis buffer with a high salt and guanidium HCl concentration was prepared to adhere DNA in fruit juice samples to Fusion 5. Fusion 5 was then washed with two types of washing buffer to remove excessive detergent and guanidium HCl, which could inhibit the downstream DNA amplification. DNA adsorbed onto Fusion 5 were finally eluted by using sterile water. The efficacy of DNA extraction was analyzed by using the SCAR PCR primers (Table 6.1). PCR reactions for fruit juice DNA extracted by using Fusion 5 yielded similar results (Figure 6.6) to that for DNA extracted by using the Qiagen kit (Figure 6.4), indicating the success of fruit juice DNA extraction by using the Fusion 5 method and the extractions were free from cross-contamination. 156 Figure 6.6 Electrophoresis agarose gel analysis of SCAR primers PCR products for DNA extracted from different fruit juices by using the Fusion 5 method. Different sets of SCAR primers were used for each panel: A, grape SCAR primer; B, apple SCAR primer; and C, pomegranate SCAR primer. Lanes in each panel were: 1, the 100-bp DNA ladder; 2, grape juice; 3, apple juice; 4, pomegranate juice; and 5, negative control. Each set of primers was only able to amplify the DNA extracted from one type of fruit juice by using the Fusion 5 method. Regardless of DNA extraction method, the PCR amplification of apple DNA was less efficient compared to that of grape and pomegranate DNA. One potential reason could be the inhibitory effects of pectin in PCR amplification 254. Apple is a common source of pectin and insufficient removal of pectin during DNA extraction might have led to the weak DNA signals in the gel electrophoresis assay. To further evaluate the DNA extraction performance of this novel method and validate its compatibility with LAMP, fruit juice DNA extracted by using Fusion 5 was then amplified by using their respective LAMP primers. As exhibited in Figure 6.7, fruit juice DNA samples extracted by Fusion 5 were successfully amplified by LAMP with a satisfactory specificity. 157 Therefore, this novel DNA extraction approach is highly compatible with the subsequent LAMP reaction and no inhibitory effect was observed for any species of the fruit. Figure 6.7 Gel electrophoresis analysis of LAMP products by using different LAMP primers for DNA extracted from different fruit juices with the Fusion 5 method. Different sets of LAMP primers were used for each panel: A, grape primers; B, apple primers; and C, pomegranate primers. Lanes in each panel were: 1, the 100-bp DNA ladder; 2, grape juice; 3, apple juice; 4, pomegranate juice; and 5, negative control. Each set of primers was only able to amplify the DNA extracted from one type of fruit juice by using the Fusion 5 method. Compared with the well-established PCR methods, Figure 6.7 and Figure 6.5 have demonstrated that the LAMP assay was more robust and less susceptible to the common inhibitors for PCR, and thus can provide significant benefits for resource-limited environments where only the crude DNA extracts are available. In addition, this novel, instrumental free Fusion 5 paper-based DNA extraction approach can further simplify the procedure of the overall analysis as well as reduce the time for DNA extraction from several hours to <5 min. 158 6.5.4 Simplified visual analysis of LAMP reaction products With the Fusion 5 DNA extraction method, the majority of the time for the overall authenticity analysis of pomegranate juice was spent on LAMP reaction (i.e. ~1 h) and electrophoresis gel assay (i.e. 40-50 min). To further reduce the analytical time and simplify the protocol, a method for direct visualization of the LAMP results to eliminate the labor for the electrophoresis gel procedure was demanded. Direct visual analysis of LAMP reaction products can be achieved by using either colorimetric dyes or fluorometric dyes. Two most commonly used dyes for LAMP are HNB for colorimetric analysis 61 and calcein/Mn2+ for fluorometric analysis 62. Although exhibiting different optical properties, the working principle of these two dyes is identical that they monitor the formation of pyrophosphate in the reaction system. As the chelating agents, these two dyes develop colors upon the changes in the concentration of specific metallic ions (e.g. Mg2+ and Mn2+). The co-precipitation of these metallic ions with the accumulated pyrophosphate during DNA amplification results in a reduced concentration of the ions, and thus cause the changes in colors of the dyes. To optimize the visualization protocol for LAMP, both dyes were evaluated and the results are illustrated in Figure 6.8. In conformance to the published research articles, HNB changed color from purple to blue upon DNA amplification, while calcein rehabilitated its green fluorescent signal after the Mn2+ being precipitated by pyrophosphate formed as a by-product of DNA amplification. Compared with the calcein/Mn2+ method, the color change in the HNB method was not as distinct. Therefore, the calcein/Mn2+ method for the visualization of LAMP reaction products was selected for further studies. 159 Figure 6.8 LAMP reactions monitored by HNB colorimetric method (A) and calcein/Mn2+ fluorometric method (B). The positive sample with HNB changed color from purple to blue, while the positive sample with calcein/Mn2+ showed strong green fluorescent signals. The color of HNB was visualized under sunlight, while the fluorescent signals of calcein were illuminated by a portable UV laser at 365 nm. The image was recorded by using the camera on an iPhone 6. Calcein/Mn2+ LAMP method was then used in the specificity tests and to analyze adulterated pomegranate juice samples. Similar to Figure 6.5A, B, and C and Figure 6.7, after LAMP reaction with different primers, only the sample containing the DNA extracted from the corresponding species of fruit juice exhibited positive calcein fluorescent signals (Figure 6.9). For the adulteration analysis, grape juice and/or apple juice were mixed with pomegranate juice at a 1:1 or 1:1:1 ratio before their DNA being extracted by using the Fusion 5 DNA extraction method. LAMP results for the different mixture have further confirmed the high specificity of the LAMP assay and the feasibility of this assay for the identification of the adulterated pomegranate juice (Figure 6.10). 160 Figure 6.9 Specificity analysis of LAMP primers for grape (A), apple (B), and pomegranate (C) monitored by using the fluorometric method of calcein/Mn2+. Samples in each panel are 1, grape; 2, apple; 3, pomegranate; and 4, negative control. All the samples were extracted by using the Fusion 5 paper-based DNA extraction method. The fluorescent signals were illuminated by using a portable UV laser at 365 nm and recorded by using the camera on an iPhone 6. Figure 6.10 Calcein/Mn2+ based visualization of LAMP results for the authentication of pomegranate juice and identification of the adulterants. DNA extraction was conducted by using the Fusion 5 method. Each panel demonstrates the LAMP results of one set of primers (A) grape, (B) apple, and (C) pomegranate. Samples in each panel are 1, grape juice; 2, apple juice; 3, pomegranate juice; 4, pomegranate juice and grape juice (1:1 v/v); 5, pomegranate juice and apple juice (1:1 v/v); 6, pomegranate juice, grape juice and apple juice (1:1:1 v/v); and 7, negative control. 161 With this direct visual analysis, the time required by this LAMP assay for the authentication of pomegranate juice was further reduced. The overall analysis, including the paper-based DNA extraction, LAMP reaction and results analysis, can be achieved in ~1 h. 6.5.5 Instrumental free sample-to-answer paper-microfluidics To achieve a simple sample-to-answer authentication of pomegranate juice, the development of an instrumental free microfluidic device is desirable. To reduce the cost of the test and to be environmental-friendlier, the ideal material for microfluidic device was the cost-effective and easily disposable paper material. To develop a paper-based microfluidic device for the authentication of pomegranate juice, preliminary evaluation of the feasibility of conducting the LAMP reaction on paper is necessary. Several research articles have demonstrated the usability of paper-based materials for LAMP reactions 232 and direct visualization of results was achieved by the addition of some colorimetric or fluorometric dyes 255,256. Confirmed by the electrophoresis gel assay, the LAMP reaction in paper-based materials (i.e. PES membrane, No. 1 filter paper, and Fusion 5) without excess of liquid was successful (data not shown). However, no visual results interpretation was achieved by using either HNB or calein/Mn2+ (color changed randomly on the paper, data not shown). This might be due to the colorimetric and fluorometric mechanism of the dyes during LAMP reactions and spatial restriction due to the paper matrices. As indicated in the previous section, both HNB and calcein/Mn2+ change color upon the changes in the concentrations of Mg2+ and Mn2+ in the reaction, whose concentrations vary due to the co-precipitation with pyrophosphate. Therefore, HNB and calcein/Mn2+ indirectly monitor the formation of pyrophosphate, a by-product of DNA amplification, and thus speculate the DNA amplification. Although the DNA amplification is highly responsible for the color change of the dyes in an aqueous system, such indirect monitoring approaches could fail in a reaction system 162 with spatial restrictions and limited reagents mobility. Without excessive liquid, reagents loaded onto the paper materials were randomly trapped in the “cages” formed by the fibers of paper with little mobility. Therefore, the localized formation of pyrophosphate might not cause the precipitation of Mg2+ and Mn2+, or the localized changes in the concentrations of Mg2+ and Mn2+ might not be sensed by the dyes trapped in other regions of the paper, resulting in failure or inconsistency in the colorimetric or fluorometric reactions. Another commonly used approach for rapid visualization of LAMP results on paper-based materials is by utilizing the modified gold-nanoparticles in a lateral flow assay 257,258. Although this approach has been successfully implemented in a variety of applications, gold-nanoparticles based visualization further complicates the analysis and increases the cost, and thus was not considered in this current study. PDMS, a common polymer for microfluidic devices, was then investigated for the feasibility of serving as reservoirs for LAMP reaction systems with unbounded liquid and monitored by calcein/Mn2+. LAMP reactions were conducted successfully in the PDMS reservoirs by loading DNA and LAMP reagents into the wells and the positive samples were detected by the fluorescent signals of calcein in the reaction systems (Figure 6.11). Therefore, a hybrid paper/polymer instrumental free microfluidic device was designed as shown in Figure 6.2. After DNA extraction, the paper device was folded as per the instruction in the Section 6.4.10. Sterile water was then loaded to elute DNA to the LAMP reaction zones and provide negative control samples. However, no DNA was detected at the LAMP reaction wells after incubating the chip at 63°C for >1.5 h. To investigate if DNA elution was successful, the Fusion 5 disk and different areas on the layer 5 and layer 6 were excised by using a 3-mm Harris micro-punch (Figure 6.12), followed by a standard PCR reaction to examine the presence of DNA. Only the Fusion 5 and areas having direct contact (area 1 in Figure 6.12) resulted in success PCR amplification 163 (data not shown), indicating that the DNA was prohibited for lateral flow on the filter paper. According to Zou and others 259, cellulose paper can adsorb and trap DNA and thus was utilized for straightforward DNA extraction. Although the targeted DNA molecules in this study were fragmented and not retained by a vertical filtration step by using a filter paper-based material (i.e. FTA card), the cellulose matrix inside filter paper exhibited strong retention capability for DNA molecules in the horizontally wicking liquid. Therefore, the elution of DNA from Fusion 5 to the LAMP reaction zones was not feasible for the current chip design. Further optimization on the design of the microfluidic device is necessary before it can achieve a simple sample-to-answer analysis of fruit juice authenticity. Figure 6.11 LAMP reactions in PDMS reaction reservoirs monitored by the fluorometric calcein/Mn2+ method. Positive marks denote samples with template DNA and NC represent negative control that contained no template DNA. Figure 6.12 To examine the efficacy of DNA elution from the layer 4, different regions on the layer 5 and layer 6 were excised by using a Harris micro-punch. The red circles labeled with different numbers represent the increased distance from the Fusion 5 paper for DNA extraction. 164 Nevertheless, by using the flat PDMS based reaction reservoirs, LAMP reactions can be activated by using a minimized portable heating pad, equipped with a temperature controller, and powered by a portable charger (Figure 6.2B). This heating device is extremely compatible with resource-limited environment and can be easily applied in field. It only costs $20. Therefore, by using the Fusion 5 paper-based DNA extraction approach, a PDMS-based reaction reservoir, and a portable heating system, an instrumental free method for pomegranate juice authentication was developed. 6.6 Conclusion Differentiation of pure pomegranate juice from the juice adulterated by grape and/or apple juice was achieved by using a LAMP-based assay. The sensitivity of LAMP analysis for grape and apple juice DNA was 100 pg, and that for pomegranate juice DNA was 10 pg. A simple paper-based or polymer/paper hybrid microfluidic device for the pomegranate juice authentication was not successful. However, with a Fusion 5 paper-based DNA extraction approach, a PDMS based reaction reservoir, and a portable heating pad, the overall analytical procedure was finished in ~1 h. The analysis required no expensive laboratory instruments and thus can be highly feasible for resource-limited environment and easily implemented for in-field applications. By designing the LAMP primers for different fruit species, authentication and/or differentiation of a variety of fruit juices can be accomplished. 165 Chapter 7: Conclusion 7.1 Major findings Food fraud, also known as economically motivated adulteration, is a severe global issue, which has been estimated to cost the food industry $10-15 billion per year 260. The common intervention strategies include strengthening the laws and regulations to increase the price of fraudulent practices and implementing traceability systems to prevent fake foods from entering the global food markets. However, the ever-increasing food fraud cases indicated the failure of these prevention strategies to some extents. Therefore, reliable food authentication techniques that are ideal for frequent inspections are inevitable to better guarantee the safety and integrity of the food supply chain. Current methods adopted by the governmental laboratories and third-party inspection agencies are usually based on liquid-chromatography and traditional polymerase chain reactions (PCR). These two types of techniques differentiate the genuine products from the adulterated ones based on the chemical compositions and DNA sequences, respectively. Although accurate and sensitive, these methods are generally slow, laborious, costly, and/or complicated, and thus are incompatible with the frequent and high-throughput analysis demanded by the inspection agencies. Therefore, alternative techniques including fingerprinting vibrational spectroscopies (i.e. mid-infrared spectroscopy, Raman spectroscopy, and nuclear magnetic resonance spectroscopy) 261 for the analysis of chemical compositions and the advanced DNA amplification method [i.e. loop-mediated isothermal amplification (LAMP)] 55 to recognize the different DNA sequences can be better solutions for high-throughput food authentication. Specifically, vibrational spectroscopic based methods require no/less samples preparation, complete data collection for each sample within a few minutes, and provide information of the overall chemical 166 compositions that can be regarded as the fingerprints of each food commodity to identify any potential adulterants. Isothermal amplification assays are less susceptible to common inhibitors; the reaction is more time-efficient; and the results can be visualized directly with colorimetric and/or fluorometric dyes. These attributes of the alternative methods fulfill the requirement of inspection agencies for food fraud analysis and thus were all investigated in this thesis project. Four types of food commodities that are highly susceptible to food fraud and their common/potential adulterants were identified based on the previously reported cases. Novel methods for the authentication of each type of foods were selected according to the inherent properties of the genuine foods and the adulterants. Specifically, ground beef meat was detected to be adulterated by meat of animal species that was less expensive, including horse meat and pork. In addition, animal offal (i.e. internal organs), the byproducts of slaughter houses, can also be used to adulterate genuine ground beef meat to increase the economic benefits of the deceptive industry. DNA sequence-based methods were commonly applied to authenticate meat products 262. However, these methods cannot identify adulterants of the same species of animal (e.g. beef offal in ground beef meat). Therefore, chemical composition-based techniques, including mid-infrared spectroscopy and Raman spectroscopy, were both applied to authenticate ground beef meat (Chapter 2). The second food model was paprika powder adulterated by Sudan I dye, which can enhance the apparent freshness of the spices. Besides the widely used liquid chromatographic based method, surface enhanced Raman spectroscopy (SERS) has also been applied to determine the Sudan I 138. However, SERS based methods usually have poor performance in quantification and require sample preparation procedures to remove the interferents in the food matrices. Therefore, nuclear magnetic resonance spectroscopy that can provide accurate quantification and require simple/no sample preparation was investigated to 167 authenticate paprika powder (Chapter 3). The third food model was finfish fillet that has been rampantly mislabeled or fraud. DNA barcoding has been the most extensively adopted method for finfish speciation and a market survey study was conducted to authenticate finfish products in the Metro Vancouver area (Chapter 4). However, this method requires highly trained personnel to conduct the experiments and analyze the results and the turnaround time is usually in the scale of several days. Thus, Raman spectroscopy combined with machine learning and chemometrics was investigated for its performance to identify different finfish species (Chapter 5). The last food model investigated in this thesis study was pure pomegranate juice adulterated by grape and/or apple juice. The increased understanding of the health benefits and high production cost of pure pomegranate juice have positioned the 100% pure pomegranate juice the target of fraudsters in food industry. Grape and apple as widely available and low-cost fruits have been used to mimic the color and enhance the sweetness of pure pomegranate juice 221. Although authentication methods based on the chemical compositions have been developed for pure pomegranate juice, chemical compositions of plant-based agri-food products are easily impacted by many other factors including the cultivation methods and the growing conditions of the plant, and thus render the accuracy of the assay. Therefore, analysis based on DNA sequence recognition (i.e. LAMP) was developed in this study (Chapter 6). Based on the different objectives of each chapter, hypotheses and objectives were developed accordingly and the status of each hypothesis is discussed chapter by chapter. Hypotheses of Chapter 2: 168 H1: Raman and FT-IR spectroscopies can detect and quantify offal concentrations in ground beef; H2: One of the spectroscopic methods performs better than another method based upon the discrimination power and quantification accuracy. According to Table 2.5, Table 2.7, Table 2.8, and Table 2.9, only FT-IR spectroscopy combined with chemometric analyses and machine learning algorithms was able to detect and quantify offal concentration in ground beef meat with adequate accuracy. The accuracy to differentiate authentic ground beef meat from meat adulterated by offal by using FT-IR spectroscopy was 100% and accurate quantification of five types of offal was also achieved. However, confocal micro-Raman spectroscopy collected spectra with poor reproducibility (Figure 1.1) and thus was unsuitable for the analysis of less homogeneous samples. Therefore, hypothesis one was rejected and hypothesis two was accepted. Hypotheses of Chapter 3: H1: Solution NMR spectroscopy can determine Sudan I in paprika powder within the commercial adulteration levels; H2: SSNMR spectroscopy can determine Sudan I in paprika powder within the commercial adulteration levels; H3: SSNMR spectroscopy can achieve a more rapid and less labor-involved analysis for Sudan I compared to solution NMR spectroscopy. The first and second hypotheses were accepted based on Figure 3.3, Figure 3.5, Figure 3.6, and Figure 3.7. The limit of detection and limit of quantification calculated by using the spectral information collected by both NMR spectrometers were all within the commercial adulteration 169 levels of Sudan I dye. Without the requirement of a liquid extraction treatment, SSNMR spectrometer-based method was less labor involved and slightly faster compared to that of the solution NMR spectrometer although SSNMR was less sensitive than solution NMR. Therefore, hypothesis three was also accepted. Hypotheses for Chapter 4: H1: As a city by the Pacific Ocean, the mislabeling rate including the intentional fraud and unintentional misidentification should be lower compared with the situation in Eastern Canada in Ontario and Montreal; H2: The compliance of labeling regulations for fish products is improved compared to the small-scale study conducted ~10 years ago in Vancouver; H3: Improvement in the current governmental regulations and industry practices minimizes the chances for finfish mislabeling. Hypothesis one was accepted because the overall seafood mislabeling rate in Metro Vancouver area was 25%, which was lower compared with that reported in the previous studies for Ontario and Montreal. However, hypothesis two was rejected because the seafood mislabeling situation revealed in this study presented no significant improvement compared with the study conducted ~10 years ago in Vancouver. After extensive discussion of the current situation of the governmental regulations and the standards in the seafood industry in Canada and comparing it with other countries, loopholes contributing to seafood mislabeling were identified. Therefore, based on the success experiences of other countries, suggestions and comments were provided to amend the current governmental regulations and seafood industry standards of Canada. These suggestions and comments were believed to reduce the seafood mislabeling rate and thus hypothesis three was accepted. 170 Hypothesis for Chapter 5: H: Both the bench-top confocal micro-Raman spectrometer and the portable Raman spectrometer combined with chemometrics can be used for species identification of raw finfish samples. Based on the results in Table 5.3, Error! Reference source not found. and Table 5.5, portable Raman spectrometer was capable of identifying 11 species of finfish fillets with adequate accuracy, while that for the bench-top confocal micro-Raman spectroscopy was not entirely satisfying. Therefore, the hypothesis was rejected. Hypothesis for Chapter 6: H1: Species-specific LAMP primers can be designed for pomegranate, apple and grape; H2: Paper-based materials can be applied to extract DNA from fruit juice; H3: Simple visual analysis of LAMP reaction can be achieved; H4: Paper-based microfluidic chip can achieve an instrumental-free sample-to-answer authentication of pure pomegranate juice. Figure 6.5A, B, and C, Figure 6.7, Figure 6.9 and Figure 6.10 all demonstrated the high specificity of the LAMP primers designed for each species of fruits and thus hypothesis one was accepted. Figure 6.6, Figure 6.7, Figure 6.9 and Figure 6.10 confirmed the success of DNA extraction by using a paper-based device. Therefore, the second hypothesis was accepted. The third hypothesis was also accepted based on the results in Figure 6.8, Figure 6.9 and Figure 6.10. However, the last hypothesis was rejected because the paper-based microfluidic device failed to enable the direct LAMP result visualization and the common cellulose-based paper materials impede the horizontal movement of DNA on paper and thus the DNA was unable to be 171 transported to the LAMP reaction zones from the DNA extraction zone. In the current study, the paper-based DNA extraction and the LAMP reaction was conducted separately and additional procedures of transferring the extracted DNA to the LAMP reaction zones were necessary. Although the whole assay required no lab-scale instrument by using a minimized portable heating pad for the LAMP incubation, hypothesis four was rejected. 7.2 Future research directions Four novel analytical methods were developed in this thesis project. The properties of each techniques including their advantages and disadvantages are summarized in Table 7.1. These techniques demonstrated high potential to be adopted by governmental laboratories, food industries and even consumers to authenticate a broad spectrum of food products. However, improvements in the experimental design and data analysis and further studies should be performed to optimize these methods. Table 7.1 Comparison of the properties of four types of analytical techniques being applied in this thesis study. Techniques Advantages Disadvantages Fourier-transformed infrared spectroscopy • Rapid • Nondestructive • Relatively low capital cost • Low maintenance cost • Simple procedure • Targeted & Untargeted analysis • Low molecular resolution • Relatively low sensitivity • uncapable of absolute quantification 172 Techniques Advantages Disadvantages Raman spectroscopy • Rapid • Nondestructive • Relatively low capital cost • Low maintenance cost • Simple procedure • Targeted & Untargeted analysis • Relatively low sensitivity • Uncapable of absolute quantification Nuclear magnetic resonance (NMR) spectroscopy • Rapid • Nondestructive (solid state NMR spectrometer) • Capable of absolute quantification • High molecular resolution • Targeted & Untargeted analysis • Low sensitivity • Liquified samples (solution NMR spectrometer) • Low sensitivity • Expensive capital cost • Relatively expensive maintenance cost Loop-mediated isothermal amplification • Rapid • Simple procedure • Low cost • High sensitivity • Susceptible to false positive reaction • Targeted analysis For Chapter 2, the Raman spectrometer used in this study was a confocal micro-Raman spectrometer with a micron-size laser spot to focus on the sample to provide high spatial resolution. However, for less homogeneous food products, such confocal micro-Raman 173 spectrometer becomes less useful. In the future study, Raman spectrometer with a larger laser spot illuminating on a larger surface of the samples should be investigated for their performance in beef meat authentication. In addition, optimized classification and quantification accuracy might be achieved by using more advanced machine learning algorithms. To eventually commercialize the FT-IR spectroscopic- and/or Raman spectroscopic-based methods for the authentication of ground beef meat, spectral information of a wider varieties of adulterants, potential adulterants and adulterated samples should be collected. For the second study (Chapter 3), two 600-MHz NMR spectrometers were applied to determine Sudan I dye in the paprika powder. Although the infrastructure for a high field NMR spectrometer was less expensive compared with decades ago, the capital cost and the maintenance are still unaffordable for many inspection agencies and food industries. Therefore, further studies should be conducted to perform the analysis on NMR spectrometers with a lower magnetic field and even on a bench-top low-field NMR spectrometer that are more suitable for real world applications. Furthermore, NMR spectroscopic-based paprika powder authentication method can be generalized to authenticate other food commodities and to determine other food adulterants. For Chapter 5, more species of finfish can be collected and their Raman spectra can be collected to complete the spectral library. More advanced machine learning algorithms should be applied to enhance the performance of the classifiers for fish species identification. Moreover, the instrumentation of the bench-top confocal micro-Raman spectrometer should be optimized to further improve the spectral reproducibility and thus improve the accuracy for finfish speciation by using the bench-top confocal micro-Raman spectrometer. 174 For the last technique developed for the authentication of pomegranate juice Chapter 6, optimization of the design for a simple sample-to-answer microfluidic lab-on-a-chip device is necessary. By designing species-specific primers, this method should be generalized to analyze other fruit juices. To further simplify the analysis and to be better prepared for resources-limited regions, pre-lyophilizing LAMP reagents into the microfluidic device is desirable. In addition, other isothermal amplification with an even simpler reaction condition, such as the recombinase polymerase amplification at body temperature, should be investigated for their performance in fruit juice authentication. Although the motivation for food fraud is as simple as for extra economic benefits, the practices vary from case to case and fraudsters can often find loopholes in the current regulation, standards, supply chain and analytical methods. Generally, there are more than one type of adulterant or adulteration approach in each type of food commodity. To achieve an effective and systematic food authentication, different techniques can be applied as complementary methods. For example, the addition of sugar or artificial flavoring compounds in fruit juice can be analyzed by using the spectroscopy-based methods, while the substitution by other species of fruit juice can be identified by using the DNA sequence-based analysis. 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Food Control 80, 23-28, doi:https://doi.org/10.1016/j.foodcont.2017.04.025 (2017). 192 Appendices The composition and characteristics of fish samples collected in Metro Vancouver Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) 1 Sushi bar raw plain fillet light 0 2 Sushi bar raw plain fillet red 0 3 Sushi bar raw plain fillet light 0 4 Sushi bar raw plain fillet light 0 5 Sushi bar raw plain fillet red 0 6 Sushi bar raw plain fillet light 0 7 Sushi bar raw plain fillet light 0 8 Sushi bar raw plain fillet red 0 9 Sushi bar raw plain fillet red 0 10 Sushi bar raw plain fillet light N/A 11 Sushi bar raw modified fillet light 0 12 Sushi bar raw plain fillet light 1 13 Sushi bar raw plain fillet light 0 14 Sushi bar raw plain fillet red 0 15 Sushi bar raw plain fillet light 0 16 Sushi bar raw plain fillet light 0 193 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) 17 Sushi bar raw plain fillet red 0 18 Sushi bar raw plain fillet light 0 19 Sushi bar raw plain fillet light 0 20 Sushi bar raw plain fillet light 1 21 Sushi bar raw plain fillet red 0 22 Sushi bar raw plain fillet light 0 23 Sushi bar raw modified fillet light 0 24 Sushi bar raw plain fillet red 0 25 Sushi bar raw plain fillet red 1 26 Sushi bar raw modified fillet light 0 27 Sushi bar raw plain fillet light 0 28 Sushi bar raw plain fillet red 0 29 Sushi bar raw plain fillet light 0 30 Sushi bar raw plain fillet red 0 31 Sushi bar raw modified fillet light 0 32 Sushi bar raw plain fillet red 0 33 Sushi bar raw plain fillet light 0 34 Sushi bar raw modified fillet light 0 35 Sushi bar raw plain fillet light 0 36 Sushi bar raw modified fillet light 0 194 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) 37 Sushi bar raw plain fillet red 0 38 Sushi bar raw plain fillet red 0 39 Sushi bar raw modified fillet light 0 40 Sushi bar raw plain fillet light 0 41 Sushi bar raw plain fillet red 0 42 Sushi bar raw modified fillet light 0 43 Sushi bar raw plain fillet light 0 44 Sushi bar raw plain fillet light 0 45 Sushi bar raw plain fillet light 1 46 Sushi bar raw modified fillet light 0 47 Sushi bar raw plain fillet red 0 48 Sushi bar raw plain fillet light 0 49 Sushi bar raw plain fillet light 1 50 Sushi bar raw modified fillet light 0 51 Sushi bar raw plain fillet red 0 52 Sushi bar raw plain fillet light 0 53 Sushi bar raw plain fillet light 0 54 Sushi bar raw plain fillet red 0 55 Sushi bar raw modified fillet light 0 56 Sushi bar raw plain fillet light 1 195 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) 57 Sushi bar raw plain fillet light 0 58 Sushi bar raw plain fillet red 0 59 Sushi bar raw modified fillet light 0 60 Sushi bar raw plain fillet light 1 61 Sushi bar raw plain fillet light 0 62 Sushi bar raw plain fillet red 0 63 Sushi bar raw modified fillet light 0 64 Sushi bar raw plain fillet light 0 65 Sushi bar raw plain fillet red 0 66 Sushi bar raw modified fillet light 0 67 Sushi bar raw plain fillet light 1 68 Sushi bar raw plain fillet light 0 69 Sushi bar raw plain fillet light 1 70 Sushi bar raw plain fillet red 0 71 Sushi bar raw plain fillet light 1 72 Sushi bar raw plain fillet light 0 73 Sushi bar raw plain fillet light 1 74 Sushi bar raw plain fillet light 1 75 Sushi bar raw plain fillet red 0 76 Sushi bar raw plain fillet light 1 196 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) 77 Sushi bar raw plain fillet red 0 78 Sushi bar raw plain fillet light 1 79 Sushi bar raw plain fillet red 0 80 Sushi bar raw plain fillet light 1 81 Sushi bar raw plain fillet light 1 82 Sushi bar raw plain fillet red 0 83 Sushi bar raw plain fillet light 1 87 Sushi bar raw plain fillet red 0 88 Sushi bar raw plain fillet red 0 89 Sushi bar raw plain fillet red 0 90 Sushi bar raw plain fillet light 0 91 Sushi bar raw plain fillet light 0 92 Sushi bar raw plain fillet light 0 93 Sushi bar raw plain fillet red 0 94 Sushi bar raw plain fillet red 0 95 Sushi bar raw plain fillet light 1 96 Sushi bar raw plain fillet light 1 97 Sushi bar raw plain fillet light 0 98 Sushi bar raw plain fillet red 0 99 Sushi bar raw plain fillet light 1 197 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) 100 Sushi bar raw plain fillet red 0 102 Sushi bar raw plain fillet light 1 103 Sushi bar raw plain fillet red 0 104 Sushi bar raw plain fillet light 1 105 Sushi bar raw plain fillet light 0 106 Sushi bar raw plain fillet light 0 107 Sushi bar raw plain fillet red 0 108 Sushi bar raw plain fillet red 0 109 Sushi bar raw plain fillet light 1 110 Sushi bar raw plain fillet red 0 111 Sushi bar raw plain fillet light 1 112 Sushi bar cooked modified fillet light 0 R1 Restaurant cooked modified fillet light 0 R2 Restaurant cooked modified fillet red N/A R3 Restaurant raw plain fillet red 0 R4 Restaurant cooked modified fillet light 0 R5 Restaurant cooked modified fillet light 0 R6 Restaurant cooked modified fillet light 1 R7 Restaurant cooked modified fillet light 0 R8 Restaurant cooked modified fillet red 0 198 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) R9 Restaurant cooked modified chunk light 0 R10 Restaurant cooked modified fillet light 0 R11 Restaurant cooked modified fillet light 1 R12 Restaurant cooked modified fillet light 0 R13 Restaurant cooked modified fillet red 0 R14 Restaurant cooked modified fillet light 0 R15 Restaurant cooked modified fillet red 0 R16 Restaurant cooked modified fillet light 0 R17 Restaurant cooked modified chunk light 1 R18 Restaurant raw modified chunk red 0 R19 Restaurant cooked modified fillet red 0 R20 Restaurant cooked modified fillet light 1 R21 Restaurant cooked modified fillet red 1 R22 Restaurant cooked modified fillet light 0 R23 Restaurant cooked modified fillet red 1 R24 Restaurant cooked modified fillet red 0 R25 Restaurant cooked modified fillet light 1 R26 Restaurant cooked modified fillet light 0 R27 Restaurant cooked modified fillet light 0 R28 Restaurant raw modified fillet red 0 199 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) R29 Restaurant cooked modified chunk light 0 R30 Restaurant cooked modified fillet light 1 R31 Restaurant raw modified fillet red 0 R32 Restaurant cooked modified chunk light 0 R33 Restaurant cooked modified fillet light 0 R34 Restaurant cooked modified fillet light 0 R35 Restaurant cooked modified fillet light 1 R36 Restaurant cooked modified fillet red 0 R37 Restaurant cooked modified chunk light 0 R38 Restaurant cooked modified fillet light 0 R39 Restaurant cooked modified fillet red 0 R40 Restaurant cooked modified fillet light 0 R41 Restaurant raw modified fillet red 0 R42 Restaurant raw modified fillet red 1 R43 Restaurant raw modified fillet light 0 R44 Restaurant cooked modified fillet light 1 R45 Restaurant cooked modified fillet light 1 R46 Restaurant cooked modified fillet light 1 R47 Restaurant cooked modified fillet light 1 R48 Restaurant cooked modified fillet light 0 200 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) R49 Restaurant cooked modified fillet light 1 R50 Restaurant cooked modified fillet light 1 R51 Restaurant cooked modified fillet light N/A R52 Restaurant cooked modified fillet light 1 R53 Restaurant cooked modified fillet light 1 R55 Restaurant raw modified chunk red 0 R56 Restaurant raw modified chunk red 0 R57 Restaurant cooked modified fillet red 0 R61 Restaurant cooked modified fillet light 0 R62 Restaurant cooked modified fillet red 0 R63 Restaurant cooked modified fillet light 0 R64 Restaurant cooked modified fillet red 0 R65 Restaurant cooked modified fillet light 0 R66 Restaurant raw modified chunk red 0 R67 Restaurant cooked modified fillet light 0 R68 Restaurant cooked modified fillet light 0 R69 Restaurant cooked modified fillet light 0 R70 Restaurant cooked modified fillet red 0 R71 Restaurant cooked modified fillet light 0 R72 Restaurant cooked modified fillet light 0 201 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) R73 Restaurant cooked modified fillet light 0 R74 Restaurant cooked modified fillet red 0 R75 Restaurant cooked modified fillet light 0 R76 Restaurant cooked modified fillet light 1 R77 Restaurant cooked modified fillet red 1 R78 Restaurant cooked modified fillet red 0 R79 Restaurant cooked modified fillet light 0 R80 Restaurant cooked modified fillet light 1 R81 Restaurant cooked modified fillet light 1 R82 Restaurant cooked modified fillet light 0 R83 Restaurant cooked modified fillet red 0 R84 Restaurant cooked modified fillet light 1 R85 Restaurant cooked modified fillet light 0 R86 Restaurant cooked modified fillet light 1 R87 Restaurant cooked modified fillet light 0 R88 Restaurant cooked modified fillet light 0 G1 Grocery raw plain fillet light 0 G2 Grocery raw plain fillet red 0 G3 Grocery raw modified chunk red 0 G4 Grocery raw modified fillet red 0 202 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) G5 Grocery raw plain fillet red 0 G6 Grocery raw plain fillet red 0 G7 Grocery raw plain fillet light 0 G11 Grocery cooked modified chunk red 0 G12 Grocery raw plain fillet light 0 G18 Grocery cooked modified chunk red 0 G19 Grocery cooked modified chunk red 0 G20 Grocery cooked modified chunk red 0 G22 Grocery cooked modified whole light 0 G24 Grocery cooked modified chunk red 1 G26 Grocery cooked modified chunk red 0 G29 Grocery cooked modified chunk red 0 G30 Grocery cooked modified chunk red 1 G31 Grocery raw modified chopped red 0 G32 Grocery cooked modified chunk red 0 G33 Grocery cooked modified chunk red 0 G37 Grocery raw modified chunk red 0 G38 Grocery raw modified chunk red 0 G39 Grocery raw modified fillet red 0 G40 Grocery raw plain fillet red 0 203 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) G41 Grocery raw plain fillet light 1 G42 Grocery raw modified fillet light 1 G43 Grocery raw plain fillet light 0 G44 Grocery raw modified fillet red 0 G45 Grocery raw modified fillet light 0 G46 Grocery raw plain fillet light 0 G47 Grocery raw plain whole light 1 G48 Grocery raw modified chopped red 0 G49 Grocery cooked modified chunk red 0 G57 Grocery raw plain fillet light 0 G58 Grocery raw plain fillet light 1 G59 Grocery raw plain fillet red 0 G60 Grocery raw plain fillet light 0 G61 Grocery raw plain fillet light 0 G62 Grocery raw plain fillet red 0 G63 Grocery cooked modified chunk light 0 G64 Grocery cooked modified chunk red 0 G65 Grocery raw plain fillet light 1 G66 Grocery raw plain fillet red 0 G67 Grocery raw plain fillet light 1 204 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) G68 Grocery raw plain fillet red 0 G69 Grocery raw modified chunk light 0 G70 Grocery raw modified chunk light 0 G71 Grocery raw modified chopped red 1 G72 Grocery raw plain whole red 0 G73 Grocery raw plain fillet red 0 G74 Grocery raw plain fillet light 1 G75 Grocery raw plain fillet red 0 G76 Grocery raw plain fillet light 0 G77 Grocery raw modified fillet light 0 G78 Grocery raw plain fillet light 1 G79 Grocery raw plain fillet light 0 G80 Grocery raw modified fillet light N/A G81 Grocery raw plain fillet light 0 G82 Grocery raw plain fillet light 1 G83 Grocery raw plain fillet red 0 G84 Grocery raw plain fillet red 0 G85 Grocery raw plain fillet light 0 G86 Grocery raw plain whole light 1 G87 Grocery raw plain whole light 1 205 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) G88 Grocery raw plain fillet light 0 G89 Grocery raw plain fillet light 0 G90 Grocery raw plain whole light 1 G91 Grocery raw plain fillet red 1 G92 Grocery raw plain fillet red 0 G93 Grocery raw plain whole light 1 G108 Grocery raw plain fillet light 0 G109 Grocery raw plain fillet red 0 G110 Grocery raw plain fillet light 0 G111 Grocery raw plain fillet light 0 G112 Grocery raw plain fillet red 0 G113 Grocery raw plain fillet light 0 G114 Grocery raw plain fillet red 0 G115 Grocery raw plain fillet light 0 G116 Grocery raw plain fillet light 0 G117 Grocery raw plain fillet light 0 G118 Grocery raw plain fillet light 0 G119 Grocery raw plain fillet light 1 G120 Grocery raw plain fillet light 1 G121 Grocery raw plain fillet light 0 206 Sample ID source raw/cooked appearance product form color of the flesh mislabeled (0 = correctly labeled, 1 = mislabeled) G122 Grocery raw plain fillet light 1 G123 Grocery raw plain fillet red 0 G124 Grocery raw plain fillet light 1 G125 Grocery raw plain fillet light 0 G126 Grocery raw plain fillet red 0 G127 Grocery raw plain fillet light 0 G128 Grocery raw plain fillet light 0 G129 Grocery raw plain fillet light 1 G130 Grocery raw plain fillet light 0
Thesis/Dissertation
eng
Food Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Development of innovative techniques for food authentication - the last barrier to prevent food fraud
Text