Land and Food Systems, Faculty of
DSpace
UBCV
Feng, Shaolong
Doctor of Philosophy - PhD
University of British Columbia
Food chemical contamination remains one of the burdens to public health and may cause significant economic loss to agri-food industry and healthcare. Rapid, selective and sensitive determination of chemical contaminants in foods is of great importance to control food quality and ensure food safety. Traditional sample pretreatment methods have limited selectivity to the analytes and detection techniques (e.g., liquid chromatography-based methods) are usually labor-intensive, time-consuming, and even lack the specificity and/or sensitivity. Therefore, the overall objective of my Ph.D. thesis project was to investigate different chemosensors combining selective molecularly imprinted polymers (MIPs) with either surface enhanced Raman spectroscopy (SERS) or fluorescent quantum dots (QDs) to determine chemical hazards in different food matrices. Firstly, a MIPs-SERS/colorimetric dual sensor was developed using MIPs-packed solid phase extraction cartridge to isolate chlorpyrifos from apple juice. Silver nanoparticle (AgNPs) was applied for both colorimetric reaction for rapidly screening and semi-quantification of chlorpyrifos with concentrations ≥5 µg/mL by naked eye and SERS analysis for sensitive determination with a limit of detection (LOD) of 0.01 µg/mL based on aggregated AgNPs induced by chlorpyrifos. To further simplify SERS measurement, MIPs was wrapped on the surface of gold nanoparticles (AuNPs) as a dual functional nanomaterial (i.e., AuNPs@MIPs). A representative herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) was selectively separated from milk by AuNPs@MIPs. Without analyte elution step, SERS measurement was directly conducted on AuNPs@MIPs and a LOD of 0.011 µg/mL was achieved for sensing 2,4-D. Furthermore, a quantum dots (QDs) based fluorescence sensor was developed to determinate enrofloxacin in swine muscle. Taking advantage of fluorescence quenching effect of QDs, enrofloxacin was sensitively detected with a LOD of 0.059 µg/mL. An inertial separation microfluidic chip was designed and fabricated to integrate sample pretreatment, analyte separation and detection on the chip. The separation efficiency of the developed microfluidic chip for QDs@MIPs microparticles was as high as 97.7±1.6%. Taken together, MIPs were validated to be robust materials for selective separation and analysis of food chemical hazards. Combining MIPs with different detection tools has great potential for determination of chemical hazards in complex agri-food products.
DEVELOPMENT OF NOVEL MOLECULARLY IMPRINTING CHEMOSENSORS FOR THE DETECTION OF CHEMICAL HAZARDS IN FOODS by Shaolong Feng BSc, Tianjin University of Science & Technology, 2011 MSc, Tianjin University of Science & Technology, 2014 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) September 2021 © Shaolong Feng, 2021 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 novel molecularly imprinting chemosensors for the detection of chemical hazards in foods submitted by Shaolong Feng in partial fulfillment of the requirements for the degree of Doctor in Philosophy in Food Science Examining Committee: Xiaonan Lu, Associate Professor, Food Science, UBC Supervisor Keng C. Chou, Professor, Chemistry, UBC Supervisory Committee Member Mu Chiao, Professor, Mechanical Engineering, UBC University Examiner Savvas G. Hatzikiriakos, Professor, Chemical and Biological Engineering, UBC University Examiner Additional Supervisory Committee Members: Christine Scaman, Associate professor, Food Science, UBC Co-supervisor Russ Algar, Associate Professor, Chemistry, UBC Supervisory Committee Member iii Abstract Food chemical contamination remains one of the burdens to public health and may cause significant economic loss to agri-food industry and healthcare. Rapid, selective and sensitive determination of chemical contaminants in foods is of great importance to control food quality and ensure food safety. Traditional sample pretreatment methods have limited selectivity to the analytes and detection techniques (e.g., liquid chromatography-based methods) are usually labor-intensive, time-consuming, and even lack the specificity and/or sensitivity. Therefore, the overall objective of my Ph.D. thesis project was to investigate different chemosensors combining selective molecularly imprinted polymers (MIPs) with either surface enhanced Raman spectroscopy (SERS) or fluorescent quantum dots (QDs) to determine chemical hazards in different food matrices. Firstly, a MIPs-SERS/colorimetric dual sensor was developed using MIPs-packed solid phase extraction cartridge to isolate chlorpyrifos from apple juice. Silver nanoparticle (AgNPs) was applied for both colorimetric reaction for rapidly screening and semi-quantification of chlorpyrifos with concentrations ≥5 µg/mL by naked eye and SERS analysis for sensitive determination with a limit of detection (LOD) of 0.01 µg/mL based on aggregated AgNPs induced by chlorpyrifos. To further simplify SERS measurement, MIPs was wrapped on the surface of gold nanoparticles (AuNPs) as a dual functional nanomaterial (i.e., AuNPs@MIPs). A representative herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) was selectively separated from milk by AuNPs@MIPs. Without analyte elution step, SERS measurement was directly conducted on AuNPs@MIPs and a LOD of 0.011 µg/mL was achieved for sensing 2,4-D. iv Furthermore, a quantum dots (QDs) based fluorescence sensor was developed to determinate enrofloxacin in swine muscle. Taking advantage of fluorescence quenching effect of QDs, enrofloxacin was sensitively detected with a LOD of 0.059 µg/mL. An inertial separation microfluidic chip was designed and fabricated to integrate sample pretreatment, analyte separation and detection on the chip. The separation efficiency of the developed microfluidic chip for QDs@MIPs microparticles was as high as 97.7±1.6%. Taken together, MIPs were validated to be robust materials for selective separation and analysis of food chemical hazards. Combining MIPs with different detection tools has great potential for determination of chemical hazards in complex agri-food products. v Lay Summary Food chemical hazards not only cause many serious foodborne illnesses but also result in significant cost to agri-food industry and healthcare every year. Safe diet is the primary goal for consumers worldwide. Effective and efficient analytical methods for chemical hazards determination are critical to ensure food quality and food safety. Traditional analytical methods for food chemical hazards detection have many flaws such as lack of selectivity, labor-intensive, and time-consuming. My Ph.D. study focused on using novel functional materials and advanced analytical technique to develop different chemosensors to improve or even conquer the drawbacks of the traditional detection methods and effectively determinate various chemical hazards in agri-food commodities. We expect the analytical methods developed in this thesis can be adopted in practical food analysis to protect consumers from food chemical contaminations. vi Preface Chapter 2 is a version of a published journal article “Feng, S., Hu, Y., Ma, L., Lu, X., (2017). Development of molecularly imprinted polymers-surface-enhanced Raman spectroscopy/colorimetric dual sensor for determination of chlorpyrifos in apple juice. Sensors & Actuators B: Chemical, 241, 750-757”. I, as the first author, designed the experiment, conducted all the testing and wrote the manuscript. Dr. Xiaonan Lu provided significant feedback on experimental design and critically reviewed the manuscript. Dr. Yaxi Hu provided technical support for data analysis and revised the manuscript. Luyao Ma provided useful feedback on revising the manuscript. Chapter 3 is the basis of an unpublished manuscript. I, as the first author, designed the experiment, conducted all the testing, analyzed the results and wrote the manuscript. Dr. Xiaonan Lu provided significant feedbacks on experimental design and critically reviewed the manuscript. Dr. Lei Chen provided technical support for establishing 3D-FDTD simulation model. Chapter 4 is the basis of another unpublished manuscript. I developed experimental design. Both Dr. Xiaonan Lu and Dr. Jie Xu provided significant feedbacks on experimental design. Dr. Yang Lin provided technical supports for the design and fabrication of the microfluidic device. I conducted all the experiments, collected and analyzed the data. I wrote the manuscript and Dr. Xiaonan Lu critically reviewed it. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii List of Abbreviations ............................................................................................................... xviii Acknowledgements ................................................................................................................... xxii Dedication ................................................................................................................................. xxiii Chapter 1: Introduction ................................................................................................................1 1.1 Food safety and food chemical hazards .......................................................................... 1 1.2 Traditional methods for determination of food chemical hazards .................................. 3 1.3 Innovative techniques for determination of food chemical hazards ............................... 6 1.3.1 Molecularly imprinted polymers (MIPs) .................................................................... 6 1.3.1.1 Preparation methods of MIPs .............................................................................. 8 1.3.1.2 Applications of MIPs in food analysis .............................................................. 11 1.3.2 Spectroscopic analytical techniques ......................................................................... 13 1.3.2.1 Infrared (IR) and Raman spectroscopy ............................................................. 13 1.3.2.2 Surface enhanced Raman spectroscopy (SERS) ............................................... 14 1.3.2.3 Applications of SERS in food chemical analysis ............................................. 15 1.3.3 Quantum dots (QDs)-based fluorescence sensors ..................................................... 17 viii 1.4 Microfluidic “lab-on-a-chip” technology ..................................................................... 20 1.5 Central hypothesis and objectives ................................................................................. 21 1.5.1 Central hypothesis ..................................................................................................... 21 1.5.2 Objectives ................................................................................................................. 21 Chapter 2: Development of molecularly imprinted polymers-surface-enhanced Raman spectroscopy/colorimetric dual sensor for determination of chlorpyrifos in apple juice ......23 2.1 Introduction ................................................................................................................... 23 2.2 Hypotheses .................................................................................................................... 27 2.3 Objectives ..................................................................................................................... 27 2.4 Materials and methods .................................................................................................. 28 2.4.1 Chemicals and materials ........................................................................................... 28 2.4.2 Synthesis of MIPs ..................................................................................................... 28 2.4.3 Adsorption test .......................................................................................................... 29 2.4.4 Pretreatment of apple juice ....................................................................................... 30 2.4.5 Molecularly imprinted solid phase extraction (MISPE) ........................................... 30 2.4.6 Colorimetric and UV-Vis spectroscopic determination of CPF ............................... 31 2.4.7 Raman spectroscopic instrumentation and SERS ..................................................... 31 2.4.8 Spectral analysis and chemometric models .............................................................. 32 2.5 Results and discussion .................................................................................................. 33 2.5.1 Synthesis and characterization of CPF MIPs ............................................................ 34 2.5.2 MISPE for CPF spiked apple juice ........................................................................... 37 2.5.3 Colorimetric and UV-Vis spectroscopic detection of CPF ....................................... 38 2.5.4 Determination of CPF in apple juice by SERS ......................................................... 42 ix 2.6 Conclusion .................................................................................................................... 49 Chapter 3: Molecularly imprinted core-shell gold nanoparticles for sensitive and quantitative detection of 2,4-dichlorophenoxyacetic acid in milk using surface-enhanced Raman spectroscopy ....................................................................................................................51 3.1 Introduction ................................................................................................................... 51 3.2 Hypotheses .................................................................................................................... 55 3.3 Objectives ..................................................................................................................... 55 3.4 Material and methods .................................................................................................... 56 3.4.1 Materials ................................................................................................................... 56 3.4.2 Preparation of functional AuNPs@MIPs SERS substrate ........................................ 56 3.4.3 Preparation of AuNPs ............................................................................................... 57 3.4.4 4-ATP-embedded MIPs shell wrapping .................................................................... 57 3.4.5 Adsorption performance confirmation for AuNPs@MIPs ....................................... 58 3.4.6 Pretreatment procedures for milk samples ................................................................ 59 3.4.7 SERS measurements ................................................................................................. 59 3.4.8 Characterizations ....................................................................................................... 60 3.4.9 Three-dimensional finite difference time domain (3D-FDTD) simulation method . 60 3.4.10 Spectral analysis and chemometric models .......................................................... 60 3.5 Results and discussion .................................................................................................. 61 3.5.1 Preparation and characterization of 2,4-D-imprinted AuNPs@MIPs SERS substrate . ................................................................................................................................... 62 3.5.2 3D-FDTD simulation for AuNPs@MIPs SERS substrate ........................................ 71 3.5.3 SERS performance of AuNPs@MIPs for 2,4-D detection ....................................... 73 x 3.6 Conclusion .................................................................................................................... 84 Chapter 4: Microfluidic-assisted rapid detection of chemical contaminant in food using quantum dots-based molecular imprinting sensor ...................................................................85 4.1 Introduction ................................................................................................................... 85 4.2 Hypotheses .................................................................................................................... 88 4.3 Objectives ..................................................................................................................... 88 4.4 Material and methods .................................................................................................... 89 4.4.1 Reagents and Materials ............................................................................................. 89 4.4.2 Characterizations ....................................................................................................... 89 4.4.3 Design and fabrication of inertial separation microfluidic device ............................ 90 4.4.4 Preparation of GSH-coated QDs ............................................................................... 91 4.4.5 Preparation of fluorescent QDs@MIPs sensing particles ......................................... 91 4.4.6 Adsorption performance of QDs@MIPs .................................................................. 92 4.4.7 Fluorescence measurement ....................................................................................... 93 4.4.8 Swine muscle sample preparation and enrofloxacin detection using microfluidic device ................................................................................................................................... 93 4.4.9 Data analysis ............................................................................................................. 94 4.5 Results and discussion .................................................................................................. 94 4.5.1 Design principle ........................................................................................................ 94 4.5.2 Characterization of the inertial separation microfluidic device ................................ 99 4.5.3 Preparation and characterization of fluorescent QDs@MIPs sensing spheres ....... 100 4.5.4 Adsorption capacity of QDs@MIPs to enrofloxacin .............................................. 103 4.5.5 Performance of QDs@MIPs for enrofloxacin detection......................................... 105 xi 4.5.6 Microfluidic-assisted enrofloxacin detection and food samples analysis ............... 107 4.6 Conclusion .................................................................................................................. 110 Chapter 5: Conclusion ...............................................................................................................111 5.1 Main findings .............................................................................................................. 111 5.2 Future research directions ........................................................................................... 113 Bibliography ...............................................................................................................................116 xii List of Tables Table 1.1 Comparison of different molecular imprinting methods for the preparation of MIPs. . 10 Table 2.1 Recoveries of spiked chlorpyrifos in apple juice by molecularly imprinted solid phase extraction (MISPE) and non-imprinted solid phase extraction (NISPE). ..................................... 37 Table 2.2 Raman peak assignment of normal Raman and SERS spectral features of chlorpyrifos(Kim, Barcelo, & Li, 2015; Movasaghi, Rehman, & Rehman, 2007; Shende, Inscore, Sengupta, Stuart, & Farquharson, 2010)........................................................................................................................................................ 45 Table 2.3 PLSR model for prediction of chlorpyrifos concentration in apple juice using SERS spectral features. ........................................................................................................................... 48 Table 3.1 Summary of vibrational band assignments for 4-ATP and 2,4-D (Costa et al., 2009; X. G. Hu, Wang, Wang, & Dong, 2007). .......................................................................................... 68 Table 3.2 PLSR model for prediction of 2,4-D concentrations in standard samples and spiked milk samples using SERS spectral features based on Raman shift range from 1020 cm-1 to 1800 cm-1. .............................................................................................................................................. 82 Table 3.3 Average extraction recoveries and relative standard deviations of 2,4-D at various spiking levels for milk sample. ..................................................................................................... 83 Table 4.1 Average extraction recoveries and relative standard deviations of enrofloxacin at various spiking levels for swine muscle samples. ...................................................................... 110 xiii List of Figures Figure 1.1 Schematic illustration of the principle and process of developing molecularly imprinted polymers (Shaolong Feng & Lu, 2015). ........................................................................................ 7 Figure 1.2 Schematic illustration of the excitation of the localized surface plasmon resonance (LSPR) of a spherical nanoparticle by incident electromagnetic field. ........................................ 15 Figure 1.3 Schematic illustration of the principle of QDs@MIPs fluorescence sensor. .............. 19 Figure 2.1 Schematic illustration of MIPs-SERS/colorimetric dual sensing system for the determination of chlorpyrifos in apple juice. SPE: solid-phase extraction; CPF: chlorpyrifos; AgNPs: silver nanoparticles; SERS: surface-enhanced Raman spectroscopic; LSPR: localized surface plasmon resonance. .......................................................................................................... 34 Figure 2.2 (A) Static binding isotherms of molecularly imprinted polymers (MIPs) and non-imprinted polymers (NIPs) for chlorpyrifos (CPF) (N = 3); (B) Kinetic binding isotherms of MIPs and NIPs (N = 3). ................................................................................................................ 36 Figure 2.3 UV-Vis absorption spectrum of AgNPs. ..................................................................... 39 Figure 2.4 (A) Representative photographic image of colorimetric sensing with different concentrations of CPF standard solutions; (B) average UV-Vis absorption spectra of AgNPs in the presence of different concentrations of CPF (N = 3); (C) representative photographic image of colorimetric sensing with MISPE eluents of apple juice spiked with different concentrations of CPF; (D) average UV-Vis absorption spectra of AgNPs in the presence of MISPE eluents of apple juice spiked with different concentrations of CPF (N = 3) ; (E) plot of the average absorption values at 393 nm versus different concentrations of CPF standard solutions (N = 3). CPF: chlorpyrifos; MISPE: molecularly imprinted solid phase extraction. ................................. 41 xiv Figure 2.5 (A) Representative spectral features of (a) CN membrane, (b) AgNPs on CN membrane, (c) normal Raman of CPF, (d) SERS of CPF standard solution (0.1 mg/L), (e) MIPs-SERS of apple juice sample spiked with CPF (0.1 mg/L); (B) Averaged MIPs-SERS spectra (N = 10) of CPF in apple juice with different concentrations a) 0 mg/L, b) 0.01 mg/L, c) 0.1 mg/L, d) 1 mg/L. CN: cellulose nitrate; CPF: chlorpyrifos; SERS: surface enhanced Raman spectroscopy. ................................................................................................................................. 44 Figure 2.6 Representative two-dimensional principal component analysis for apple juice samples spiked with different concentrations of chlorpyrifos (blue: 0 mg/L, orange: 0.01 mg/L, yellow: 0.1 mg/L, purple: 1 mg/L) (N = 3). ............................................................................................... 47 Figure 2.7 Representative calibrated partial least-squares regression model for the prediction of chlorpyrifos concentration in apple juice using SERS spectral features (Raman shift range: 600 – 900 cm-1). ...................................................................................................................................... 48 Figure 3.1 Schematic illustration of the preparation of 2,4-D imprinted AuNPs@MIPs SERS substrate with 4-ATP embedded as the internal standard. ............................................................ 57 Figure 3.2 Characterization of the as-synthesized nanoparticles. Representative TEM images (left) of (a) AuNPs, (b) AuNPs@MIPs, and (c) AuNPs@NIPs, and the relevant histograms (right) of (d) AuNPs size distribution, and (e/f) MIPs/NIPs shell thickness analyzed from TEM images using ImageJ software based on over 50 nanoparticles. (g) UV-Vis absorption spectra of AuNPs, AuNPs@MIPs, and AuNPs@NIPs. ................................................................................ 65 Figure 3.3 (a) Representative Raman spectra of AuNPs, AuNPs@SiO2, the molecularly imprinted SERS substrate (AuNPs@MIPs) with 4-ATP as internal standard, and 4-ATP normal Raman. (b) SERS spectra of 4-ATP obtained at 10 random sites using AuNPs@MIPs as the substrate. ....................................................................................................................................... 68 xv Figure 3.4 Characterization of the adsorption capacities of AuNPs@MIPs and AuNPs@NIPs towards 2,4-D. ............................................................................................................................... 70 Figure 3.5 3D-FDTD simulation results for the electric field intensity (|E/E0|2) distributions calculated at the excitation wavelength of 785 nm for AuNPs@MIPs with different gap distances: 0-10 nm (a-f). The diameter of AuNPs is 33.6 nm and the thickness of the MIPs shell is 6.5 nm. ....................................................................................................................................... 72 Figure 3.6 (a) Representative Raman spectra of AuNPs@MIPs (black), 2,4-D normal Raman (green), as well as SERS spectra of 2,4-D standard solution (10 µg/mL) (red) and 2,4-D (30 µg/mL) spiked milk sample (blue). (b-e) Reproducibility of the AuNPs@MIPs SERS substrate for 2,4-D detection. (b) Spectra of 10 µg/mL 2,4-D standard solution obtained at 10 different sites in three droplets. Normalized Raman intensities at 594 cm-1 (c), 1175 cm-1 (d), and 1280 cm-1 (e) collected from (b). (f) Representative normalized SERS spectra of 2,4-D standard solutions at different concentrations using AuNPs@MIPs as the substrate. ................................ 76 Figure 3.7 Original Raman intensities at 594 cm-1 (a), 1175 cm-1 (b), and 1280 cm-1 (c) collected from Figure 4b without peak normalization. ................................................................................ 78 Figure 3.8 Normalized peak intensity-concentration plot of the SERS spectra at 1175 cm-1, 1280 cm-1, and 1585 cm-1 (n=5). ............................................................................................................ 79 Figure 3.9 (a) Representative PCA score plot of 2,4-D standard solutions and 2,4-D spiked milk samples. For each concentration (represented by dots of the same color, black: 0 µg/mL; red: 0.1 µg/mL; green: 1 µg/mL; blue: 10 µg/mL; orange: 30 µg/mL; solid dot: standard sample; circle: milk sample), the 95% confidence ellipsoid is also shown. (b) Representative calibrated PLSR model for the prediction of 2,4-D concentrations using SERS spectral features (Raman shift range: 1020-1800 cm-1). ................................................................................................................ 81 xvi Figure 4.1 (a) Schematic illustration of the proposed inertial microfluidic device for separation of enrofloxacin using QDs@MIPs particles. Particles that flow through the sprial microchannel experience net lift force (FL) and Dean drag force (FD), resulting in equilibrating particles at different positions along the inner wall of the microchannel depending on the particle sizes. Outer inlet 1: sample inlet; outer inlet 2: QDs@MIPs suspension inlet; inner inlet: buffer solution inlet. (b) Microchannel cross-sections illustrating the principle of inertial separation for microparticles. Randomly dispersed particles align in the four equilibrium positions within the microchannel where the lift forces balance each other. The addition of Dean force reduce the four equilibrium postions to two vertically aligned positions (viewed as a single stream across the microchannel) near the inner wall of the microchannel. ......................................................... 98 Figure 4.2 (a) Experimental confirmation of two layers of fluids that flow through the spiral microchannel in parallel and come out from two outlets without mixing. (b) Representative images of silica spheres collected from the inner and outer outlets. .......................................... 100 Figure 4.3 (a) Schematic illustration for the preparation of micron-size QDs@MIPs particles and the sensing mechanism for enrofloxacin detection. (b) Images of QDs@MIPs and QDs@NIPs under UV light before and after the removal of template molecules. ......................................... 102 Figure 4.4 Fluorescence microscopy analysis of silica spheres, QDs@MIPs, and QDs@NIPs. Left to right: differential interference contrast (DIC), fluorescence (QDs, emission at 630 nm), and merged microscopic images. ................................................................................................ 103 Figure 4.5 Characterization of the adsorption capacities (Q) of QDs@MIPs and QDs@NIPs towards enrofloxacin. .................................................................................................................. 104 xvii Figure 4.6 Representative fluorescence emission spectra of (a) QDs@MIPs and (b) QDs@NIPs with the addition of enrofloxacin standard solutions with different concentrations (0 - 50 µg/mL). (c) The Stern-Volmer plots for QDs@MIPs and QDs@NIPs. (n=4) ......................................... 106 Figure 4.7 Fluorescence quenching amounts of QDs@MIPs after passing through the spiral microchannel. Enrofloxacin standard solutions or enrofloxacin spiked swine muscle sample extractions were injected for MIPs adsorption and fluorescence quenching. (n=5) ................... 108 xviii List of Abbreviations 2,4-D 2,4-dichlorophenoxyacetic acid 3D-FDTD Three-dimensional finite difference time domain 4-ATP 4-aminothiophenol ACN Acetonitrile AgNPs Sliver nanoparticles AIBN 2,2’-azobis(isobutyronitrile) APTES (3-aminopropyl)trimethoxysilane AuNPs Gold nanoparticles BB Borate buffer CCD Charge-coupled device CDC the Centers for Disease Control and Prevention CEC Capillary electrochromatography CFIA the Canadian Food Inspection Agency CN Cellulose nitrate CPF Chlorpyrifos CTAB Hexadecyltrimethylammonium bromide DAD Photodiode array detector DIC Differential interference contrast DMF Dimethylformamide EGDMA Ethylene glycol dimethacrylate xix EPA the United States Environmental Protection Agency FDA the Food and Drug Administration FLD Fluorescence detector GC Gas chromatography GSH L-Glutathione reduced HCA Hierarchical clustering analysis IR Infrared LC Liquid chromatography LOD Limit of detection LSPR Localized surface plasmons MAA Methacrylic acid MIPs Molecularly imprinted polymers MISPE Molecularly imprinted solid phase extraction MRL Maximum residue limit MS Mass spectrometry NC Nitrocellulose NIPs Non-imprinted polymers NISPE Non-imprinted solid phase extraction PCA Principal component analysis PDMS Polydimethylsiloxane elastomer PLSR Partial least squares regression PML Perfectly matched layer xx PTFE Polytetrafluoroethylene PVP Polyvinylpyrrolidone QDs Quantum dots QuEChERS Quick, Easy, Cheap, Effective, Rugged and Safe Re Reynolds number RMSE Root mean squares error RMSEC Root mean squares error of calibration. RMSECV Root mean squares error of cross-validation RMSEP Root mean squares error of prediction RSD Relative standard deviation SERS Surface enhanced Raman spectroscopy SHINERS Shell-isolated nanoparticle-enhanced Raman spectroscopy SPE Solid phase extraction SPME Solid phase microextraction SPR Surface plasmon resonance TEM Transmission electron microscope TEOS Tetraethoxysilane TLC Thin layer chromatography TMAH Tetramethylammonium hydroxide TMOS Tetramethoxysilane TFSF Total-field scattered-field xxi USDA the United States Department of Agriculture UV Ultraviolet WHO World Health Organization xxii Acknowledgements Firstly, I would like to express my sincere gratitude to my supervisor, Dr. Xiaonan Lu, for his invaluable mentorship, patience, encouragement, and support throughout the time of my Ph.D. study. Dr. Lu’s enthusiastic and prudent attitude towards research are both admirable and inspiring, which also set an example for me to be a good scientist. Besides, I also would like to express my appreciation to Dr. Lu for his selfless help and financial support during my Ph.D. study. Secondly, I would like to thank my co-supervisor Dr. Christine Scaman, my committee members Dr. Keng C Chou, Dr. Russ Algar and Dr. Eunice Li-Chan for their dedication both in time and knowledge. I also want to thank Dr. Jie Xu, who has provided professional advices and selfless help during my study in his laboratory at University of Illinois at Chicago. I want to thank all my lab mates and friends at UBC, with whom we spent lots of happy moments. Particularly, I would like to thank Jinsong Feng and Yaxi Hu for their company, help, inspiration and encouragement during the past several years. I also want to thank Dr. Lina Ma for her help in the laboratory as well as in life. Last but not least, the most special thank you goes to my families. Thank my parents for their endless love and support. Thank my younger brother for taking care of my parents during I studied abroad. I also want to express my sincere appreciation to my aunt, Kuixia Liu, who regards me as her son and provided me endless love and support. xxiii Dedication I dedicate this dissertation to my family for their endless love and support. 1 Chapter 1: Introduction 1.1 Food safety and food chemical hazards Foodborne illnesses are commonly caused by hazardous residues in foods and have posed great burdens on public health and resulted in significant cost to agri-food industry and healthcare. Consumption of contaminated foods can cause over 200 diseases, including fatigue, chills, mild fever, vertigo, upset stomach, dehydration caused by diarrhea, severe cramps, and in some cases even death (Uçar, Yilmaz, & Çakiroglu, 2016). According to a most recent report from World Health Organization (WHO), it is estimated that about 600 million people in the word fall ill after eating contaminated foods and 420,000 die every year, resulting in a cost of about US$ 110 billion in productivity and medical expense (Group, 2015). Even for the United States that has one of the world’s best food safety systems, an estimation of ~48 million people get sick due to foodborne diseases along with 128,000 hospitalization and 3,000 deaths each year was reported by the Centers for Disease Control and Prevention (CDC) (States, 2018). As the main causes of foodborne illnesses, food hazards enter the food supply chain at any time during harvesting, processing, packaging, transportation, storage, preparation, and serving (Agency, 2017). Based on the nature of food hazards, they are generally classified as four primary categories, namely biological, chemical, physical and allergenic hazards. Among them, biological hazards (bacteria, viruses and parasites) are responsible for most reported foodborne outbreaks (Bélanger P, Tanguay F, Hamel M, & Phypers M, 2015; Team, 2018). Nevertheless, chemical hazards usually cause more serious health problems such as disrupt body metabolism, cause cancers, damage genes, alter organ functions, and affect reproduction and development, 2 which are different from the short period illness symptoms (e.g., gastroenteritis) caused by biological hazards (Health Canada, 2014). For example, aflatoxins can increase the risk of liver cancer and mercury can affect the development of brains in fetuses, infants and children for a lifetime (J. D. Park & Zheng, 2012; Ratnaseelan, Tsilioni, & Theoharides, 2018). According to the origins of chemical contaminants present in foods, food chemical hazards are commonly divided into five categories (Anonymous, 2013): • Pesticides and veterinary drug residues: resulting from application of pesticides (insecticides, herbicides, and fungicides) to kill, repel, or control certain forms of plant or pest, and veterinary drugs (e.g., antibiotics, hormones) to control and/or prevent diseases in the animal. • Natural toxins: include mycotoxins produced by fungi (e.g., aflatoxins, deoxynivalenol, ochratoxin A, fumonisins, etc.), marine toxins (e.g., domoic acid, brevetoxin, etc.) accumulated in fish and shellfish, and some other toxins existed in food commodities. • Natural and environmental contaminants: chemicals that accidentally or deliberately enter the environment. Examples of environmental contaminants include arsenic, mercury, cadmium, lead, bromates, dioxins, furans and polychlorinated biphenols (PCBs). • Deliberately added contaminants: chemical substances added into food products during manufacture to cause harms to consumers, such as Sudan-I and melamine. • Process and storage-derived contaminants: undesirable chemicals formed during processing or storage as a result of reactions between compounds that are natural components of the food. Examples of chemical contaminants formed during food processing include acrylamide, ethyl carbamate, furan, etc. 3 Keeping food safe is the key to prevent foodborne illnesses. Well established food quality and safety regulation systems and reliable analytical methods are essential for protecting consumers from food contaminants. Maximum residue limits (MRLs) are standards developed by both international (e.g., the Codex Alimentarius Commission) and national [e.g., the Food and Drug Administration (FDA), the United States Department of Agriculture (USDA), Health Canada and the Canadian Food Inspection Agency (CFIA)] authorities to regulate the amounts of chemical contaminants likely to occur in food matrices ((USDA), 2020; (WHO), 2018 ; Canada, 2020). Although an MRL does not mean a toxicological safety threshold, it indicates the maximum level of chemical residue that is legally allowed in raw crops or foods. On the other hand, sensitive and accurate determination of chemical contaminants in foods is also vital to prevent the distribution of contaminated foods, reduce the risk of widespread infections, and decrease economic loss caused by foodborne outbreaks. 1.2 Traditional methods for determination of food chemical hazards As most known chemical contaminants in foods are small organic molecules and present at low concentrations, sensitive and accurate analysis of specific chemical contaminant(s) in food matrices can be very challenging. Given the complex nature of food matrices, quantification of molecules of interest in food usually consists procedures of sample pretreatment, analytes separation and detection. Among them, sample pretreatment is essential to assure satisfactory precision and accuracy of chemical analysis. Traditional methods, such as liquid-liquid extraction and liquid-solid extraction, are commonly applied to remove interferents (e.g., lipids and proteins), preconcentrate analytes, and convert the analytes to suitable form for detection. However, most sample pretreatment methods applied for food chemical analysis still lack 4 specificity towards different detection targets, time-consuming, and may not able to assure quantification accuracy (Kataoka, Lord, & Pawliszyn, 2000). Although some improved methods, such as QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method (Musarurwa, Chimuka, Pakade, & Tavengwa, 2019), solid phase extraction (SPE) (Otles & Kartal, 2016), solid-phase microextraction (SPME) (Kataoka et al., 2000), etc. have been introduced, they aim to improve the overall separation efficiency but fail to selectively recognize and separate the analytes. Currently, most of the routine food chemical contamination analyses are conducted using chromatography-based methods [e.g., liquid chromatography (LC) and gas chromatography (GC)]. To ensure the detection accuracy, laborious and time-consuming sample preparation and extraction procedures are inevitable before performing the precise separation of chemical contaminants on LC or GC columns. Detection tools that integrated with chromatography-based methods include photodiode array detector (DAD), fluorescence detector (FLD), mass spectrometry (MS), etc. However, conventional detection tools, such as DAD and FLD, may neither be sensitive enough to meet requirement of MRLs nor differentiate structural analogues. LC-MS or GC-MS, as advanced analytical technologies, were widely investigated in recent years for the determination of chemical contaminants in foods (H. Song & Liu, 2018; Stachniuk & Fornal, 2016). For example, Radišić and colleagues analyzed 11 pesticides in fruit juices using LC-MS and reported limits of detection (LODs) in the range of 0.01-0.94ng/mL (Radišić, Grujić, Vasiljević, & Laušević, 2009). Which indicates excellent sensitivity of MS for detecting chemical contaminants in foods at trace levels. However, in-field detection of chemical contamination using chromatographic-based technique is not feasible. Besides, the analysis 5 process is also very labor-intensive, time-consuming, consume large volume organic solvent, and the expensive and sophisticated instruments demand highly trained personnel to operate. In recent years, various antibody-based immunoassays and enzymatic biosensors have been developed for the detection of food chemical hazards and some other contaminations. Based on the working mechanism of either antibody-antigen reactions or reactions catalyzed by enzyme, selective and sensitive determination of chemical contaminants in foods can be achieved (Nikoleli, Nikolelis, Siontorou, Karapetis, & Varzakas, 2018; F. Xu et al., 2015). For example, a dipstick immunoassay was developed by Zhao and coauthors to detect carbofuran residue in vegetables with a LOD of 20 ng/mL (Y. Zhao et al., 2019). Besides, immunoassays are easy to use and can be applied without any instrument, which make them very ideal for in-field detection. However, most of the immunoassays focus on detection of targets with relatively large molecular weights (e.g., mycotoxins). For chemicals with smaller molecular weights (e.g., pesticides), immunoassays may not be applicable due to the lack of effective recognition sites. Besides, cumbersome sample pretreatment procedures are also inevitable to ensure the detection accuracy of immunoassays and enzymatic biosensors. Therefore, development of simple, rapid, user-friendly, and cost-effective techniques for selective, sensitive, accurate, and efficient determination of trace level chemical contaminants in food is of significant importance. 6 1.3 Innovative techniques for determination of food chemical hazards 1.3.1 Molecularly imprinted polymers (MIPs) Molecular recognition is important in many areas such as assessment of reaction process, detection of drugs, analysis in foods and environment. Commonly used recognition elements include enzymes, antibodies and aptamers (Haupt & Mosbach, 2000). However, these biological compounds suffer from poor chemical and physical stability and are not suitable for applications in harsh environments. Molecularly imprinted polymers (MIPs), also known as “artificial antibodies”, are polymers synthesized using molecular imprinting technique to create tailor-made binding sites with the memory of size, shape, and functional groups of template molecules (BelBruno, 2019; L. Chen, Wang, Lu, Wu, & Li, 2016; L. Chen, Xu, & Li, 2011; X. L. Song, Xu, Chen, Wei, & Xiong, 2014). Because of their specific affinity to targeted molecules as well as high physical and chemical stability at extreme conditions (e.g., extreme temperature or pH), MIPs have drawn increasing interests to researchers in various fields. Generally, MIPs are synthesized by copolymerizing functional monomers and cross-linkers in the presence of template molecules. As illustrated in Figure 1.1, templates (analyte molecules) firstly interact with functional monomers via covalent or non-covalent association in a suitable solvent. Then, copolymerization of functional monomers and cross-linkers is conducted under the catalysis of initiator. After removal of template molecules from the polymers, highly cross-linked MIPs materials are formed with specific recognition cavities complementary to the templates in terms of shape, size and chemical functionality. Consequently, a molecular memory is introduced in MIPs materials, allowing selective recognition and binding of targeted molecules in the imprinted sites, similar to the reaction between an antibody and its antigen. Hence, MIPs 7 materials can be used to rebind, separate and concentrate targeted analytes form complex biological and/or food matrices. Figure 1.1 Schematic illustration of the principle and process of developing molecularly imprinted polymers (Shaolong Feng & Lu, 2015). 8 Based on the methods applied to form molecular imprinting between functional monomers and imprinted molecules (i.e., templates), MIPs are normally categorized into two types: the covalent imprinting-based MIPs proposed by Wulff (Wulff, 1995) and noncovalent imprinting-based MIPs proposed by Mosbach (Mosbach, 1994). Although MIPs synthesized based on covalent imprinting normally exhibit excellent selectivity and binding affinity to template molecules and possess homogeneous binding sites, the rigorous polymerization conditions and strong nature of covalent reactions make it challenging to successfully develop MIPs using covalent imprinting method (Spivak, 2005). To deal with these problems, noncovalent imprinting is commonly adopted for MIPs development. Because noncovalent imprinting-based MIPs bind templates via hydrogen bonding, electrostatic interactions, van der Waals forces, π–π interactions, etc., which are not as strong as covalent bonds, the reactions between template molecules and functional monomers are easy to beak and regenerate, resulting in more popular use in real applications (L. Chen et al., 2011). 1.3.1.1 Preparation methods of MIPs Generally, MIPs materials are prepared based on the mechanism of either free-radical polymerization or sol-gel polymerization (L. Chen et al., 2016). Bulk polymerization is the most widely used free-radical polymerization method to prepare MIPs due to its simplicity in preparation and require no sophisticated instrumentation. However, the obtained monolithic polymer has to be ground and sieved to fine particles, which is labor-intensive and time-consuming. Besides, the shape and size of MIPs synthesized using bulk polymerization method are irregular and binding sites are heterogeneously distributed (Pratiwi, Megantara, Rahayu, Pitaloka, & Hasanah, 2018). To overcome the drawbacks of bulk polymerization, alternative 9 polymerization strategies have been proposed. Precipitation polymerization is a method developed on the basis of bulk polymerization (Beltran, Marcé, Cormack, & Borrull, 2009). By adding a large amount of organic solvent, polymeric chains grow individually without coalescence and finally precipitate as uniform sized MIPs particles. However, precipitation polymerization method also suffers from the drawback of large amount of organic solvent consumption and the reaction conditions need strict controls (e.g., polarity of solvents, temperature) to obtain successful MIPs materials (Beltran, Marce, Cormack, & Borrull, 2009; Y. Liu, Hoshina, & Haginaka, 2010). Suspension polymerization and emulsion polymerization have also been proposed to synthesize MIPs particles using water as the continuous phase, or in either oil-in-water or water-in-oil dispersing medium, respectively (Refaat et al., 2019; Walsh, Osmani, Hughes, Duggan, & McLoughlin, 2011; Z. H. Wang et al., 2018; T. Zhou, Zhang, Kamra, Bulow, & Ye, 2015). These two polymerization methods can synthesize spherical MIPs materials with a high yield. However, they also need a large amount of reagent consumption, and MIPs particles synthesized using these two methods tend to have a broad size distribution and display poor affinities to the targeted molecules. In comparison to the free-radical polymerization methods, sol-gel polymerization method has drawn increasing interests for the preparation of MIPs due to its distinct advantages, such as ease of polymerization at room temperature, eco-friendly reaction solvents, controllable monodisperse particles, and excellent binding capacity. Generally, this type of MIPs materials is synthesized using tetraalkoxysilane [e.g., tetraethoxysilane (TEOS) and tetramethoxysilane (TMOS)] as the precursor to form a colloidal solution (a sol), followed by poly-condensing the mixture in the presence of templates and functional monomers [e.g., (3-Aminopropyl)triethoxysilane (APTES)] 10 to form highly cross-linked silica particles (the gels) (Lofgreen & Ozin, 2014; Moein, Abdel-Rehim, & Abdel-Rehim, 2019). In the meantime, sol-gel polymerization method is also feasible to prepare MIPs particles with mesoporous (Croissant, Fatieiev, Almalik, & Khashab, 2018; S. Xu & Lu, 2015; Z. Zhang, Li, Wang, Shen, & Chen, 2015) or core/shell structures (J. M. Liu, Wei, Liu, Fang, & Wang, 2017; M. Liu et al., 2018; Qian, Fang, & Wang, 2013; Tian et al., 2015; Y. C. Wang et al., 2015) to improve their binding capacity or achieve specific functions. However, due to the very limited choices for functional monomers (normally is a silane with specific functional group), the application of sol-gel polymerization method for MIPs preparation has been restricted and only the template molecules with specific functional group(s) can be adopted. The typical MIPs preparation methods are summarized and compared in Table 1.1. Table 1.1 Comparison of different molecular imprinting methods for the preparation of MIPs. Polymerization method Advantage Disadvantage Free radical polymerization Bulk polymerization -Various choices for functional monomers and crosslinkers -Can be applied to synthesize MIPs for various templates -No requirement for sophisticated or expensive instruments - Easy preparation -Need to be ground to small particles -Binding sites can be destroyed during the grinding procedure -Particles with big size and irregular shape -Low binding capacity Precipitation polymerization -Similar reagents used as bulk polymerization -Spherical particles with controllable size (a few to a few hundred nanometers) -Very uniform size distribution -Large amount of reagent consumption 11 -Relatively high binding capacity -Easy preparation Suspension -Spherical particles -Can use water as the continuous phase -Complex polymerization system -Broad size distribution (a few to several hundred micrometers) -Suffer the remnants of surfactants -Poor recognition -Large amount of reagent consumption Emulsion -High yield -Monodispersed particles -Water-soluble polymers - Complex polymerization system - Suffer the remnants of surfactants -Low imprinting capacity - Large amount of reagent consumption Sol-gel polymerization -Ease of fabrication at room temperature -Controllable particles (solid or core/shell particles) with uniform size distribution -High binding capacity -Limited choices for functional monomers and crosslinkers 1.3.1.2 Applications of MIPs in food analysis Taking advantage of the selective affinity of MIPs to its template molecules, MIPs materials have been synthesized and applied in a variety of fields, such as drug delivery (Zaidi, 2020), chromatographic separation (X. Huang, Zou, Chen, Luo, & Kong, 2003), purification of biological and chemical reagents (Chapuis, Mullot, Pichon, Tuffal, & Hennion, 2006), and analysis in environment and foods (Mattiasson, 2015). In food analysis, MIPs are generally applied as separation materials to selectively recognize, separate, and concentrate trace level analytes from complex food matrices. For example, MIPs have been widely used as adsorbent 12 materials for solid phase extraction (SPE), called molecularly imprinted solid phase extraction (MISPE), in either off-line (Qian, Fang, He, Pan, & Wang, 2010; X. Shi et al., 2011; Y. T. Wu et al., 2014) or on-line (Z. J. Duan, Fan, Fang, Yi, & Wang, 2011; Junping, Mingfei, Guozhen, & Shuo, 2009; C. Zhao, Zhao, Liu, & Zhang, 2010) modes. Compared to traditional adsorbent materials packaged in SPE columns, the selective affinity of MIPs towards analytes can contribute to higher efficiencies for sample matrix clean-up and analyte extraction. Extracts from MISPE can be detected by various analytical tools, such as high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS) (H. H. Yang et al., 2009) and Raman spectroscopy (S. Feng et al., 2013). Besides, MIPs have been applied as stationary phases in chromatographic techniques, such as HPLC (Jia, Qin, He, & Li, 2012), thin layer chromatography (TLC) (F. Gao, Hu, et al., 2015), and capillary electrochromatography (CEC) (Jia et al., 2012) for selective analysis of chemical compounds. Moreover, MIPs are also employed as specific recognition elements to couple with different sensing elements as novel chemosensors, namely MIPs-based sensors, for selective determination of chemical analytes in foods. The characteristic feature of MIPs-based sensors is that MIPs can selectively bind target analytes and introduce transduction to the sensing elements so as to generate specific output signals for detection. Typical MIPs-based sensors are classified as electrochemical sensors (Ahmad, Bedwell, Esen, Garcia-Cruz, & Piletsky, 2019; Lakshmi et al., 2009), optical sensors (X. Guo et al., 2020; H. Liu, Zhou, Wu, Wang, & Sun, 2016; Mehrzad-Samarin, Faridbod, Dezfuli, & Ganjali, 2017; S. Xu et al., 2013), and piezoelectric sensors (Pan et al., 2018; Pietrzyk et al., 2010) according to the transduction mechanisms. Among them, surface enhanced Raman spectroscopy (SERS)-based optical sensors and quantum dots (QDs)-based fluorescence sensors 13 have shown distinct advantages for rapid, selective, and sensitive determination of chemical contaminants in foods. 1.3.2 Spectroscopic analytical techniques 1.3.2.1 Infrared (IR) and Raman spectroscopy Vibrational spectroscopies include both IR and Raman spectroscopies can provide structural and chemical compositional information about molecules on the basis of their vibrational transitions. Apart from the difference between light sources that commonly used in IR and Raman spectroscopy for sample radiation (IR spectroscopy uses infrared light, Raman spectroscopy uses a monochromatic visible or near infrared light from a laser), the molecular vibration information obtained from these two spectroscopic methods are not exactly the same. Specifically, IR spectroscopy detects vibrations caused by electrical dipole moment changes (Alishahi, Farahmand, Prieto, & Cozzolino, 2010; Osborne, 2006). In comparison, Raman spectroscopy detects vibrations due to changes in polarizability (A. P. Craig, Franca, & Irudayaraj, 2013; Zhong Zhang, 2017), indicating that bonds that connect two identical or nearly identical parts of a molecule (e.g., C=C) tend to be more active in Raman spectroscopy. Compared to IR spectroscopy, Raman spectroscopy has the advantages of providing more structural and chemical information, higher resolution and the possibility for confocal measurement (Thygesen, Lokke, Micklander, & Engelsen, 2003). Hence, increasing attentions have been focusing on the application of Raman spectroscopy in food chemical analysis. However, normal Raman spectroscopy has its inherent drawback for trace level chemical analysis. Due to the small percentage of inelastic Raman scattering cross-section, the signals of trace amount chemical analytes are faint to be detected (A. P. Craig et al., 2013; Nie & Emory, 1997). As a result, 14 various approaches have been investigated to improve the detection sensitivity of Raman spectroscopy. 1.3.2.2 Surface enhanced Raman spectroscopy (SERS) Surface enhanced Raman spectroscopy (SERS) is one of the widely investigated techniques owing to its significant enhancement of faint Raman scattering signals. Raman scattering signal enhancement is achieved in the presence of noble metallic (e.g., gold and silver) nanostructures. Two mechanisms have been proposed to explain the enhancement effect of SERS, namely electromagnetic enhancement and chemical enhancement (Ding, You, Tian, & Moskovits, 2017; Haynes, McFarland, & Van Duyne, 2005; Kambhampati, Child, Foster, & Campion, 1998; Stiles, Dieringer, Shah, & Van Duyne, 2008; Yaseen, Pu, & Sun, 2018; Zheng & He, 2014). Electromagnetic enhancement mechanism is widely accepted to be dominant in SERS. As illustrated in Figure 1.2, incident light excites the localized surface plasmon resonance (LSPR) surround nanostructures and induce a large electromagnetic field. Molecules adsorbed on or in the vicinity to nanostructures are located in electromagnetic field and can be excited to a virtual state and instantly release a scattered photon that resonates with the new electromagnetic field, therefore, the excited Raman scattering signals are significantly enhanced (Willets & Van Duyne, 2007). In this situation, both the incident light and the scattered light are enhanced by the electromagnetic field. It is reported that the electromagnetic enhancement mechanism can contribute an enhancement as high as 9 to 12 orders of magnitude (Perez-Jimenez, Lyu, Lu, Liu, & Ren, 2020). For chemical enhancement mechanism, the explanation of either chemical complexation, or charge transfer and charge-transfer resonance caused by molecular adsorption was proposed (Kambhampati et al., 1998; Perez-Jimenez et al., 2020). Chemical complexation 15 refers to the formation of adsorbate–substrate complex may lead to change in Raman polarizability of the adsorbed molecule. The charge transfer indicates the exchange of electrons between metallic nanoparticle and chemical molecule. Charge-transfer resonance occurs when the incident light wavelength matched the electronic transitions of the adsorbate–substrate complex. It is expected that the SERS enhancement caused by chemical mechanism is usually not higher than a factor of 103, which is much lower than the enhancement cause by the electromagnetic mechanism (Perez-Jimenez et al., 2020). Figure 1.2 Schematic illustration of the excitation of the localized surface plasmon resonance (LSPR) of a spherical nanoparticle by incident electromagnetic field. 1.3.2.3 Applications of SERS in food chemical analysis On considering the two types of SERS enhancement mechanisms, the overall enhancement factor of SERS can be as high as 1015, indicating that single molecule could be detected using this technique (Kneipp et al., 1997). Hence, SERS is considered as an excellent analytical technique for trace level chemical analysis. Recently, various studies have been conducted to 16 detect chemical contaminants in foods using SERS. For example, Hou and coworkers using SERS for in situ detection of four insecticides on plant surfaces (Hou, Pang, & He, 2015). Without necessary sample pretreatment procedure, LODs of 0.01, 0.01, 0.02, 0.02 µg/g were achieved for isocarbophos, phorate, imidacloprid and deltamethrin on apple peels, respectively. Pham and colleagues developed a silver dendrite- coated SERS-active optical fiber substrate to detect permethrin pesticide with a LOD of 0.0035 µg/mL (Pham et al., 2019). Although SERS has been validated to be a versatile analytical technique for the detection of trace level chemical compounds, the impurity of samples can tremendously affect the performance of SERS, resulting in challenges in spectral deconvolution. Hence, efficient separation methods should be applied to couple with SERS for selective determination of chemical compounds in complicated matrices (e.g., foods). Numerous studies have been conducted to integrate SERS with recognition elements, such as antibodies (L. He et al., 2011) and aptamers (L. L. He et al., 2011; Pang, Labuza, & He, 2014), so as to selectively separate and detect chemical contaminants in complex food matrices. He and coauthors successfully applied aptamer as the recognition element to selectively separate and sensitively detect ricin B with LODs of 10 ng/mL in phosphate buffered saline (PBS), 50 ng/mL in orange juice, and 100 ng/mL in milk, respectively (L. L. He et al., 2011). Zhang and colleagues applied a multiplex SERS-based lateral flow immunosensor to determine six major mycotoxins in maize (W. Zhang et al., 2020). Under properly optimized experimental conditions, the LODs for aflatoxin B1, zearalenone, fumonisin B1, deoxynivalenol, ochratoxin A, and T-2 toxin were 0.96 pg/mL, 6.2 pg/mL, 0.26 ng/mL, 0.11 ng/mL, 15.7 pg/mL, and 8.6 pg/mL, respectively. Recently, our group developed several MIPs-SERS sensors for rapid and accurate screening and detection of different chemical contaminants 17 in various food products (F. Gao et al., 2014; F. Gao, Grant, & Lu, 2015; F. Gao, Hu, et al., 2015; Y. Hu et al., 2015). These MIPs-SERS sensors showed many advantages over traditional detection methods, such as high selectivity, high sensitivity, and short time for detection. However, both the sensitivity and reproducibility of the MIPs-SERS sensor need to be further improved. To interpret the spectral information obtained from SERS in a convenient manner, chemometric models are generally employed. Based on multivariate statistical analysis, chemometrics treat each Raman shift of the spectra as one variable and use different algorithms to achieve classification and/or quantification analysis (X. Lu & Rasco, 2012). Commonly used chemometric models include unsupervised principal component analysis (PCA) and hierarchical clustering analysis (HCA) for spectra classification and supervised partial least squares regression (PLSR) for quantification analysis (Kumar, Bansal, Sarma, & Rawal, 2014). Appropriate selection of chemometric models is essential to achieve selective and quantitative determination of chemical hazards in foods. 1.3.3 Quantum dots (QDs)-based fluorescence sensors Quantum dots (QDs) are semiconductor nanocrystals composed of groups II−VI (e.g., CdSe, CdTe, CdS and ZnSe) or III−V (e.g., InP and InAs) elements with interesting quantum confinement effects and some size-dependent fluorescence emission properties (Hildebrandt et al., 2017). In comparison to conventional fluorescent organic dyes, QDs present distinct advantages, such as narrow and size-tunable emission spectra, broad excitation, bright photoluminescence, high resistance to photobleaching, high quantum yield, and large Stokes 18 shift (Resch-Genger, Grabolle, Cavaliere-Jaricot, Nitschke, & Nann, 2008; Wolfbeis, 2015). Taking advantage of these characteristic features, QDs have been widely applied as fluorescent probes for bioimaging (Michalet et al., 2005; Rao, Dragulescu-Andrasi, & Yao, 2007; Smith, Duan, Mohs, & Nie, 2008), labelling (Parak, Pellegrino, & Plank, 2005) and biosensing (Medintz & Mattoussi, 2009; Medintz, Uyeda, Goldman, & Mattoussi, 2005). In food analysis, QDs have been explored as fluorescence labels in various assays to determinate different foodborne pathogens, such as Escherichia coli O157:H7, Salmonella enterica serotype Typhimurium (Du, Wang, Yang, & Wu, 2021; L. Yang & Li, 2006), and Listeria monocytogenes (Tully, Hearty, Leonard, & O'Kennedy, 2006). Different biological recognition elements, such as antibodies (L. Yang & Li, 2006), oligonucleotides (A. Huang et al., 2014), and aptamers (Algar, Tavares, & Krull, 2010; N. Duan et al., 2015), are investigated to combine with QDs for selective recognition and detection of foodborne pathogens. Compared to conventional immunoassays, QDs-based fluorescence assays exhibit higher sensitivities and more reliable quantitative results for foodborne pathogen detections. For the application of QDs in food chemical analysis, similar recognition elements (e.g., antibodies, oligonucleotides and aptamers) are also applied to develop QDs-based fluorescence sensors (J. W. Zhou, Zou, Song, & Chen, 2018). Because most targeted chemical compounds should be first extracted from complex food matrices using organic solvents, the stability of biological recognition elements in organic solvents remain big challenges for direct separation and analysis of chemicals. Recently, a variety of studies were proposed using MIPs as the recognition elements to integrate with QDs as fluorescence sensors for food chemical analysis (Chantada-Vazquez et al., 2016; H. B. Li & Qu, 2007; H. Liu, Fang, & Wang, 2014; H. Liu et 19 al., 2013; Ren & Chen, 2015; S. Xu & Lu, 2015; S. Xu et al., 2013; Y. Yang, Wang, Niu, & Zhang, 2016). The mechanisms of using QDs-MIPs sensors for determination of chemical compounds have been attributed to either electron (H. Liu et al., 2014) or energy (S. Xu et al., 2013) transfer-based fluorescence quenching effects (as illustrated in Figure 1.3). Taking advantages of specific recognition feature of MIPs materials and excellent optical properties of QDs, the developed QDs-MIPs sensors can selectively and sensitively detect chemical hazards in foods with limits of detection (LOD) as low as parts per billion (ppb) or even sub ppb level in some specific studies (G. Liu et al., 2019; J. W. Zhou et al., 2018). Figure 1.3 Schematic illustration of the principle of QDs@MIPs fluorescence sensor. 20 1.4 Microfluidic “lab-on-a-chip” technology Microfluidic chips are small devices designed and fabricated to manipulate the behaviors of small volume fluids and achieve various desired features (e.g., micro separation, detection, electrophoresis, DNA analysis, etc.) (Haeberle & Zengerle, 2007; Mark, Haeberle, Roth, von Stetten, & Zengerle, 2010). Principles for the development of microfluidic devices involve the intersection of various disciplines, such as fluid mechanics, thermodynamics, material science, and electrostatics (Guo, Feng, Fang, Xu, & Lu, 2015). With miniaturized scales, microfluidic devices have been demonstrated as attractive analytical systems to scale-down and integrate all necessary processes implemented in the laboratory and realize biological and chemical analysis. Taking advantages of small sample and reagent requirement, simple operation, fast detection, and multi-functional integration, microfluidic chips have been applied in numerous research areas, such as biological and chemical analyses, molecular biology and biomedicine (Haeberle & Zengerle, 2007; Mark et al., 2010). Paper-based microfluidic device was first proposed by Whitesides et al. in 2006 (Whitesides, 2006) and has been widely investigated in various fields (e.g., diagnostic, biological and chemical analysis) due to its advantages over the traditional PDMS (polydimethylsiloxane elastomer)- and glass-based microfluidic devices (H. Gao, Yan, Wu, & Li, 2020) (Nishat, Jafry, Martinez, & Awan, 2021). However, the fluids that flow on paper microfluidic devices are less controllable and very limited functions can be achieved on such devices. In comparison, traditional PDMS-based microfluidic devices are more versatile to achieve highly complicated functions. Although many studies have been conducted to apply PDMS-based microfluidic devices for detection of foodborne pathogens (Azinheiro et al., 2020; Y. M. Park et al., 2021; 21 Safavieh, Ahmed, Tolba, & Zourob, 2012), studies of applying traditional PDMS-based microfluidic devices for determination of chemical contaminants in foods is still in its infancy. 1.5 Central hypothesis and objectives 1.5.1 Central hypothesis Based on the advantages and capacities of the aforementioned techniques, the central hypothesis of my PhD thesis is that selective MIPs materials synthesized using different polymerization methods can be applied to integrate with different detection tools as novel chemosensors to determinate food chemical contaminants in a rapid, selective, sensitive, and accurate manner. Traditional PDMS-based microfluidic device can be applied to further simplify the detection of food chemical hazards by integrating the processes of sample pretreatment, separation, and detection on a single chip. 1.5.2 Objectives The overall objective of this study is to take advantage of the selective recognition property of MIPs to combine with advanced analytical technique (i.e., SERS) or novel sensing material (i.e., QDs) as innovative chemosensors to detect trace level chemical contaminants in foods. The individual objectives are listed as below. Objective 1: To develop a MIPs-based SERS/colorimetric dual sensor for the determination of chlorpyrifos in apple juice. 22 Objective 2: To develop a MIPs-AuNPs incorporated dual functional nanomaterial for selective separation and sensitive determination of 2,4-D in milk. Objective 3: To develop a microfluidic-assisted QDs@MIPs fluorescence sensor for rapid, selective separation and sensitive determination of enrofloxacin in swine muscle. 23 Chapter 2: Development of molecularly imprinted polymers-surface-enhanced Raman spectroscopy/colorimetric dual sensor for determination of chlorpyrifos in apple juice 2.1 Introduction Chlorpyrifos (O,O-diethyl O-3,5,6-trichloropyridin-2-yl phosphorothioate, CPF) is one of the most widely used organophosphate pesticides that can effectively inhibit acetylcholinesterase and block the signals travel between nerve cells. This causes the malfunction of central nervous system of the pests and eventually kills the pests (Anirudhan & Alexander, 2013). CPF is commonly used in agriculture crops (e.g., corn, soybeans, fruits, etc.). According to the United States Environmental Protection Agency (EPA), CPF remains the most commonly used organophosphate pesticide for crops in the United States with an estimated annual consumption of 5-10 million pounds to be applied to over 50% apple crops during 2006-2012 (Sass, 2015). CPF is moderately toxic to humans and considered as a neurotoxin and endocrine disruptor, and it is especially harmful to pregnant women and infants (Anirudhan & Alexander, 2013; P. Wang et al., 2013). A recent study also reported an association between human exposure to CPF and the incidence of lung cancer (Lee et al., 2004). Considering its toxicity and enormous application in agriculture, effective detection of CPF residues in agri-food products is highly demanding. Traditional detection methods are based upon high cost instruments, such as high performance liquid chromatography coupled with photodiode array detector (HPLC-DAD) (Ma & Chen, 2013), liquid chromatography-mass spectrometry (LC-MS) (Armstrong, Dills, Yu, Yost, & Fenske, 2014), and gas chromatography-mass spectrometry (GC-MS) (Ranz, Maier, Motter, & 24 Lankmayr, 2008). However, all these methods are laborious, time-consuming, and require large amount of organic reagents. Therefore, novel, simple, low-cost, and accurate analytical methods are highly demanded. Silver nanoparticles exhibit intense colors due to the surface plasmon resonance (SPR) phenomenon (Y. Sun & Xia, 2003; Wei, Abtahi, & Vikesland, 2015). Under the irradiation of electromagnetic waves, the SPR effect will produce characteristic absorption peak, which depends upon the size and shape of the particles and the dielectric properties of surrounding medium and inter-particle distances (Mi, Wu, Zhong, & Huang, 2015). Many factors can promote the aggregation of nanoparticles and cause SPR band shift to a longer wavelength (N. Li et al., 2014). The interparticle distance changes and subsequently results in quantifiable red-shift or blue-shift of the UV-Vis absorption, which is the basic principle of colorimetric sensing system (J. Duan, Yang, Lai, Yuan, & Zhan, 2012). Due to the rapid, sensitive, and easy-operational features, colorimetric-based detection methods have been extensively investigated as different colorimeter sensors to analyze various chemical and biological molecules (Tharmaraj & Yang, 2014; Walekar et al., 2014; G. H. Wu et al., 2015; Yao, Zhou, Dai, Yue, & Xue, 2014). However, the poor selectivity of colorimetric sensor has limited its application in complex sample matrices (e.g., foods) because numerous interferents may cause the aggregation of nanoparticles. Many studies were conducted to improve the selectivity for AgNPs-based colorimetric sensors, such as the addition of different stabilizers. For example, Su and colleagues used folic acid functionalized silver nanoparticle-based colorimetric sensor to detect Hg2+ in a sensitive and selective manner (Su et al., 2014). In another study, Xiong and others modified AgNPs with calixarene to specifically detect pesticides in water (Xiong & Li, 2008). However, 25 the synthesis of these AgNPs was complicated and may affect the sensitivity of the colorimetric sensors because the stabilizers could also prohibit the aggregation of AgNPs. Therefore, cleaning up sample matrices and analyte enrichment are required before the analysis. Surface-enhanced Raman spectroscopy (SERS) has been investigated as a rapid and reliable method for the determination of trace level chemical compounds (Ellis et al., 2012; S. Feng et al., 2013; Y. Hu et al., 2015; Zheng & He, 2014) and microbiological agents (A. P. Craig et al., 2013; X. N. Lu, Al-Qadiri, Lin, & Rasco, 2011) in agricultural and food products. When the analyte molecules are in the proximity to the surface of the noble metallic (e.g., gold, silver) nanostructures, the localized SPR of the nanostructures will significantly enhance the faint Raman scattering signals derived from low concentrations of analyte molecules (Stiles et al., 2008). The enhancement factor of high quality SERS-active substrates for Raman active molecules (e.g., pyridine) can reach to 1014, corresponding to the detection of single molecules (R. L. Craig, Bondy, & Ault, 2015; Kneipp et al., 1997). Although SERS is an ideal tool for the detection of trace level analytes, it suffers from the interference of sample matrix components whose SERS signals may overlap with the signals of analytes, resulting in the challenges in spectral deconvolution and reduced detection sensitivity and accuracy. Consequently, effective removal of sample matrix components is necessary before SERS spectral collection. Extensive sample pre-treatments, such as extraction by organic solvents and centrifugation, are greatly involved before implementing the aforementioned colorimetric and SERS sensing techniques. However, these methods lack the selectivity and thus are not effective to fully eliminate the interferents. New approaches such as antibody-based (X. Li et al., 2015) and 26 aptamer-based (L. L. He et al., 2011) separation methods have been successfully developed to isolate and/or enrich chemical hazards from complicated food matrices and enable more sensitive and accurate detection using SERS. Nevertheless, high cost and laborious manufacture of antibody and aptamer for specific analytes significantly limit the application of these two methods in routine detection of chemical residues in foods. Recently, molecularly imprinted polymers (MIPs)-based SERS sensors were constructed to detect and quantify melamine (Y. Hu et al., 2015), histamine (F. Gao, Grant, et al., 2015), α-tocopherol (S. Feng et al., 2013), and Sudan I (F. Gao, Hu, et al., 2015) in different food products. MIPs are rigid polymers developed by chemical synthesis with high affinity towards target analyte. Thus, MIPs are resistant to harsh environment and more suitable to be used as separation element for sensing systems compared to antibody and aptamer (Liao & Lu, 2016). Recently, two different colorimetric-SERS dual sensors were developed to determinate mercuric ions in water samples for environmental analysis (J. Duan et al., 2012; Mi et al., 2015). However, the authors failed to determine heavy metal pollution in complicated food matrices. In another study, Lang and coauthors constructed a gold nanoparticle-based colorimetric-SERS dual sensor to detect melamine in milk. However, the selectivity of this sensor was poor and the sensitivity did not meet the regulation of maximum residue limit (Lang, Pang, & He, 2015). The current study aims to develop a MIPs-SERS/colorimetric dual sensor for the determination of CPF in apple juice. MIPs are synthesized to selectively separate CPF from apple juice while AgNPs are used both as color indicator and SERS-active substrate. The colorimetric sensor is developed for initial rapid high-throughput screening and semi-quantification of CPF in apple juice, followed by SERS to quantify trace level CPF contamination further accurately. To the 27 best of our knowledge, this was the first study to integrate colorimetric and SERS as a single sensing system for rapid and accurate detection and quantification of trace level chemical hazards in agri-food products. 2.2 Hypotheses H1: Specific MIPs material for CPF recognition can be synthesized using bulk polymerization method. H2: The MIPs material can be applied to selectively separate CPF from apple juice. H3: CPF can cause the aggregation of AgNPs and result in a colorimetric reaction which can be applied for the detection of CPF. H4: The aggregated AgNPs can be applied as SERS substrate for sensitive and quantitative determination of CPF in apple juice. 2.3 Objectives O1: To synthesize MIPs material with the selective affinity towards CPF using bulk polymerization method. O2: To develop a method based on the MIPs material for selective separating CPF from apple juice. O3: To develop a AgNPs-based colorimetric method for CPF detection. 28 O4: To quantitatively detect CPF based on spectral features of CPF using the aggregated AgNPs as SERS substrate. 2.4 Materials and methods 2.4.1 Chemicals and materials Methacrylic acid (MAA), ethylene glycol dimethacrylate (EGDMA), 2,2’-azobis(isobutyronitrile) (AIBN), chlorpyrifos (CPF), silver nitrite, sodium borohydride, hexane, ethanol, methanol, acetonitrile, acetic acid, and toluene were purchased from Sigma-Aldrich (St. Louis, MO, USA). All of the chemical agents are of at least reagent grade. MAA and EGDMA were purified by reduced pressure distillation before use. Cellulose nitrate (CN) membrane was purchased from Sartorius Stedim Biotech S.A. (Göttingen, Germany). Apple juice samples were obtained from local grocery stores in Vancouver, Canada. 2.4.2 Synthesis of MIPs The synthesis of CPF-MIPs was conducted according to the following procedure: 1 mmol of CPF was dissolved in 5 ml of toluene and mixed with 6 mmol of MAA. The mixture was stirred at room temperature for 30 min, followed by addition of 25 mmol of cross-linking agent EGDMA and 0.05 g of initiator AIBN into the mixture. The mixture was then purged with nitrogen for 10 min at room temperature, sealed, and thermal polymerized in water bath at 40°C for 2 hours, followed by adjusting the temperature to 60°C for another 16 hours. After the polymerization, the rigid polymer was crushed and passed through a 200 mesh steel sieve. Then, the template was removed by Soxhlet extraction with 200 mL of methanol/acetic acid (9:1, v/v) 29 for 24 hours, followed by extraction with 200 mL of pure methanol for another 24 hours. After examining with a UV-Vis spectrometer at 286 nm to validate the complete removal of CPF, the synthesized MIPs were dried overnight in a vacuum drying oven at 60°C. In comparison, non-imprinted polymers (NIPs) were also prepared following the same procedure in the absence of CPF. 2.4.3 Adsorption test The static and kinetic adsorption tests were conducted individually to determine the adsorption capacity and binding equilibrium rate of MIPs and NIPs. For static adsorption test, 15 mg of MIPs were mixed with 3 mL of CPF methanol solutions at different concentrations (i.e., 5, 10, 20, 30, 40, 60, 80, and 100 mg/L). The mixtures were incubated at room temperature with shaking for 2 hours, followed by centrifugation at 7,500 ×g for 10 min. The supernatants were measured by UV-Vis spectrometry at 286 nm for unbound CPF. This same procedure was also applied to test the static adsorption capacity of NIPs. The adsorption capacity was calculated using the method as reported in a previous work (Qian et al., 2010). For kinetic adsorption tests, 15 mg of MIPs or NIPs were mixed with 3 ml of 100 mg/L CPF methanol solution. The mixtures were incubated at room temperature with shaking for various time intervals (i.e., 5, 10, 20, 40, 60, 90, and 120 min), followed by centrifugation at 7,500 ×g for 10 min. The unbound CPF in supernatants were measured by UV-Vis spectrometry at 286 nm. 30 2.4.4 Pretreatment of apple juice The apple juice samples were first determined to be free of CPF by HPLC-DAD before use. After that, 5 mL of apple juice samples were individually spiked with different amounts of CPF (i.e., 0, 0.01, 0.1, 1, 5, 10, 20, and 30 mg/L). Then, 5 mL of hexane and 1.5 mL of ethanol were added and vortex-mixed for 30 s. The mixtures were centrifuged at 2,000 ×g for 1 min to form the hexane layer (top layer) and the aqueous layer (bottom layer). The top layer (5 mL) was collected and hexane was removed by reduced pressure rotary distillation. The residues were re-dissolved in 5 mL of 50% methanol (methanol/H2O, v/v). 2.4.5 Molecularly imprinted solid phase extraction (MISPE) Dried MIPs or NIPs (300 mg) were individually packed into a 3-mL polypropylene SPE cartridge with two TFE frits (Agilent, Santa Clara, CA, USA) at each end. The MISPE and non-imprinted solid phase extraction (NISPE) columns were conditioned sequentially with 2 mL of H2O and 2 mL of ethanol at a flow rate of 1 mL/min, followed by equilibration with 2 mL of 50% methanol (methanol/H2O, v/v). Then, 2 mL of apple juice samples spiked with different concentrations of CPF were individually loaded onto MISPE or NISPE columns at a flow rate of 1 mL/min. After washing with 2 mL of 60% methanol (methanol/H2O, v/v) at a flow rate of 1 mL/min, the columns were dried, and 2 mL of ethanol (100%) was used to elute CPF from the columns at a flow rate of 1 mL/min. The recovery of CPF from spiked apple juice was determined using HPLC-DAD at a wavelength of 286 nm. The eluted solutions were directly used for further experiments. 31 2.4.6 Colorimetric and UV-Vis spectroscopic determination of CPF AgNPs were synthesized by a simple chemical reduction method. Briefly, 4 mL of AgNO3 aqueous solution (1.0 mM) was dropwisely added into 10 mL of freshly prepared NaBH4 solution (2.0 mM) under vigorous stirring at 300 rpm for 20 min at room temperature until the color of the solution turned to yellow. The fresh synthesized AgNPs were immediately applied for the following experiments. The colorimetric determination of CPF in aqueous solution was performed at room temperature. The standard stock solutions of CPF at various concentrations (i.e., 0, 0.01, 0.1, 1, 5, 10, 20, and 30 mg/L) were prepared by dissolving CPF in ethanol. Typically, 100 µL of CPF ethanol solution was added into 400 µL of fresh prepared AgNPs solution. After gently shaking the mixtures at room temperature for 10 min, the color changes were observed by naked eyes and the UV-Vis absorption spectra (300-800 nm) were collected as well. The same procedures were also applied for spiked apple juice samples eluted from MISPE. 2.4.7 Raman spectroscopic instrumentation and SERS A confocal micro-Raman spectroscopic system coupled with a 300-mW line shape 785 nm near-infrared laser was used in this study. This system includes a Raman spectrometer (Renishaw, Gloucestershire, UK) and a Leica microscope (Leica Biosystems, Wetzlar, Germany). The spectrometer has an entrance aperture of 50 µm and a focal length of 300 mm and is equipped with a 1200-line mm-1 grating. The Rayleigh scattered light can be completely eliminated by the filters and only the Raman scattered light is collected and dispersed by the spectrometer and 32 recorded by a 578 × 385 pixel charge-coupled device (CCD) array detector with a pixel size of 22 µm. Selected concentrations (i.e., 0, 0.01, 0.1, and 1 mg/L) of CPF spiked in apple juice were added into AgNPs aqueous solution following the same procedures as aforementioned in section 2.6. After the colorimetric reaction, AgNPs solutions were centrifuged at 4,500 ×g for 5 min and the aggregated AgNPs were deposited onto a CN membrane for SERS spectral collection. The SERS spectra were recorded by using a 50× Nikon objective (NA = 0.75, WD = 0.37 mm) over a wavenumber range of 1400-600 cm-1. Each sample was illuminated with incident laser power of ~0.1 mW for 10 s and at least four spectra were collected. The system was operated under the control of WiRE 3.4 software (Renishaw). All samples were prepared at least in triplicate. 2.4.8 Spectral analysis and chemometric models The UV-Vis absorption spectra of different concentrations of CPF standard solutions and/or spiked apple juice eluents of MISPE from three trials were averaged. The average absorbance values of all of the concentrations or selected concentrations (i.e., 0.1, 1, 5, and 10 mg/L) at 393 nm were plotted. Collected SERS spectra were analyzed by OMNIC software version 8.2 (Thermo-Nicolet, Madison, WI, USA). Automatic baseline correction was firstly applied for all raw SERS spectra, followed by smoothing (9-point Savitzky-Golay algorithm) to reduce spectral noise before further analyses. 33 Unsupervised principal component analysis (PCA) and supervised partial least-squares regression (PLSR) models were constructed using Matlab R2015b (MathWorks Inc, Natick, MA, USA). Forty spectra collected from CPF spiked apple juice samples (10 spectra for each concentration) were used to establish PCA and PLSR models. The PCA model was served to cluster and differentiate apple juice samples spiked with different concentrations of CPF, while the PLSR calibration model was constructed to determine whether a linear relationship existed between the SERS spectra and different concentrations of CPF in apple juice samples. A five-fold cross-validation (i.e., randomly selected one fifth of the spectra was removed from the dataset for validation, followed by calibration with the remaining data) was used to evaluate the prediction power of the calibration model. 2.5 Results and discussion Figure 2.1 provides a detailed schematic illustration of this MIPs-SERS/colorimetric dual sensing system for the determination of CPF in apple juice. Briefly, CPF MIPs were synthesized by bulk polymerization and packaged into empty SPE cartridges to selectively separate CPF from apple juice. Then, a colorimetric sensing system was developed to rapidly screen and quantify CPF in apple juice based upon the color changes of synthesized AgNPs solutions. For the detection and quantification of CPF at a much lower concentration in apple juice, the aggregated AgNPs after colorimetric reaction were collected and illuminated by Raman laser for SERS measurement. With the availability of the synthesized MIPs and AgNPs, the analysis time of this MIPs-colorimetric sensor for rapid screening of CPF in apple juice is ~20 min. To further validate and quantify trace level CPF contamination using SERS, another 5 min is required for the centrifugation of AgNPs and Raman spectral collection. 34 Figure 2.1 Schematic illustration of MIPs-SERS/colorimetric dual sensing system for the determination of chlorpyrifos in apple juice. SPE: solid-phase extraction; CPF: chlorpyrifos; AgNPs: silver nanoparticles; SERS: surface-enhanced Raman spectroscopic; LSPR: localized surface plasmon resonance. 2.5.1 Synthesis and characterization of CPF MIPs The current study employed a non-covalent bulk polymerization method for the synthesis of MIPs to selectively separate CPF in apple juice. Previous studies have confirmed the feasibility of using MIPs synthesized by different polymerization methods to separate and enrich CPF in water (G. F. Lin et al., 2014; J. Liu, Yang, & Huai, 2010) and rice (Ma & Chen, 2013). Among these methods, bulk polymerization has many advantages over other polymerization methods, 35 such as no requirement of sophisticated instrumentation, simple and fast synthesis procedures, and the feasibility towards various target molecules (Y. Hu et al., 2015). To evaluate the selective recognition, binding capacity and equilibrium rate of the synthesized MIPs, static and kinetic adsorption tests were performed for both MIPs and NIPs. Figure 2.2(A) shows the static adsorption isotherm curves of MIPs and NIPs. The adsorption capacity (Q) was calculated by the following equation: Q=[(Ci-Cf) × V/W] (Qian et al., 2010), where Ci and Cf represent the initial and final concentration of CPF in methanol solution, respectively. Q values of MIPs were higher than that of NIPs and Q values for both MIPs and NIPs improved along with the increase in the initial concentration of CPF. At the CPF concentration of 100 mg/L, the adsorption capacity of MIPs (~0.739 mg/g) was about 4 times that of NIPs (0.170 mg/g). Therefore, the chemical and physical properties of CPF have been successfully imprinted onto MIPs. In general, the adsorption capacities of both MIPs and NIPs increase along with the increase in polarity of the solution. The kinetic adsorption tests were conducted to assess the equilibrium rate of both MIPs and NIPs. Figure 2.2(B) shows that both MIPs and NIPs reached the binding equilibrium within 10 min, demonstrating the feasibility of using MIPs to rapidly separate CPF from apple juice. Moreover, the higher Q values of MIPs over NIPs further validated the success imprinting of CPF into MIPs. 36 Figure 2.2 (A) Static binding isotherms of molecularly imprinted polymers (MIPs) and non-imprinted polymers (NIPs) for chlorpyrifos (CPF) (N = 3); (B) Kinetic binding isotherms of MIPs and NIPs (N = 3). 37 2.5.2 MISPE for CPF spiked apple juice In this study, a simple and rapid sample pretreatment procedure was applied using liquid-liquid extraction to remove macronutrients (e.g., sugar) from apple juice before MISPE. Considering the recovery of CPF and operational time, we set the flow rate for each step of MISPE process at 1 mL/min. The total recoveries of CPF from apple juice after sample pretreatment and MISPE were confirmed using HPLC-DAD and the results are listed in Table 2.1. In general, our MISPE achieved almost complete recovery (>90%) of CPF from apple juice, while about half of the CPF lost after being processed with NISPE. Besides, a decreased recovery of CPF from apple juice was observed with the increase in CPF concentration after passing through both MISPE and NISPE. This observation could be attributed to the saturation of the polymers and the reduced equilibrium rate of the polymers under the standstill condition. Considering the cost and operation time, MISPE cartridges containing 300 mg of MIPs were prepared and used for further experiments. Table 2.1 Recoveries of spiked chlorpyrifos in apple juice by molecularly imprinted solid phase extraction (MISPE) and non-imprinted solid phase extraction (NISPE). Concentration of chlorpyrifos (mg/L) MISPE recovery NISPE recovery 30 0.91 0.52 20 0.96 0.57 10 0.96 0.55 5 0.97 0.60 38 1 0.99 0.63 2.5.3 Colorimetric and UV-Vis spectroscopic detection of CPF In the current study, MISPE was utilized to selectively and effectively separate CPF from apple juice. Therefore, no further strategies are required to ensure the selectivity for the colorimetric sensor. The fresh label-free colloidal solution of AgNPs was prepared for colorimetric and SERS detection by the reduction of silver ions in the presence of NaBH4. In our preliminary study, we noticed the effects of stirring speed and reaction time on the performance of AgNPs for colorimetric reaction. AgNPs aggregated at low stirring speed and thus were no longer applicable for colorimetric sensing. In addition, inappropriate incubation time reduced the sensitivity and reproducibility of colorimetric sensing because the stability of AgNPs is strongly dependent upon the incubation time. By setting the incubation time during the synthesis of AgNPs to be 20 min and stirring speed to be 300 rpm, the produced colloidal solution of AgNPs exhibited the best performance of colorimetric sensing. AgNPs solution was measured by UV-Vis spectrometry (Figure 2.3) and the appearance of a sharp absorption band at 393 nm confirmed the nano-crystalline characteristic and the well dispersed state of AgNPs (Hajizadeh, Farhadi, Forough, & Sabzi, 2011). 39 Figure 2.3 UV-Vis absorption spectrum of AgNPs. After the addition of different concentrations of CPF standard solutions into the fresh prepared AgNPs colloidal solutions, the changes in the color of the solutions were recorded (Figure 2.4). Figure 2.4(A) demonstrates the darkening of the color from yellow to brown with CPF concentrations increased from 1 to 5 mg/L, and then became light purple at 10 mg/L. The color of the solution diminished and became colorless (or light gray) when the concentration of CPF was further increased to 20 mg/L due to the extensive aggregation of AgNPs. Therefore, we can approximately determine the concentrations of CPF ≥ 5 mg/L by naked eyes based upon the colors of the solutions. However, when the concentration of CPF was ≤ 1 mg/L, there was no observable difference in color among different concentrations of CPF. To improve the sensitivity of this colorimetric sensor, UV-Vis spectrometer was used and the spectra are shown in Figure 2.4(B). The intensity of the absorbance at 393 nm decreased along with the increase in the 40 concentration of CPF and red shift was observed in the spectra as well. The sensitivity of this proposed colorimetric sensor increased significantly and was capable of differentiating samples containing 0.01 mg/L CPF from the control group (i.e., 0 mg/L CPF). By confirming that the addition of 100 µL of ethanol or 100 µL of DI water has no influence on the color of AgNPs, we concluded that the aggregation of AgNPs was caused by CPF specifically (data not shown). The absorbance of different concentrations of CPF at 393 nm was plotted against the concentration and this result is shown in Figure 2.4(E). Linear regression model could not be constructed to correlate the absorbance and corresponding CPF concentrations at the range of 0-30 mg/L. However, a strong linear relationship (R2 = 0.9937) was identified at the CPF concentrations ranging 0.1 to 10 mg/L, validating the feasibility of using UV-Vis spectroscopic-colorimetric method to quantify CFP at concentrations from 0.1 to 10 mg/L. Nevertheless, this value cannot meet the maximum residue limit (MRL) proposed by Health Canada for CPF in apples (0.01 mg/L). 41 Figure 2.4 (A) Representative photographic image of colorimetric sensing with different concentrations of CPF standard solutions; (B) average UV-Vis absorption spectra of AgNPs in the presence of different concentrations of CPF (N = 3); (C) representative 42 photographic image of colorimetric sensing with MISPE eluents of apple juice spiked with different concentrations of CPF; (D) average UV-Vis absorption spectra of AgNPs in the presence of MISPE eluents of apple juice spiked with different concentrations of CPF (N = 3) ; (E) plot of the average absorption values at 393 nm versus different concentrations of CPF standard solutions (N = 3). CPF: chlorpyrifos; MISPE: molecularly imprinted solid phase extraction. The corresponding tests of using MIPs-colorimetric sensor for the determination of CPF in apple juice were also conducted. The results shown in Figure 2.4(C) and Figure 2.4(D) are in agreement with the results of CPF standard solutions very well, indicating the feasibility of using MIPs-colorimetric sensor for the determination of CPF contamination in apple juice. 2.5.4 Determination of CPF in apple juice by SERS The aggregated AgNPs collected by centrifugation after colorimetric reactions were served as SERS-active substrates for SERS spectral collection. In the current study, CN membrane was utilized as the carrier for AgNPs because of its relative Raman-inert feature compared to gold-coated glass slide [Figure 2.5(A)]. In addition, the concentrated AgNPs solution dry faster on CN membrane surface compared to that on gold-coated glass slide, achieving reduced waiting time and immediate Raman spectral collection. After depositing the AgNPs, several Raman bands contributed by the CN membrane completely disappeared. As discussed in section 2.5.3, no stabilizer was added into AgNPs, therefore no obvious Raman signal was derived from AgNPs. Taken together, AgNPs synthesized by the method adopted in this study was feasible to serve as SERS-active substrate. 43 The CPF spectra of normal Raman, SERS and MIPs-SERS are shown in Figure 2.5(A), and the band assignment is summarized in Table 2.2. The spectral features of normal Raman spectra represent specific vibrational modes derived from CPF molecule, and thus reflect the molecular structure of CPF (X. N. Lu et al., 2011). The bands at wavenumbers of 630, 803, 917, 1102, and 1169 cm-1 are assigned to P=S or C-Cl stretch, ring breathing mode, C-C stretch of benzenoid ring, ν (P-O-C)’, and the in-plane vibrations of the conjugated =C-C=, respectively, and these bands appeared at the same wavenumber for normal Raman, SERS, and MIPs-SERS spectra. In the SERS and MIPs-SERS spectra, the band that occurs at 676 cm-1 in normal Raman spectrum shifted to 683 cm-1, which can be attributed to a SERS effect (Haynes et al., 2005). This may be caused by the dipole changes of the targeted molecules that is affected by SERS-active substrate and subsequently shift the location of SERS spectral bands. In MIPs-SERS spectrum, the band at 1218 cm-1 was related to the band shifted from 1238 cm-1 in normal Raman and SERS. This blue shift may be due to the presence of Ca2+, Fe2+ and Zn2+ in apple juice (L. M. Chen & Liu, 2011). The uniformity of the bands in SERS and MIPs-SERS indicates the effective removal of apple juice components and extraction of CPF by MISPE. The bands with minor intensity appeared in MIPs-SERS spectrum may be derived from apple juice residues. In conclusion, the MIPs-SERS sensing system can be applied to effectively separate and reliably detect CPF in apple juice. 44 Figure 2.5 (A) Representative spectral features of (a) CN membrane, (b) AgNPs on CN membrane, (c) normal Raman of CPF, (d) SERS of CPF standard solution (0.1 mg/L), (e) 45 MIPs-SERS of apple juice sample spiked with CPF (0.1 mg/L); (B) Averaged MIPs-SERS spectra (N = 10) of CPF in apple juice with different concentrations a) 0 mg/L, b) 0.01 mg/L, c) 0.1 mg/L, d) 1 mg/L. CN: cellulose nitrate; CPF: chlorpyrifos; SERS: surface enhanced Raman spectroscopy. Table 2.2 Raman peak assignment of normal Raman and SERS spectral features of chlorpyrifos(Kim, Barcelo, & Li, 2015; Movasaghi, Rehman, & Rehman, 2007; Shende, Inscore, Sengupta, Stuart, & Farquharson, 2010). Raman shift (cm-1) Assignment 630 P=S or C-Cl stretch 649 P=S or C-Cl stretch 676 C-Cl stretch 683 C-Cl stretch 719 C-N stretch 747 P-O-C or P-O-R stretch 785 O-P-O stretch 803 Ring breathing mode 832 C-H out of plane bending in benzenoid ring 917 C-C stretch of benzenoid ring 972 ν (C-C) wagging 1017 C-O stretch 1102 ν (P-O-C)’ 46 1154 C-C, C-N stretch 1169 In-plane vibrations of the conjugated =C-C= 1238 Ring mode 1276 CH2 deformation 1300 CH2 deformation SERS spectra of MISPE-treated apple juice spiked with different amounts of CPF were collected as well. All the raw SERS spectra were automatically baseline corrected and smoothed, and the average spectra of each spiked concentration (N = 10) are shown in Figure 2.5(B). The intensities of bands at 630 cm-1, 683 cm-1, and 803 cm-1 increased along with the increase in the concentration of CPF spiked in apple juice. Thereafter, an unsupervised two-dimensional PCA model was constructed using spectral features at wavenumbers of 600 to 900 cm-1 to differentiate apple juice samples spiked with different levels of CPF. As shown in Figure 2.6, data points in each concentration group (i.e., 0, 0.01, 0.1, and 1 mg/L) were well segregated and clustered, exhibiting high sensitivity of this MIPs-SERS method to detect CPF in apple juice with concentration as low as 0.01 mg/L (i.e., MRL proposed by Health Canada). 47 Figure 2.6 Representative two-dimensional principal component analysis for apple juice samples spiked with different concentrations of chlorpyrifos (blue: 0 mg/L, orange: 0.01 mg/L, yellow: 0.1 mg/L, purple: 1 mg/L) (N = 3). The PLSR model was then constructed to reveal the correlation between the concentrations of CPF in apple juice and the corresponding SERS spectral features (wavenumbers from 600 to 900 cm-1). A five-fold cross-validation was applied to test the reliability of the constructed PLSR calibration model and a representative calibrated partial least-squares regression model is shown in Figure 2.7. Table 2.3 summarizes the major parameters derived from this model analysis. The high regression coefficient (R2-C = 0.9885, R2-CV = 0.8688) and low root-mean-square errors of both calibration (RMSEC = 0.0453) and cross-validation (RMSECV = 0.1470) confirmed the 48 reliability of this PLSR model. Therefore, we conclude that this MIPs-SERS sensing system has outstanding performance for both detection and quantification of CPF contamination in apple juice. Figure 2.7 Representative calibrated partial least-squares regression model for the prediction of chlorpyrifos concentration in apple juice using SERS spectral features (Raman shift range: 600 – 900 cm-1). Table 2.3 PLSR model for prediction of chlorpyrifos concentration in apple juice using SERS spectral features. Features value 49 Na 40 nb 8 Number of latent variables 8 RMSECc 0.0453 R2 - C 0.9885 RMSECVd 0.1470 R2 - CV 0.8688 RMSEPe 0.1220 R2 - P 0.8965 a N, number of spectra for calibration. b n, number of spectra for prediction. c RMSEC, root mean squares error of calibration. d RMSECV, root mean squares error of cross-validation (5-fold). e RMSEP, root mean squares error of prediction. 2.6 Conclusion We have successfully developed a MIPs-SERS/colorimetric dual sensor for determination of trace level CPF in apple juice. The synthesized MIPs can effectively separate CPF in apple juice, while AgNPs-based colorimetry and SERS are able to detect CPF. MIPs-colorimetric sensor is an easy-to-operate and rapid screening method that can determine high concentration of CPF by naked eyes, and a quantitative method to detect relatively low concentration of CPF using a UV-Vis spectrometer. In contrast, MIPs-SERS sensor is more sensitive for quantification of trace-level CPF in apple juice. The integration of these two sensing methods shows great potential for 50 rapid (entire analysis time <25 min) and accurate determination of CPF in apple juice required by both government laboratories and food industry. 51 Chapter 3: Molecularly imprinted core-shell gold nanoparticles for sensitive and quantitative detection of 2,4-dichlorophenoxyacetic acid in milk using surface-enhanced Raman spectroscopy 3.1 Introduction In recent years, the quality and safety of foods have attracted increasing attentions among the publics due to the reported tremendous food safety issues and their moderate/severe adverse health effects. Rapid, accurate, and ultrasensitive determination of contaminants (e.g., microbes, pesticides, mycotoxins, etc.) in agri-food products is of great importance to control food quality and ensure food safety (Chiou, Leung, Lee, & Wong, 2015). Pesticides are widely used in agriculture sectors to protect crops from insects, weeds, fungi, and other pests, and their routine detection in agri-foods is required because of their potential toxicity to humans (Carvalho, 2017; Chiou et al., 2015; Y. Guo et al., 2020). Although traditional methods (e.g., chromatography and immunoassays) may be sensitive and accurate for detection of trace level pesticides, cumbersome sample pretreatments and complicated operation processes are inevitable, and therefore have limited their application in food industry for high-throughput screening and detection of chemical hazards (D. W. Sun, Huang, Pu, & Ma, 2021). As a result, development of novel, rapid, accurate, and ultrasensitive methods for pesticides detection is of great significance to food industry (Narenderan, Meyyanathan, & Babu, 2020). Surface-enhanced Raman scattering (SERS) is a promising technology for rapid and sensitive determination of various analytes in food products (Gillibert, Huang, Zhang, Fu, & de la 52 Chapelle, 2018; Z. S. Lin & He, 2019; Pilot, 2018; D. R. Zhang, Pu, Huang, & Sun, 2021). Sensitive detection by SERS is achieved by amplifying Raman scattering signals of molecules in close proximity to the surface of noble metallic nanostructures (e.g., gold, silver, copper) through the excitation of localized surface plasmon resonance (LSPR) present on the surface of nanostructures. Recent studies suggested that the enhancement factor can be as much as 1014, indicating that single-molecule detection is possible by SERS (Jiang, Bosnick, Maillard, & Brus, 2003; Kneipp et al., 1997; Nie & Emory, 1997). Thus, SERS is expected to be a powerful tool for ultrasensitive detection of trace level food contaminants. Although significant progress has been achieved on improving the sensitivity and accuracy of SERS-based detection methods, the relatively poor reproducibility and absence of selectivity towards specific analytes have restricted their applications in practical food analysis (Bernat, Samiwala, Albo, Jiang, & Rao, 2019; Y. Huang, Wang, Lai, Fan, & Rasco, 2020; R. Shi, Liu, & Ying, 2018). Regarding the poor reproducibility of SERS-based analytical methods, two major restriction factors are heterogeneous distribution of hot spots on SERS substrate and aggregation of colloidal SERS substrates that occurred during sample preparation or upon substrate drying (Perez-Jimenez et al., 2020). Hence, numerous studies have been conducted to improve the uniformity and stability of SERS substrates. For example, electrochemical oxidation and reduction cycles method have been widely investigated for the fabrication of solid SERS substrates, and protecting agents (e.g., citrate ions, CTAB, and PVP) have also been applied for the synthesis of colloidal SERS substrates (Perez-Jimenez et al., 2020; Xie, Pu, & Sun, 2018). Shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS), as a novel SERS technique, has attracted a great deal of attention since its discovery (J. F. Li et al., 2010). This 53 new strategy allows chemically coating a uniform and ultrathin layer of silica shell around the surface of colloidal nanoparticles. As a result, SERS-active nanostructures can be protected from the surrounding environment by silica shell to avoid aggregation of metallic nanoparticles. In the meanwhile, enhancement performance of SHINERS is also associated with the thickness of silica shell that has been coated (J. F. Li et al., 2010; W. Li et al., 2019). Hence, it is critical to optimize the thickness of silica shell to achieve better sensitivity and reproducibility for SHINERS. The other reason of poor reproducibility of SERS is that signals usually vary along with the inconstant Raman instrumental conditions and focusing positions. To conquer this drawback, internal standards that experience the same physical and chemical environment as the target molecules have been introduced for signal calibrations in SERS analysis (Loren et al., 2004; X. Wang, Huang, Hu, Yan, & Ren, 2020). A recent study demonstrated that it is possible to combine core-shell structure nanoparticles with embedded internal standards to further improve the performance of SERS-based detection and serve as a reliable quantitative analytical tool (Shen et al., 2015). Selectivity is another obstacle that has restricted SERS-based analytical methods for practical applications in agri-food industry. Compared to the native ingredients/components in foods or crops, the target compounds (e.g., toxin) in those matrices are usually at much lower concentrations. Hence, cumbersome sample extraction, cleanup and concentrating procedures are requested to limit spectral interferences and achieve more accurate SERS analyzing results (Narenderan et al., 2020). Traditional sample preparation methods, such as liquid-liquid extraction, solid phase extraction, and QuEChERS, are laborious, time-consuming, and have no selectivity towards specific detection targets. To endow SERS-based detection methods with 54 selective functions, biological receptors such as antibodies and aptamers have been widely investigated as selective sensing elements to develop novel SERS substrates (Boushell, Pang, & He, 2017; X. Li et al., 2015). For example, Luo and colleagues have applied aptamer-functionalized SERS substrate for simultaneous separation and detection of microcystin-LR and microcystin- RR (Luo et al., 2021). Although biological receptors can offer SERS substrates with selective functions, the performances of this type of substrates are susceptible to the environmental conditions. Molecularly imprinted polymers (MIPs) are chemically synthesized materials with selective affinities to target molecules and have been investigated to develop functional SERS substrates in the recent studies (X. Guo et al., 2020). Compared to biological receptors, MIPs have shown great promise for recognition and separation of analytes of low molecular weight with the advantages of excellent mechanical and chemical stability, remarkable robustness, and low-cost. However, most of the reported MIPs-based SERS methods only applied MIPs as a separate sample pretreatment tool and failed to integrate it with SERS-active substrates (especially for colloidal nanoparticles) as an integral element to realize rapid and accurate determination of food contaminants (F. Gao et al., 2014; Y. Hu et al., 2015). Herein, we report a facile method to assemble tailor-made MIPs with colloidal gold nanoparticles together as a functional SERS substrate for selective, sensitive, and quantitative detection of 2,4-dichlorophenoxyacetic acid (2,4-D) in food matrix. To prepare the SERS substrate, an ultrathin layer of silica shell was firstly coated on the surface of AuNPs to protect AuNPs from getting aggregated during the harsh synthesis conditions of MIPs. In the meanwhile, SERS-active molecules, 4-aminothiophenol (4-ATP), were embedded inside the MIPs shell as an internal standard for spectral intensity calibrations. As the 2,4-D imprinted 55 MIPs material was wrapped on the surface of AuNPs as an ultrathin and very uniform shell structure, SHINERS detection can be achieved using the obtained AuNPs@MIPs hybrid SERS substrate. The capability of using AuNPs@MIPs SERS substrate for selective and quantitative detection of 2,4-D was investigated. Finally, the performance of AuNPs@MIPs for the determination of 2,4-D in milk was assessed. 3.2 Hypotheses H1: Specific MIPs material for 2,4-D recognition can be synthesized and coated onto the surface of AuNPs. H2: AuNPs@MIPs nanoparticles can be applied to selectively recognize and separate 2,4-D. H3: AuNPs@MIPs captured with 2,4-D can be applied directly for SERS measurement. H4: Adding 4-ATP into AuNPs@MIPs nanomaterial as internal reference standard can improve the accuracy for SERS measurement. 3.3 Objectives O1: To synthesize an ultrathin layer of 4-ATP embedded MIPs material onto the surface of AuNPs for 2,4-D. O2: To apply AuNPs@MIPs nanomaterial for selective separation of 2,4-D from milk sample. 56 O3: To identify the analytical performance of using AuNPs@MIPs nanoparticles as SERS substrate for quantitative 2,4-D measurement. 3.4 Material and methods 3.4.1 Materials Hydrogen tetrachloroaurate(Ⅲ) trihydrate (HAuCl4·3H2O, 99.9%), 2,4-dichlorophenoxyacetic acid (2,4-D), trisodium citrate (C6H5Na3O7·3H2O), hexadecyltrimethylammonium bromide (CTAB), tetraethyl orthosilicate (TEOS), (3-aminopropyl)trimethoxysilane (APTES), and 4-aminothiophenol (4-ATP) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Hydrogen chloride (HCl) and sodium hydroxide (NaOH) were obtained from Fisher Scientific (Toronto, Canada). Nitrocellulose (NC) membrane was bought from Sartorius Stedim Biotech S.A. (Göttingen, Germany). Milli-Q water (18.2 MΩ·cm) was applied throughout the experiment. All chemicals used in this work were of analytical grade and applied without further purification. Milk samples were obtained from a local grocery store in Vancouver, Canada. 3.4.2 Preparation of functional AuNPs@MIPs SERS substrate The 4-ATP-tagged AuNPs@MIPs SERS nanomaterials were prepared by wrapping an ultrathin layer of MIPs shell onto the surface of pre-synthesized AuNPs according to the strategy shown in Figure 3.1. 57 Figure 3.1 Schematic illustration of the preparation of 2,4-D imprinted AuNPs@MIPs SERS substrate with 4-ATP embedded as the internal standard. 3.4.3 Preparation of AuNPs The AuNPs were synthesized according to a previously reported sodium citrate reduction method with some modifications (J. T. Song, Zhang, Qin, & Zhao, 2015). In detail, 200 mL of boiling water was added with 3 mL of HAuCl4 solution (1%, w/v) and stirred for 10 min before rapidly injected with 3 mL of sodium citrate solution (1%, w/v). After boiling for 20 min, the mixture was naturally cooled to room temperature. The obtained Au colloidal solution was directly applied for further experiments. 3.4.4 4-ATP-embedded MIPs shell wrapping An ultrathin layer of silica was firstly coated onto the surface of AuNPs to avoid the aggregation of AuNPs to occur during the MIPs polymerizing process. Briefly, 50 mL of Au colloid solution was added with 3.5 mL of CTAB solution (0.1 M) and stirred for 1 hour. Then, 750 µL of NaOH solution (0.1 M) was added to the mixture to adjust the pH value to 10. After that, 20 µL of TEOS was added and the mixed solution was stirred for 3 hours to wrap an ultrathin layer of silica shell on the surface of AuNPs. Subsequently, the resultant Au@SiO2 colloid solution was 58 mixed with 5 mL of 4-ATP ethanol solution (5 mg/mL) and stirred for another 3 hours to allow the self-assembly of 4-ATP onto AuNPs@SiO2. For the MIPs shell wrapping procedure, 7.5 mg of 2,4-D crystal was directly added into the above mixture, followed by adding 22.5 µL of APTES and 25 µL of TEOS. The mixture was stirred at room temperature for 24 hours to assure the complete polymerization of the MIPs shell. Then, the 2,4-D template molecules were removed by suspending the AuNPs@MIPs nanomaterials in ethanol with the addition of 1M HCl to adjust the pH value to 1. After stirring at room temperature for 1 hour, AuNPs@MIPs were collected and washed with ethanol and water alternately. Finally, AuNPs@MIPs was stored in ethanol for further use. In comparison, nonimprinted polymers (NIPs)-wrapped AuNPs (AuNPs@NIPs) were also synthesized following the same procedure as aforementioned but with the absence of the template molecules (2,4-D). 3.4.5 Adsorption performance confirmation for AuNPs@MIPs The adsorption capacities of AuNPs@MIPs and AuNPs@NIPs were determined as follows: 0.5 mL of AuNPs@MIPs or AuNPs@NIPs suspension (A528 = 1.0) was centrifuged at 6,000 ×g for 5 min and the supernatant was discarded. Then, 0.5 mL of 2,4-D standard solution at different concentrations was added, followed by shaking at room temperature for 2 hours. After being centrifuged at 6,000 ×g for 5 min, the supernatant was analyzed using an Agilent 1260 Infinity HPLC system coupled with a diode array detector (DAD) with the wavelength fixed at 284 nm. A total volume of 20 µL of samples were injected into a Kinetex C18 column (3.00 × 100 mm, 2.6 µm) at 22˚C. The mobile phase was 0.1% formic acid/acetonitrile (40:60, v/v) with a flow 59 rate at 0.5 mL/min. The absorption capacities (Q) of AuNPs@MIPs and AuNPs@NIPs were calculated according to the following equation: Q = (c0 – ct) × V2,4-D/Vp, where c0 and ct are the initial and finial concentrations of 2,4-D in the mixture, respectively. V2,4-D is the volume of 2,4-D solution and Vp is the volume of either AuNPs@MIPs or AuNPs@NIPs suspension. 3.4.6 Pretreatment procedures for milk samples The milk samples were firstly detected to be free of 2,4-D using HPLC-DAD before use. To prepare the 2,4-D contaminated milk samples, 5 mL of milk sample was individually spiked with different concentrations of 2,4-D (i.e., 0, 0.1, 1, 10, 30 µg/mL). After that, 5 mL of acetonitrile containing 1% formic acid was added, followed by vortex-mixing for 1 min. Then, 2.5 g of MgSO4 was added into the mixture and vortex-mixed for another 1 min. After centrifugation at 8,000 ×g for 10 min, the supernatant was passed through a 0.2-µm PTFE syringe filter and stored at 4˚C before use. The recoveries of 2,4-D extracted from milk samples were determined using HPLC-DAD. 3.4.7 SERS measurements The as-prepared AuNPs@MIPs suspension (0.5 mL, A528 = 1.0) was centrifuged at 6,000 ×g for 5 min, followed by mixing with 200 µL of either 2,4-D standard solution or milk sample extract at different concentrations (i.e., 0, 0.1, 1, 10, 30 µg/mL). After shaking at room temperature for 30 min, the mixture was concentrated in 10 µL of 50% ethanol (v/v). For SERS spectral collection, 1 µL of the AuNPs@MIPs concentrate was deposited onto a piece of NC membrane and dried at room temperature before SERS measurements. SERS spectra were collected using a Renishaw inVia confocal Raman microscopy system coupled with a 785 nm line shape diode 60 laser as the excitation source. During the measurements, the laser was guided at certain positions using a 50× Nikon objective lens (NA = 0.75, WD = 0.37 mm). The laser power at sample was about 0.1 mW and the acquisition time was 10 s. 3.4.8 Characterizations The morphologies of the as-prepared SERS substrate materials were examined using a transmission electron microscope (TEM, Hitachi H7600). UV-vis absorption spectra were recorded using a Shimadzu UV-1800 spectrophotometer (Shimadzu Inc., Japan). 3.4.9 Three-dimensional finite difference time domain (3D-FDTD) simulation method The electromagnetic field distribution on AuNPs@MIPs was simulated using the 3D-FDTD method (Lumerical FDTD Solutions, Lumerical Inc, Vancouver, Canada). The simulation region was a 150×80×80 nm3 cuboid space with two AuNPs@MIPs particles (AuNPs core: 33.6 nm, MIPs shell: 6.5 nm) placed on the surface of NC membrane (refractive index: 1.5). The cell size was 0.5×0.5×0.5 nm3. The distance between the two adjacent AuNPs@MIPs spheres was set to be 0 - 10 nm. A Total-field scattered-field (TFSF) plane wave source (λ = 785 nm) was placed in positive z-axis as the excitation laser. Perfectly matched layer (PML) boundary conditions were adopted for the simulation. Finally, the enhancement of electromagnetic field was also calculated based on the FDTD model. 3.4.10 Spectral analysis and chemometric models Raw SERS spectra were analyzed by OriginPro software (OriginLab Corporation, Northampton, MA, USA). Both substrate baseline correction and spectral smoothing (9-point Savitzky-Golay 61 algorithm) were applied to reduce spectral noise. Raman band at 1078 cm-1, which was attributed to the ν(CS) vibrations of 4-ATP, was employed as the internal standard for SERS spectral normalization. Chemometric models were constructed using MATLAB R2018b (MathWorks Inc, Natick, MA, USA) to interpretate the results obtained from Raman spectroscopy. Unsupervised principal component analysis (PCA) was established on the basis of SERS intensity of Raman shift from 1020 cm-1 to 1800 cm-1 to differentiate samples with different 2,4-D levels. Supervised partial least-squares regression (PLSR) model was developed to investigate the co-relationship between 2,4-D concentrations and the corresponding SERS spectra for quantitative analysis of 2,4-D in milk. 3.5 Results and discussion As a useful characterization and detection method, SERS has been intensively investigated in food science in the past decade. Despite the significant achievements on improving the sensitivity of SERS-based detection, the inherent poor reproducibility for Raman spectra and lack of selectivity for specific analytes still impede the application of SERS in practical food analysis (Langer et al., 2020; Ouyang, Ren, Zhu, & Irudayaraj, 2017). In our previous study, we successfully applied MIPs as the solid-phase extraction material to selectively separate and concentrate pesticide from food matrices for sensitive SERS detection (S. L. Feng, Hu, Ma, & Lu, 2017). Besides, some recent studies also represented that MIPs were successfully integrated with immobilized SERS substrate for selective analyte detection (Castro-Grijalba et al., 2020; Kamra, Zhou, Montelius, Schnadt, & Ye, 2015). Due to some technical challenges (e.g., 62 nanoparticle modification and aggregation), it is still challenging to hybrid MIPs with colloidal gold or silver nanoparticles as the functional substrates for selective SERS analysis. Although several attempts have been reported, there are many problems to overcome before practical applications. On the basis of these considerations, we designed a simple and easy implemented approach to fabricate MIPs-wrapped AuNPs for selective SERS detection of chemical hazards in foods. To make the SERS results more reliable and quantifiable, an internal standard was also introduced to the designed MIPs@AuNPs for SERS spectral calibrations. 3.5.1 Preparation and characterization of 2,4-D-imprinted AuNPs@MIPs SERS substrate As shown in Figure 3.1, the 2,4-D-imprinted SERS substrate was designed using AuNPs as the core, followed by pre-coating an ultrathin layer of silica shell on the surface of AuNPs to protect the AuNPs from getting aggregated during the MIPs shell wrapping process. Then, an additional thin layer of MIPs shell was coated onto the surface of silica-protected AuNPs using 2,4-D as template molecule, APTES as functional monomer and TEOS as crosslinking agent. After the removal of template molecules, cavities containing specific binding sites for the analytes can be tailored on the as-synthesized AuNPs@MIPs SERS substrate. According to a previous study, the SHINERS performance was impacted by the thickness of silica shell that was coated, and the thinner the silica shell coated on AuNPs the higher SHINERS intensity would be recorded (J. F. Li et al., 2010). Hence, the thicknesses of both silica shell pre-coated and MIPs shell wrapped were optimized to be as thin as possible (data not shown). In this study, the SERS-active molecule, 4-ATP, was also embedded in the MIP shell and served as an internal standard for 63 SERS spectral normalization, and therefore to quantitatively analyze the contamination levels of 2,4-D in food matrices (i.e., milk). TEM images of bare AuNPs, 2,4-D imprinted polymers-wrapped AuNPs@MIPs, and non-molecularly imprinted polymers-wrapped AuNPs@NIPs are shown in Figure 3.2(a-c). The images clearly show a layer of polymers shell uniformly coated on the surface of both AuNPs@MIPs and AuNPs@NIPs compared to bare AuNPs. No self-aggregation among the AuNPs after shell coating is observed in the TEM images. The diameter distribution analyzed from the TEM images shows that the average size of as-prepared AuNPs is 33.6 ± 5.6 nm. The shell thicknesses of AuNPs@MIPs and AuNPs@NIPs are 6.5 ± 1.5 nm and 7.3 ± 1.1 nm, respectively. In addition, UV-Vis absorption spectroscopy was performed to characterize the as-synthesized nanoparticles (as shown in Figure 3.2g). The original absorption peak of AuNPs is located at 530 nm and slightly red-shifted after the MIPs/NIPs shell being wrapped. This red-shift of the absorption peak is caused by the coating of shell structure on AuNPs surface, which has altered the surrounding refractive index of the nanoparticles (Kang & Taton, 2005). Because the chemical components of MIPs shell and NIPs shell are similar, no significant difference is observed in UV-Vis absorption spectra between AuNPs@MIPs and AuNPs@NIPs. Moreover, the comparable fine shapes of the absorption peaks for both AuNPs@MIPs and AuNPs@NIPs to AuNPs also indicate that there was no AuNPs getting aggregated during the shell wrapping process, which is consistent to the TEM results. 64 65 Figure 3.2 Characterization of the as-synthesized nanoparticles. Representative TEM images (left) of (a) AuNPs, (b) AuNPs@MIPs, and (c) AuNPs@NIPs, and the relevant histograms (right) of (d) AuNPs size distribution, and (e/f) MIPs/NIPs shell thickness analyzed from TEM images using ImageJ software based on over 50 nanoparticles. (g) UV-Vis absorption spectra of AuNPs, AuNPs@MIPs, and AuNPs@NIPs. We then investigated the SERS activity of the synthesized AuNPs@MIPs nanomaterial using Raman spectroscopy. Our previous study validated that the NC membrane is a good supporting substrate for SERS spectral collection as colloidal SERS substrates can be evenly distributed on its surface and no interference peak will be introduced during the measurement (S. L. Feng et al., 2017). Hence, NC membrane was also applied as the supporting substrate for the deposition of AuNPs@MIPs in the current study. As discussed above, the SERS active molecules, 4-ATP, with distinct spectral peaks were embedded in the MIPs shell and applied to characterize the SERS activity of AuNPs@MIPs. Raman spectra of AuNPs, AuNPs@SiO2, 4-ATP, and AuNPs@MIPs are shown in Figure 3.3a. With a layer of silica shell pre-coated on the surface of AuNPs, Raman signals of AuNPs disappeared and no new feature peak was detected. After the 4-ATP-embedded MIPs shell being wrapped, characteristic Raman peaks of 4-ATP were consistently detected for AuNPs@MIPs. Peaks at 634 cm-1 (γ(CCC)), 1005 cm-1 (γ(CC) + γ(CCC)), 1175 cm-1 (δ(CH)), and 1480 cm-1 (ν(CC) + δ(CH)) are observed in both the normal Raman spectrum of 4-ATP and SERS spectrum of AuNPs@MIPs. In addition, the strong peaks at 1088 cm-1 (ν(CS)) and 1595 cm-1 (ν(CC)) for 4-ATP normal Raman spectrum are shifted to 1078 cm-1 and 1585 cm-1, respectively, for AuNPs@MIPs. Generally, the phenomenon of these blue shifts for SERS spectra has been validated to be related to the tunneling charge transfer 66 effect that occurs between nanoparticles and chemical molecules (Wan et al., 2017; Q. Zhou, Li, Fan, Zhang, & Zheng, 2006). Taking advantage of the ultrathin thickness of the silica and MIPs shells coated, 4-ATP molecules embedded in the MIPs shell were able to be distributed within the localized surface plasmon resonance (LSPR) induced electromagnetic field where tunneling charge transfers occur between 4-ATP and AuNPs, and therefore caused the variances of ν(CS) and ν(CC) vibrations. As a result, blue shifts were observed for some of the 4-ATP feature peaks in the current study. Moreover, because 4-ATP molecules are embedded in the shell structure and separated from the environment, they were barely washed off during the preparation and measurement, which allows the enhanced 4-ATP Raman signals to be detected consistently (as shown in Figure 3.3b) (Y. Feng et al., 2012). Therefore, the feasibility of applying embedded SERS active molecule, 4-ATP, as an internal standard for SERS spectral calibration is validated. The unique SERS peak of 4-ATP at 1078 cm-1 (ν(CS)) was selected as the reference peak for spectral normalization. Other Raman feature peaks of 4-ATP are summarized in Table 3.1. 67 68 Figure 3.3 (a) Representative Raman spectra of AuNPs, AuNPs@SiO2, the molecularly imprinted SERS substrate (AuNPs@MIPs) with 4-ATP as internal standard, and 4-ATP normal Raman. (b) SERS spectra of 4-ATP obtained at 10 random sites using AuNPs@MIPs as the substrate. Table 3.1 Summary of vibrational band assignments for 4-ATP and 2,4-D (Costa et al., 2009; X. G. Hu, Wang, Wang, & Dong, 2007). 4-ATP 2,4-D Raman shift/cm-1 Assignment Raman shift/cm-1 Assignment Normal Raman SERS Normal Raman SERS 471 Ring twisting 412 ν(CC)ring + ν(C–Cl) 634 634 γ(CCC) 456 δ(CC)ring out of plane 799 Ring deformation 558 δ(COC) + δ(CC) 819 C-H wag 594 594 δ(CC)ring out of plane 1005 1005 γ(CC) + γ(CCC) 645 ν(CC)ring + ν(C–Cl) 1088 1078 ν(CS) 716 δ(COO−) + ν(CC)ring 1175 1175 δ(CH) 839 855 ν(CC)ring + ν(C–O) + ν(C–Cl) 1480 1480 ν(CC) + δ(CH) 892 ν(C–COO−) 69 1493 ν(CC) + δ(CS) 1005 1005 γ(CC) + γ(CCC) 1595 1585 ν(CC) 1026 ν(CC)ring + ν(CO) 1053 1053 ν(CC)ring + ν(CO) + δ(CH)ring 1105 ν(CC)ring + ν(C–Cl) 1160 1175 δ(CH)ring 1228 τ(CH2) 1258 δ(CH)ring + ν(CO) 1271 1280 δ(CH)ring 1310 ν(CC)ring 1394 ω(CH2) 1445 1414 νs(COO−) + ν(CC) + ω(CH2) 1579 1565 νas(COO−) + ν(CC)ring 1594 1585 ν(CC)ring + νas(COO−) 1672 ν(CC)ring Furthermore, the adsorption performance of AuNPs@MIPs was investigated. The adsorption capacities (Q) of AuNPs@MIPs and AuNPs@NIPs towards 2,4-D was compared. As shown in Figure 3.4, the Q values for both AuNPs@MIPs and AuNPs@NIPs increased along with the increase in 2,4-D concentrations. The Q values for AuNPs@MIPs are much higher compared to those for AuNPs@NIPs at every tested concentration. Specially, the Q value for AuNPs@MIPs 70 (2.37 ± 0.28 µg/mL) at the concentration of 50 µg/mL is about 1.6 times of that for AuNPs@NIPs (1.46 ± 0.30 µg/mL) at the same concentration. These adsorption test results demonstrate that the 2,4-D imprinted MIPs have been successfully synthesized and wrapped on the surface of AuNPs. Figure 3.4 Characterization of the adsorption capacities of AuNPs@MIPs and AuNPs@NIPs towards 2,4-D. Taken together, both the results from TEM and UV-Vis absorption spectroscopy have confirmed the formation of core/shell structure for the AuNPs@MIPs SERS substrate. Raman spectroscopic analysis demonstrates the embedding of 4-ATP in the MIPs shell as well as its feasibility to serve as the internal standard for SERS spectral calibration. The adsorption capacity test validates the 71 successful synthesis of functional MIPs shell onto AuNPs surface for 2,4-D recognition and separation. Hence, the new designed AuNPs@MIPs functional material can be applied for 2,4-D detection by SERS. 3.5.2 3D-FDTD simulation for AuNPs@MIPs SERS substrate To investigate the SERS behavior of the synthesized AuNPs@MIPs functional material, 3D-FDTD model was performed to simulate the electric filed intensity distribution around the AuNPs@MIPs nanomaterials. In the model, the diameter of Au sphere for AuNPs@MIPs was 33.6 nm and the thickness of MIPs shell was 6.5 nm, which are consistent to the dimensions measured from the TEM images. The spatial electric field intensity (|E/E0|2) distributions calculated on the basis of two adjacent AuNPs@MIPs particles are shown in Figure 3.5. The maximal electric field intensities were obtained in the gap between the two AuNPs@MIPs particles because the localized surface plasmons from two adjacent nanostructures are strongly coupled, resulting in the electric field in the gap to be significantly enhanced. As a result, the scattered light from molecules absorbed in the gap would also be significantly enhanced (J. F. Li, Zhang, Ding, Panneerselvam, & Tian, 2017; Yi et al., 2015). In practical, AuNPs@MIPs were randomly deposited onto NC membrane and the distances between two nanoparticles may not be exact 0 nm. Then, the relationship between the gap distance and SERS enhancement was investigated. As shown in Figure 3.5, while the gap distance increased from 0 to 10 nm, the electric field intensities decreased gradually. To quantify the SERS enhancement, the enhancement factors (|E/E0|4) were also calculated on the basis of the FDTD simulation result using the method reported in a previous study (Ding et al., 2017). The highest simulated electric 72 field intensity was obtained when the gap distance was 0 nm, and the enhancement factor was as high as 5.85×106. Figure 3.5 3D-FDTD simulation results for the electric field intensity (|E/E0|2) distributions calculated at the excitation wavelength of 785 nm for AuNPs@MIPs with different gap distances: 0-10 nm (a-f). The diameter of AuNPs is 33.6 nm and the thickness of the MIPs shell is 6.5 nm. 73 3.5.3 SERS performance of AuNPs@MIPs for 2,4-D detection The sensing capacity of the as prepared AuNPs@MIPs SESR substrate was evaluated for both 2,4-D standard solutions and 2,4-D spiked milk samples. The AuNPs@MIPs nanoparticles were incubated together with different concentrations of either 2,4-D standard solutions or 2,4-D spiked milk sample extracts for 30 min to allow the MIPs to recognize and adsorb 2,4-D molecules. After simple wash with buffer solution to remove the interference molecules, AuNPs@MIPs was deposited on NC membrane and analyzed by Raman spectroscopy. Figure 3.6a shows the representative spectrum of 2,4-D normal Raman as well as SERS spectra for both 2,4-D standard solution (10 µg/mL) and 2,4-D spiked milk sample extract solution (30 µg/mL). Compared to the spectrum of AuNPs@MIPs, the distinct Raman peaks identified in both 2,4-D standard solution and milk extract solution at 594 cm-1 (δ(CC)ring out of plane), 855 cm-1 (ν(CC)ring + ν(C–O) + ν(C–Cl)), 1280 cm-1 (δ(CH)ring) and 1414 cm-1 (νs(COO−) + ν(CC) + ω(CH2)) are associated with the enhanced feature peaks of 2,4-D molecules adsorbed by AuNPs@MIPs. In addition, the intensities of peaks at 1005 cm-1, 1175 cm-1, and 1585 cm-1 for SERS spectra are higher than those in the spectrum of AuNPs@MIPs and increase along with the increase in 2,4-D concentration. As mentioned above, these three peaks are also the enhanced signals from 4-ATP, and the increase of intensities for these peaks is because the same chemical bonds (i.e., γ(CC) + γ(CCC), δ(CH)ring, and ν(CC)ring) also exist in 2,4-D molecule and have been enhanced by AuNPs@MIPs. At the highest tested concentration of 30 µg/mL, another two unique feature peaks at 1053 cm-1 (ν(CC)ring + ν(CO) + δ(CH)ring) and 1565 cm-1 (νas(COO−) + ν(CC)ring) were identified for 2,4-D in SERS spectra. The invisibility of these two peaks at low concentrations of 2,4-D should be attributed to the existence of the strong peak adjacent to each 74 of these two peaks that shield their signals. Only when the concentration of 2,4-D increased to specific levels, these two peaks appear from the shoulder of their adjacent peaks and become detectable. This result is in accordance with the general phenomenon that the peak intensities of SERS usually increase along with the increase in the concentration of the target compound. Besides, slight peak shifts were also observed for some of the 2,4-D feature peaks in SERS spectra compared with 2,4-D normal Raman spectra. Same phenomenon was identified in another study and had been attributed to charge transfers occur between nanoparticles and 2,4-D molecules (Costa et al., 2009). Detailed vibrational peak assignment for 2,4-D can be found in Table 3.1. 75 76 Figure 3.6 (a) Representative Raman spectra of AuNPs@MIPs (black), 2,4-D normal Raman (green), as well as SERS spectra of 2,4-D standard solution (10 µg/mL) (red) and 2,4-D (30 µg/mL) spiked milk sample (blue). (b-e) Reproducibility of the AuNPs@MIPs SERS substrate for 2,4-D detection. (b) Spectra of 10 µg/mL 2,4-D standard solution obtained at 10 different sites in three droplets. Normalized Raman intensities at 594 cm-1 (c), 1175 cm-1 (d), and 1280 cm-1 (e) collected from (b). (f) Representative normalized SERS spectra of 2,4-D standard solutions at different concentrations using AuNPs@MIPs as the substrate. To evaluate the reproducibility of AuNPs@MIPs substrate for SERS analysis, spectra of 10 µg/mL 2,4-D standard solutions were collected at 10 random sites in three droplets (as shown in Figure 3.6b). Figure 3.6(c-e) depicts the normalized Raman intensities (based on the peak at 1078 cm-1) of three Raman peaks at 594 cm-1, 1175 cm-1, and 1280 cm-1, respectively, from 10 collected spectra. The RSDs of the normalized SERS intensities for peaks at 594 cm-1, 1175 cm-1, and 1280 cm-1 were 9.57%, 7.80%, and 9.00%, respectively, indicating the relatively uniform distribution of 2,4-D molecules adsorbed by AuNPs@MIPs and excellent reproducibility of using the AuNPs@MIPs SERS substrate for 2,4-D determination. The RSDs of SERS intensities for the same peaks above but without peak normalization were also calculated as comparison. The results are presented in Figure 3.7 and the RSDs of intensities for peaks at 594 cm-1, 1175 cm-1, and 1280 cm-1 were 11.26%, 7.99%, and 11.29%, respectively. The relatively larger RSD values of original peak intensities compared to the RSDs of the corresponding normalized peak intensities indicate that the addition of 4-ATP as internal standard for SERS spectra calibration can improve the detection accuracy for SERS-based analytical method. The SERS enhancement 77 factor (EF) was calculated according the equation from a previous study (Le Ru, Blackie, Meyer, & Etchegoin, 2007): EF = (ISERS/cSERS)/(IRS/cRS), where IRS and ISERS are the intensities of specific peak under normal Raman and SERS condition, respectively. The cRS and cSERS represent the concentrations of analyte applied for normal Raman and SERS detection, respectively. The EFs for peaks at 594 cm-1, 1175 cm-1, and 1280 cm-1 were calculated to be 5.51×105, 5.20×105, and 9.35×105, respectively. These calculated enhancement factors are comparable to the theoretical results calculated on the basis of the 3D-FDTD model. Hence, both the theoretical EF and experimental EFs have validated the sensitive detection of 2,4-D using AuNPs@MIPs-based SERS method. 78 Figure 3.7 Original Raman intensities at 594 cm-1 (a), 1175 cm-1 (b), and 1280 cm-1 (c) collected from Figure 4b without peak normalization. To quantitatively analyze 2,4-D using our developed SERS method, a series dilution of 2,4-D standard solutions in the range of 0.1-30 µg/mL was prepared and detected using AuNPs@MIPs substrate. As shown in Figure 3.6f, the normalized Raman intensities of the peak at 1280 cm-1 for 2,4-D and the shared peaks at 1175 cm-1 and 1585 cm-1 for both 2,4-D and 4-ATP increased along with the increase in 2,4-D concentration. When the concentration of 2,4-D increased to 30 µg/mL, another two peaks at 1053 cm-1 and 1565 cm-1 for 2,4-D became detectable. However, no simple linear regression relationship was established within the tested concentration range based on the enhanced peak intensities (Figure 3.8), which maybe because of the heterogeneous distribution of the hotpots. As a result, chemometric models were developed to simplify data interpretation and quantify the concentrations of 2,4-D in samples. 79 Figure 3.8 Normalized peak intensity-concentration plot of the SERS spectra at 1175 cm-1, 1280 cm-1, and 1585 cm-1 (n=5). The unsupervised three-dimensional PCA model was firstly constructed based on the spectral features in the Raman shift range from 1020 cm-1 to 1800 cm-1 to distinguish 2,4-D at different levels. As shown in Figure 3.9a, all samples are tightly clustered and well separated on the basis of 2,4-D concentrations, indicating the high sensitivity of using AuNPs@MIPs as the SERS substrate for the detection of 2,4-D at different levels. PLSR model using the same Raman shift range as PCA model applied was then constructed to define a linear regression relationship between the concentrations of 2,4-D and the obtained corresponding SERS spectral features (Figure 3.9b). A 7-fold cross-validation was applied to test the reliability of the PLSR model 80 and the major parameters derived from this analysis are summarized in Table 3.2. The regression coefficients (R2) and root-mean-square errors (RMSE) for calibration model and cross-validation model are 0.9970, 0.9823, 0.6441, and 1.5324, respectively. These results demonstrate that the constructed PLSR model can accurately predict the concentrations of 2,4-D in the tested samples. Besides, the limit of detection (LOD) calculated on the basis of the PLSR model is 0.011 µg/mL (Allegrini & Olivieri, 2014), indicating the feasibility of using AuNPs@MIPs for sensitive and quantitative 2,4-D detection. 81 Figure 3.9 (a) Representative PCA score plot of 2,4-D standard solutions and 2,4-D spiked milk samples. For each concentration (represented by dots of the same color, black: 0 82 µg/mL; red: 0.1 µg/mL; green: 1 µg/mL; blue: 10 µg/mL; orange: 30 µg/mL; solid dot: standard sample; circle: milk sample), the 95% confidence ellipsoid is also shown. (b) Representative calibrated PLSR model for the prediction of 2,4-D concentrations using SERS spectral features (Raman shift range: 1020-1800 cm-1). Table 3.2 PLSR model for prediction of 2,4-D concentrations in standard samples and spiked milk samples using SERS spectral features based on Raman shift range from 1020 cm-1 to 1800 cm-1. Features value Na 40 nb 20 Number of latent variables 6 RMSECc 0.6441 R2-C 0.9970 RMSECVd 1.5324 R2-CV 0.9823 RMSEPe 1.3736 R2-P 0.9858 a N, number of spectra for calibration. b n, number of spectra for prediction. c RMSEC, root mean squares error of calibration. d RMSECV, root mean squares error of cross-validation (7-fold). e RMSEP, root mean squares error of prediction. 83 In parallel, 2,4-D spiked milk samples were also tested to investigate the practical performance of AuNPs@MIPs for SERS detection. Milk samples spiked with different concentrations of 2,4-D were simply extracted to remove proteins and lipids before SERS analysis. The average extraction recoveries of 2,4-D at different spiking levels in milk samples are summarized in Table 3.3. The good recoveries (>90% for all tested concentrations) indicate that the extraction method that we developed can separate most of the 2,4-D from contaminated milk samples and ensure the accuracy for quantitative 2,4-D detection. Representative SERS spectrum of 2,4-D spiked milk sample is shown in Figure 3.6a. Remarkably, the characteristic SERS peaks of 2,4-D were identified for 2,4-D spiked milk sample, and the relatively clean SERS spectrum indicates that the MIPs shell embedded on AuNPs surface has selectively and efficiently separated 2,4-D molecules from the complex milk matrix. Then, the performances of the constructed PCA and PLSR models for real food sample analysis were evaluated. As shown in Figure 3.9, all 2,4-D spiked milk samples (marked as circles) tested have been clustered based on the spiking levels and locate in the same concentration groups as 2,4-D standard samples in the PCA model. Besides, the constructed PLSR model also fit the milk samples well so as to predict the contamination level of 2,4-D in milk. Therefore, the performances of our developed AuNPs@MIPs-based SERS method for the detection and quantification of 2,4-D in milk have been validated. Table 3.3 Average extraction recoveries and relative standard deviations of 2,4-D at various spiking levels for milk sample. 84 Spiked concentration (mg/kg) Recovery (%) RSD % (n=3) 1 106.4 6.4 10 98.6 0.4 30 91.1 2.1 3.6 Conclusion In summary, we have successfully hybridized gold nanoparticles with an ultrathin layer of MIPs shell (~6.5 nm) as the functional substrate for highly selective and sensitive detection of 2,4-D in milk using SERS. The core-shell AuNPs@MIPs hybrids were controllably synthesized to allow selective separation and enrichment of 2,4-D molecules within the near surface of AuNPs. By controlling the distance between AuNPs and 2,4-D molecules adsorbed by MIPs, highly sensitive SERS determination of 2,4-D was achieved. Embedding SERS active molecules 4-ATP within MIPs shell as internal reference standard further improved the reproducibility and accuracy for SERS detection. Our new developed strategy provides a simple and reliable solution for hybridizing selective MIPs material with gold nanoparticles as functional SERS substrate for selective, sensitive, and quantitative SERS detection. This proposed MIPs-SERS method also shows considerable potential to determine chemical hazards in foods without complicated sample preparation, making the analysis much simpler and faster. 85 Chapter 4: Microfluidic-assisted rapid detection of chemical contaminant in food using quantum dots-based molecular imprinting sensor 4.1 Introduction Food chemical contamination is a matter of serious concern for food industry as the high amounts of chemicals remained in foods may pose negative implications to human health. There are various types of chemical contaminants (e.g., veterinary drugs, mycotoxins, pesticides, environmental contaminants, food additives, etc.) that enter the food chain either accidentally or intentionally, resulting in foodborne illnesses with symptoms ranging from mild gastroenteritis to fatal renal, hepatic, and neurological syndromes (Health Canada, 2014; Kamel & Hoppin, 2004; Ratnaseelan et al., 2018). Monitoring the entire process of food production, processing, and distribution from farm to table is of great significance to reduce dietary exposure to food hazards. Traditional detection technologies are based on chromatography, mass spectrometry, and immunoassays, which are accurate but have limitations of high cost, large material consumption and complicated operation procedures (Chiou et al., 2015). In addition, direct analysis of specific chemical contaminant(s) in food matrices requires cumbersome and time-consuming sample pretreatment, separation, and detection procedures. Therefore, development of low-cost, rapid, convenient, and efficient analytical methods for sensitive determination of chemical contaminants in food is urgently needed. In recent years, molecularly imprinted polymers (MIPs) have been investigated for the analysis of specific targets in complex food matrices (Tang et al., 2015; Xiao, Zhang, Wu, Han, & Zhang, 86 2017; Yuan et al., 2014). Typical MIPs materials are polymerized using functional monomers and crosslinking agents in the presence of temple or dummy template molecules. After removal of templates, recognition sites with memory of size, shape and functional groups of the template molecules are created, providing MIPs with unique advantages such as good chemical stability, excellent recognition specificity, and high selectivity (BelBruno, 2019; W. Chen et al., 2015). To endow MIPs with sensing capacity, different studies were developed to integrate semiconductor quantum dots (QDs) with MIPs as fluorescence sensors for the detection of chemical contaminants in foods (Fang et al., 2014; H. Liu et al., 2016; The Huy, Seo, Zhang, & Lee, 2014; S. Xu & Lu, 2015). Compared to traditional fluorescence dyes, QDs possess excellent optical properties such as good photostability, bright photoluminescence, narrow emission and broad excitation (Hildebrandt et al., 2017). Application of QDs@MIPs sensors for sensitive detection of targeted analytes takes advantage of fluorescence quenching effect of QDs that is induced by either energy transfer or electron transfer mechanisms (H. Liu et al., 2014; S. Xu et al., 2013). Microfluidic technology has features of miniaturization, automation, rapid and high-throughput sample analysis, but its applications in food analysis are still in the infancy due to the complexity of food matrices (Crevillen, Avila, Pumera, Gonzalez, & Escarpa, 2007). It is also challenging to integrate the processes of sample pretreatment, separation and detection in one micro-device for the determination of food hazards. Different types of microdevices have been designed and explored to separate circulating tumor cells (Warkiani et al., 2014), blood cells (Kuan, Wu, Su, & Huang, 2018), spores (Inglis, Herman, & Vesey, 2010), and bacteria (Z. Wu, Willing, Bjerketorp, Jansson, & Hjort, 2009). Among them, spiral inertial separation microfluidic device demonstrates excellent performance of sized-based particle separation. Generally, the dominant 87 inertial lift drag forces coupled with the Dean drag force due to curvilinear microchannel geometry equilibrate particles with different sizes at different positions in the microchannel, contributing to rapid and high-throughput separation of microparticles with outstanding efficiency (Bhagat, Kuntaegowdanahalli, & Papautsky, 2008; Kuntaegowdanahalli, Bhagat, Kumar, & Papautsky, 2009). However, there is still a lack of strategies for applying functional microfluidic devices to assist in the separation of chemical contaminants from complex food matrices and realize sensitive target analysis. Herein, we proposed a novel analytical approach to integrate the processes of sample pretreatment, analyte separation and detection on a single microfluidic platform to realize one-step analysis of chemical contamination in food. Enrofloxacin, a widely used antibiotic to treat animals with certain bacterial infections in the livestock industry, was selected as the detection template to evaluate the performance of the proposed analytical approach. To the best of our knowledge, the inertial separation microchannel was first time applied in food analysis for convenient, rapid, and high-throughput chemical contaminants analysis. Fluorescent QDs@MIPs particles with desired sizes were synthesized as both separation element to be applied on the microfluidic chip and sensing material for enrofloxacin detection. The fabricated microfluidic device was well characterized and its separation efficiency was evaluated. QDs@MIPs sensor was also characterized and its sensing property was investigated in details. Finally, sensing system that incorporated microfluidic chip and QDs@MIPs was applied for rapid separation as well as selective and sensitive determination of trace level enrofloxacin in swine muscle. 88 4.2 Hypotheses H1: Specific MIPs material for enrofloxacin recognition can be synthesized and coated onto the surface of silica spheres with QDs embedded inside. H2: QDs@MIPs-based fluorescence sensor can be applied to detect enrofloxacin. H3: Inertial separation microfluidic device can be designed for the separation of micro-size QDs@MIPs particles from one liquid flow to another, and the two liquid flows will transfer in parallel in the spiral microchannel without mixing. H4: Sensing system that incorporated QDs@MIPs-based fluorescence particles with inertial separation microfluidic device can be applied to selectively separate enrofloxacin from swine muscle and achieve sensitive determination. 4.3 Objectives O1: To synthesize micro-size sore/shell structure MIPs particles for enrofloxacin with fluorescent QDs embedded inside the MIPs shell. O2: To develop a QDs@MIPs-based fluorescence sensor for enrofloxacin detection. O3: To design and fabricate an inertial separation microfluidic device for QDs@MIPs particles separation. 89 O4: To incorporate QDs@MIPs-based fluorescence sensor with inertial separation microfluidic device to selectively separate and detect enrofloxacin in swine muscle. 4.4 Material and methods 4.4.1 Reagents and Materials Enrofloxacin, (3-aminopropyl)trimethoxysilane (APTES), tetraethyl orthosilicate (TEOS), L-Glutathione reduced (GSH), tetramethylammonium hydroxide (TMAH) solution in methanol (25%, w/v), sodium borohydride, sodium cyanoborohydride, methanesulfonic acid (70%), acetone, acetonitrile (ACN), and silica spheres (5-13 μm) were purchased from Sigma-Aldrich (St. Louis, MO, USA). CdSe1-xSx/ZnS core/shell nanocrystals (dispersed in toluene) with emission maxima at 630 nm (QDs) were acquired from Cytodiagnostics (Burlington, ON, Canada). Dimethylformamide (DMF), ammonium hydroxide (NH4OH, 30%), potassium carbonate (K2CO3) and sodium chloride (NaCl) were obtained from Fisher Scientific (Ottawa, ON, Canada). Ultrapure water (18.2 MΩ·cm) was prepared using a Milli-Q purification system and applied throughout the experiment. Buffer solutions included borate buffer-Ⅰ (BB-Ⅰ, 10 mM, pH 8.5, 50 mM NaCl) and borate buffer-Ⅱ (BB-Ⅱ, 50 mM, pH 9.2). All chemicals used in this work were at least of analytical grade and applied without further purification. Swine muscle samples were obtained from local grocery stores in Vancouver, Canada. 4.4.2 Characterizations QDs@MIPs fluorescent sensing material was characterized using a Zeiss Axio Imager M2 microscope equipped with an ORCA-Flash4.0 LT+ Digital CMOS camera (Hamamatsu, Japan) and a Zeiss Plan-Apochromat 100× oil immersion objective lens. Images of silica spheres, 90 QDs@MIPs and QDs@NIPs under fluorescence mode and differential interference contrast (DIC) mode were captured. The images were then overlaid to create composite images to indicate the wrapping of QDs-embedded MIPs shell on silica spheres. All fluorescence measurements were performed using an Infinite M200 Pro multifunctional microplate reader (Tecan, Morrisville, NC, USA). UV-vis absorption spectra were recorded using a Shimadzu UV-1800 spectrophotometer (Shimadzu Inc., Japan). Flow dynamic of fluids that passed through the spiral microchannel were characterized by introducing two flows of food dye with different colors into the microfluidic device. To experimentally evaluate the separation efficiency of the inertial separation microfluidic device, silica spheres suspended in DI water and DI water only were introduced into the spiral microchannel using syringe pumps at the flow rate of 2 µL/min. Solutions came out from the two outlets were separately collected and imaged using an Olympus SZX10 Stereomicroscope equipped with a 20× objective lens to enumerate the number of separated particles. 4.4.3 Design and fabrication of inertial separation microfluidic device Figure 4.1 illustrates the design of spiral microchannel that depends on fluid inertia for the separation of particles based on their sizes. The device design consists of three connected serpentine microchannels with two inlets for the injection of particle suspension and sample solution, jointed with a 5-loop spiral microchannel, and a 1:1 splitter connected with two outlets at the outer periphery of the spiral channel. Besides, a third inlet was reserved for introducing buffer solution, and the connected channel was merged with the serpentine channel in front of the spiral region. The width of either serpentine channel or buffer introducing channel is 250 µm. The width of spiral channel is 500 µm with 1 mm spacing between two successive loops. The 91 initial radius of curvature of the spiral is 10 mm. The height of microchannel is 130 µm. This designed spiral inertial separation microfluidic system was fabricated using the standard soft lithography technique (Qin, Xia, & Whitesides, 2010). 4.4.4 Preparation of GSH-coated QDs Water-soluble QDs was prepared according to a previously reported ligand exchange method with some modifications (M. Wu & Algar, 2015). Briefly, the as-received alkyl-capped QDs in toluene (100 μL, ~5 μM) were diluted with 900 μL of chloroform and then mixed with 50 mg of GSH in 150 μL of TMAH in a microcentrifuge tube. The mixture was gently shaken for mixing and let stand overnight at room temperature. Subsequently, GSH-coated QDs were extracted with 200 μL of BB-Ⅰ and the organic layer was discarded, followed by precipitation with ethanol (1,503 ×g, 5 min). Then, QDs were redispersed in 200 μL of BB-Ⅰ and washed with ethanol for another twice. Finally, the pellet of QDs was redispersed in 200 μL of BB-Ⅱ. After quantification using UV-Vis spectrophotometry, GSH-coated QDs solution was diluted to the concentration of 5 μM and stored at 4˚C (W. W. Yu, Qu, Guo, & Peng, 2003). 4.4.5 Preparation of fluorescent QDs@MIPs sensing particles Micro-size QDs@MIPs sensing particles were synthesized by coating a layer of MIPs shell on the surface of activated silica spheres with CdSe1-xSx/ZnS QDs embedded inside. Firstly, 2.7 g of silica spheres were activated in boiling methanesulfonic acid (8%, v/v) for 8 hours. After washed with water and ethanol, the silica spheres were dried at 60˚C in a vacuum oven. To wrap the MIPs shell onto the surface of silica spheres, 50 mg of activated silica spheres were suspended in 50 mL of DMF. Then, 18 mg of enrofloxacin and 45 μL of APTES were separately added and 92 stirred for 30 min. Subsequently, 50 μL of GSH-coated QDs was added, followed by addition of 50 μL of TEOS. Then, 200 μL of NH4OH was added to initiate the polymerization of MIPs. After polymerization, enrofloxacin templates in the MIPs shell were extracted by washing with acetone and ACN until no template was detected. Finally, the fluorescent QDs@MIPs particles were obtained after drying at 40˚C overnight in a vacuum oven. In comparison, non-imprinted polymers based on QDs (QDs@NIPs) were prepared following the same procedure as aforementioned but without the addition of template molecules (enrofloxacin). 4.4.6 Adsorption performance of QDs@MIPs Adsorption capacities of QDs@MIPs and QDs@NIPs were compared as follows: 250 μL of either QDs@MIPs or QDs@NIPs suspension (2 mg/mL in 50% ACN) was mixed with equal volume of enrofloxacin solution (dissolved in 50% ACN) with various concentrations. After shaking at room temperature for 2 hours, the mixtures were centrifuged at 3,381 ×g for 5 min. Then, the supernatants were collected and passed through a 0.2-μm nylon syringe filter before HPLC analysis was conducted to confirm the adsorption capacities (Q) for both QDs@MIPs and QDs@NIPs. An Agilent 1260 Infinity HPLC system coupled with a diode array detector (DAD) with the detection wavelength fixed at 280 nm was applied in this study. A volume of 20 μL of samples was injected into a Kinetex C18 column (3.00 × 100 mm, 2.6 µm). Mobile phase was a mixture of (A) 0.1% formic acid aqueous solution and (B) 0.1% formic acid in ACN and delivered at flow rate of 0.5 mL/min. A gradient program was used as 0 min, 15% B; 2 min, 15% B; 4 min, 30% B; 6 min, 95% B; 12 min, 95% B. The 𝑄𝑄 values were calculated according to the following equation: 𝑄𝑄 = (𝑐𝑐0 − 𝑐𝑐𝑡𝑡) × 𝑉𝑉 𝑊𝑊⁄ , where, 𝑐𝑐0 and 𝑐𝑐𝑡𝑡 are the initial and finial 93 concentrations of enrofloxacin in the mixture, respectively. 𝑉𝑉 is the volume of enrofloxacin solution and 𝑊𝑊 is the weight of either AuNPs@MIPs or AuNPs@NIPs. 4.4.7 Fluorescence measurement In the current study, all the fluorescence measurements were performed under the same conditions. Specifically, the excitation wavelength was set at 383 nm and the recording emission range was 550-700 nm. In preparation, 100 µL of QDs@MIPs suspension and 100 µL of a given concentration of enrofloxacin solution were added into a glass tube sequentially. The mixture was shaken at room temperature for 30 min, transferred into a 96-well plate, and measured using a multifunctional microplate reader. In the meanwhile, fluorescence spectra of QDs@NIPs under the same condition were also recorded as comparison. 4.4.8 Swine muscle sample preparation and enrofloxacin detection using microfluidic device Ground swine muscle obtained from local grocery stores in Vancouver was determined to be free of enrofloxacin and used for enrofloxacin spiking. Typically, 10 g of ground swine muscle was weighted in a 50-mL centrifuge tube, followed by spiking with enrofloxacin at different levels: 0, 0.5, 1, 5, and 10 µg/g, respectively. Then, 20 mL of ACN was added and the mixture was shaken at 200 rpm for 1 hour. After centrifugation at 1,503 ×g for 10 min, the supernatant (10 mL) was transferred to a new tube with addition of 100 µL of K2CO3 solution (4 M) and 10 mL of hexane. After being vortexed for 2 min, the mixture was centrifuged at 1,503 ×g for 10 min. Finally, the ACN phase was collected and stored at 4˚C for further analysis. 94 To perform on-chip separation of enrofloxacin, QDs@MIPs suspension, enrofloxacin standard solution or swine muscle extract, and a buffer solution of 20% ACN were separately introduced into inertial separation microfluidic device via the inlets as described in Figure 4.1. QDs@MIPs suspension and enrofloxacin sample were injected at the flow rate of 1 µL/min. In the meanwhile, buffer solution was introduced into the microchannel at the flow rate of 2 µL/min. Fluid flowed from the inner outlet was collected and analyzed using a multifunctional microplate reader after incubation at room temperature for 30 min. 4.4.9 Data analysis Along with the Zeiss Zen2.3 pro software (blue edition), ImageJ (Fiji) software was applied for post image processing. Fluorescence results were analyzed using OriginPro software (OriginLab Corporation, Northampton, MA, USA). Student’s t-test was applied to evaluate the significant difference between samples. All measurements were conducted at least in triplicate. 4.5 Results and discussion 4.5.1 Design principle In fluidic mechanics, the flow of a fluid is usually characterized by the Reynolds number that is defined as 𝑅𝑅𝑅𝑅 = 𝜌𝜌𝑈𝑈𝑓𝑓𝐷𝐷ℎ 𝜇𝜇⁄ , where 𝜌𝜌 is the fluid density, 𝑈𝑈𝑓𝑓 is the average fluid velocity, 𝜇𝜇 is the fluid viscosity, and 𝐷𝐷ℎ is the hydraulic diameter of microchannel [𝐷𝐷ℎ = 2𝑤𝑤ℎ (𝑤𝑤 + ℎ)⁄ , with channel width 𝑤𝑤 and height ℎ]. Due to the mall dimensions of microchannels, 𝑅𝑅𝑅𝑅 is usually very small that implies fluids flowing through the microchannels as laminar flow (Morini, 2004; Squires & Quake, 2005). A previous study validated that it is possible to allow two or more layers of fluids flowing next to each other without any interference caused by the diffusion of 95 their constituent components inside the microchannel by taking advantage of laminar flow conditions (Kane, Takayama, Ostuni, Ingber, & Whitesides, 1999). Besides, the nature of laminar flow also provides a means by which molecules or particles can be transported via microchannels in a relatively predictable manner (Garimella & Sobhan, 2003; Mehendale, Jacobi, & Shah, 2000; S. Z. Zhang, Wang, Onck, & den Toonder, 2020). In a curved rectangular microchannel, secondary flow arises because of the mismatch of velocity in the downstream direction between fluid in the center and near-wall regions of the microchannel, leading to the formation of two counter-rotating vortices known as Dean vortices located in the top and bottom halves of the microchannel (as illustrated in Figure 4.1a) (Berger, Talbot, & Yao, 1983; Bhagat et al., 2008; Di Carlo, 2009; Nivedita, Ligrani, & Papautsky, 2017). The magnitude of Dean vortices is quantified by a dimensionless Dean number (𝐷𝐷𝑅𝑅) given by: 𝐷𝐷𝑅𝑅 = 𝜌𝜌𝑈𝑈𝑓𝑓𝐷𝐷ℎ𝜇𝜇�𝐷𝐷ℎ2𝑅𝑅= 𝑅𝑅𝑅𝑅�𝐷𝐷ℎ2𝑅𝑅 (1) , where 𝑅𝑅 is the radius of curvature of the microchannel. Particles suspended in fluid flowing in a curvilinear microchannel experience a Dean drag force (𝐹𝐹𝐷𝐷) due to the transverse Dean vortices that entrain and drive them towards either the inner or outer wall of the microchannel depending on the size of the particles (Russom et al., 2009). The expression for 𝐹𝐹𝐷𝐷 can be determined by assuming a Stokes drag force (Bhagat et al., 2008; Ookawara, Street, & Ogawa, 2006): 𝐹𝐹𝐷𝐷 = 3𝜋𝜋𝜇𝜇𝑈𝑈𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑎𝑎𝑝𝑝 = 5.4 × 10−4𝜋𝜋𝜇𝜇𝐷𝐷𝑅𝑅1.63𝑎𝑎𝑝𝑝 (2) , where 𝑈𝑈𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 is the average Dean velocity for a given 𝐷𝐷𝑅𝑅 and is described as 𝑈𝑈𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 =1.8 × 10−4𝐷𝐷𝑅𝑅1.63, 𝑎𝑎𝑝𝑝 is the diameter of particle. 96 In addition to the Dean drag force (𝐹𝐹𝐷𝐷), particles in a curvilinear microchannel also experience shear-induced inertial lift force to drive particles away from the channel center toward the channel walls and wall-induced lift force to direct particles away from the walls, which equilibrate the particles to four positions within the microchannel where the inertial lift forces balance each other (Figure 4.1a) (Asmolov, 1999; Di Carlo, Irimia, Tompkins, & Toner, 2007; Russom et al., 2009). Combination of these opposing inertial lift forces is also described as the net lift force (𝐹𝐹𝐿𝐿) and its magnitude is expressed as (Asmolov, 1999; Kuntaegowdanahalli et al., 2009): 𝑭𝑭𝑳𝑳 = 𝝆𝝆𝑮𝑮𝟐𝟐𝑪𝑪𝑳𝑳𝒂𝒂𝒑𝒑𝟒𝟒 (3) , where 𝐺𝐺 is the shear rate of the fluid (the average 𝐺𝐺 value is given by 𝐺𝐺 = 𝑈𝑈𝑚𝑚𝐷𝐷𝑚𝑚/𝐷𝐷ℎ, with 𝑈𝑈𝑚𝑚𝐷𝐷𝑚𝑚 as the maximum fluid velocity and generally approximated as 2 × 𝑈𝑈𝑓𝑓); 𝐶𝐶𝐿𝐿 is the lift co-efficient, which is a function of the particle position across the microchannel cross-section and the channel Reynolds number (𝑅𝑅𝑅𝑅). Taken together, the equilibrium positions of given particles in the microchannel with curvilinear geometry are determined by the interplay between net lift force (𝐹𝐹𝐿𝐿) and Dean drag force (𝐹𝐹𝐷𝐷), which reduces the four equilibrium positions described above to two vertically aligned positions located at the same distance from the inner wall of the microchannel (viewed as a single stream across the microchannel) (Figure 4.1b) (Bhagat et al., 2008; Warkiani et al., 2016). Therefore, the position at which particles of different sizes equilibrate is contingent on the equilibrium between Dean drag force and lift forces, which vary with the particle diameters (Di Carlo, Edd, Irimia, Tompkins, & Toner, 2008; Di Carlo et al., 2007). 97 By taking advantages of 1) the nature of laminar flow in microchannel to allow two layers of fluids flow side-by-side without mixing in a single channel, 2) inertial lift forces acting on particles in a flow, and 3) Dean drag force due to the curvilinear microchannel structure, a spiral microchannel was designed in the current study to transfer QDs@MIPs particles with suitable diameters from fluid near the outer wall (food matrix) to the fluid near the inner wall (buffer solution). Accordingly, separation of chemical contaminants from food matrix can be achieved with the migration of QDs@MIPs in the microchannel. Liquid food sample (or solid food extract) and QDs@MIPs suspension were introduced into the microchannel via two outer inlets and buffer solution was introduced into microchannel via inner inlet as described in Figure 4.1a. Fluorescent QDs@MIPs sensing particles were tailor-made with desirable dimeter to selectively capture and separate target chemical compounds (i.e., enrofloxacin) from complex food matrix. Subsequently, QDs@MIPs and contaminated food matrix were well mixed in the serpentine region, allowing selective recognition of target compounds by QDs@MIPs. When the mixture and buffer solution flowed through the spiral microchannel, QDs@MIPs particles migrated from the mixture of sample-QDs@MIPs into buffer solution, but the two fluids were not mixed together. As a consequence, target chemical contaminants can be selectively separated from complex food matrix. Coupled with sensing property of QDs based on its fluorescence quenching effect, it is feasible to achieve rapid, high-throughput separation as well as selective and sensitive determination of chemical contaminants in foods. 98 Figure 4.1 (a) Schematic illustration of the proposed inertial microfluidic device for separation of enrofloxacin using QDs@MIPs particles. Particles that flow through the sprial microchannel experience net lift force (𝑭𝑭𝑳𝑳) and Dean drag force (𝑭𝑭𝑫𝑫), resulting in equilibrating particles at different positions along the inner wall of the microchannel depending on the particle sizes. Outer inlet 1: sample inlet; outer inlet 2: QDs@MIPs suspension inlet; inner inlet: buffer solution inlet. (b) Microchannel cross-sections illustrating the principle of inertial separation for microparticles. Randomly dispersed particles align in the four equilibrium positions within the microchannel where the lift forces balance each other. The addition of Dean force reduce the four equilibrium postions to two vertically aligned positions (viewed as a single stream across the microchannel) near the inner wall of the microchannel. 99 4.5.2 Characterization of the inertial separation microfluidic device The performance of spiral microchannel was firstly demonstrated by introducing two flows of food dye solutions with distinct colors into the microfluidic device via inner inlet and one of the outer inlets, respectively (Figure 4.2a). Two individually injected fluids merged in spiral microchannel, passed through spiral microchannel in parallel without mixing, and finally came out from two separate outlets. This result demonstrates that, under current experimental condition, fluids flowed through microchannel were transferred as laminar flows. To evaluate particle separation efficiency of spiral microchannel, commercially available silica spheres (also applied as the core for MIPs shell wrapping) were suspended in DI water and introduced into microfluidic device via one of the outer inlets, while the inner inlet was injected with DI water. Images of silica spheres collected from the two outlets were captured for particle enumeration (Figure 4.2b). Although particles were introduced into spiral microchannel with the flow near the outer wall, most particles were equilibrated to the position near the inner wall by the time they reached to the end of the microchannel and were collected at the inner outlet. Particles counted at inner outlet were 97.7±1.6% of total particles collected at both outlets, indicating excellent separation efficiency of spiral microchannel on particles with given diameters. 100 Figure 4.2 (a) Experimental confirmation of two layers of fluids that flow through the spiral microchannel in parallel and come out from two outlets without mixing. (b) Representative images of silica spheres collected from the inner and outer outlets. 4.5.3 Preparation and characterization of fluorescent QDs@MIPs sensing spheres As illustrated in Figure 4.3a, enrofloxacin-imprinted QDs@MIPs fluorescent sensing material was designed using commercially available silica sphere as the core, followed by coating a layer of QDs-embedded fluorescent MIPs shell on its surface. To improve shell wrapping efficiency, 101 silica spheres were firstly activated in boiling methanesulfonic acid solution for 8 hours. Then, enrofloxacin-imprinted MIPs shell with CdSe1-xSx/ZnS QDs embedded inside as fluorophore was polymerized onto the surface of silica spheres using enrofloxacin as template molecule, APTES as functional monomer and TEOS as crosslinking agent. Prior to removal of enrofloxacin templates, no fluorescence signal was observed for QDs@MIPs under UV light excitation in comparison to the strong red fluorescence signal observed for QDs@NIPs (Figure 4.3b). This phenomenon indicates that the adsorption of enrofloxacin molecules onto QDs@MIPs has induced fluorescence-quenching effect to QDs, which is attributed to electron transfer induced fluorescence-quenching mechanism (H. Liu et al., 2014; J. L. Yu et al., 2017). Finally, after removal of templates from QDs@MIPs, fluorescence signal of QDs@MIPs was recovered, leaving cavities with specific recognition sites for enrofloxacin rebinding. 102 Figure 4.3 (a) Schematic illustration for the preparation of micron-size QDs@MIPs particles and the sensing mechanism for enrofloxacin detection. (b) Images of QDs@MIPs and QDs@NIPs under UV light before and after the removal of template molecules. Morphological structures of silica spheres, QDs@MIPs and QDs@NIPs were investigated using a fluorescence microscope and images are presented in Figure 4.4. No distinct difference between bare silica spheres, QDs@MIPs particles, and QDs@NIPs particles was identified under DIC mode. When UV light was applied to the same particles for excitation, red fluorescence signal (emission at 630 nm) was observed on QDs@MIPs and QDs@NIPs but not bare silica spheres, indicating successful embedding of QDs in QDs@MIPs and QDs@NIPs particles. Besides, a shell structure was coated on each sphere with a higher fluorescence intensity than the core, suggesting successful wrapping of QDs-embedded MIPs shell on silica spheres. Consistent fluorescence distribution around silica spheres suggests uniform distribution of QDs among shell structure that contributes to a high sensitivity of QDs@MIPs fluorescence sensor. 103 Figure 4.4 Fluorescence microscopy analysis of silica spheres, QDs@MIPs, and QDs@NIPs. Left to right: differential interference contrast (DIC), fluorescence (QDs, emission at 630 nm), and merged microscopic images. 4.5.4 Adsorption capacity of QDs@MIPs to enrofloxacin To evaluate binding performance of QDs@MIPs and QDs@NIPs to enrofloxacin, an equilibrium adsorption analysis was carried out using enrofloxacin standard solutions with different concentrations. As shown in Figure 4.5, both QDs@MIPs and QDs@NIPs have binding 104 capacity to enrofloxacin at all tested concentrations. Along with the increase in enrofloxacin concentrations, Q values increased correspondingly. Typically, QDs@MIPs have higher adsorption capacities to enrofloxacin compared with QDs@NIPs at each tested concentration. When the concentration of enrofloxacin increased to 10 µg/mL, Q value for QDs@MIPs was 1.29±0.05 µg/mg, which was ~1.4 times of that for QDs@NIPs. Taken together, MIPs were successfully synthesized with selective affinity towards enrofloxacin. Figure 4.5 Characterization of the adsorption capacities (Q) of QDs@MIPs and QDs@NIPs towards enrofloxacin. 105 4.5.5 Performance of QDs@MIPs for enrofloxacin detection To investigate sensing property of QDs@MIPs-based fluorescence sensor, enrofloxacin standard solutions were prepared and incubated with QDs@MIPs suspension for fluorescence quenching. Fluorescence intensities of the mixtures were measured using a multifunctional microplate reader and plotted against enrofloxacin concentrations ranging from 0 to 50 µg/mL (Figure 4.6). Fluorescence intensity of QDs@MIPs was dramatically quenched with the increase in enrofloxacin concentration. Although fluorescence intensities of QDs@NIPs were also quenched in the presence of enrofloxacin, the quenching levels were not comparable to that of QDs@MIPs at the same concentration of enrofloxacin. Because QDs@MIPs particles were tailor-synthesized in the presence of enrofloxacin as the template, a tremendous number of binding sites with selective affinity to enrofloxacin were imprinted on QDs@MIPs in comparison to QDs@NIPs. Hence, QDs@MIPs can capture more enrofloxacin molecules at a given concentration and contribute to a higher fluorescence quenching amount. As a result, the sensitivity of using QDs@MIPs for enrofloxacin detection is higher than that of QDs@NIPs. Fluorescence quenching in this study followed the Stern-Volmer equation (Eftink & Ghiron, 1981): 𝑭𝑭𝟎𝟎 𝑭𝑭⁄ = 𝟏𝟏 + 𝑲𝑲𝑺𝑺𝑺𝑺𝑪𝑪𝒒𝒒 (4) , where 𝐹𝐹0 and 𝐹𝐹 are fluorescence intensity of the mixture in the absence and presence of enrofloxacin, respectively. 𝐾𝐾𝑆𝑆𝑆𝑆 is the Stern-Volmer quenching constant, and 𝐶𝐶𝑞𝑞 is the concentration of enrofloxacin. As shown in Figure 4.6c, the Stern-Volmer plots of QDs@MIPs exhibited an excellent linear relationship over the concentrations of enrofloxacin ranging from 0.1 to 50 µg/mL with a correlation coefficient of 0.9952. The limit of detection (LOD) based on 3σ was calculated to be 0.059 µg/mL, which was lower than the maximum residue limit (MRL) of 0.1 µg/mL for swine muscle regulated by Health Canada (Health Canada, 2021). The 106 precision for five replicate measurements of 50 µg/mL enrofloxacin was 3.8% relative standard deviation (RSD). Therefore, the developed QDs@MIPs-based fluorescence sensor holds a great potential to determine trace level enrofloxacin in foods. Figure 4.6 Representative fluorescence emission spectra of (a) QDs@MIPs and (b) QDs@NIPs with the addition of enrofloxacin standard solutions with different concentrations (0 - 50 µg/mL). (c) The Stern-Volmer plots for QDs@MIPs and QDs@NIPs. (n=4) 107 4.5.6 Microfluidic-assisted enrofloxacin detection and food samples analysis To evaluate the performance of combining QDs@MIPs-based fluorescence sensor with spiral microchannel for enrofloxacin separation and detection, enrofloxacin standard solutions with different concentrations, QDs@MIPs suspension, and a buffer solution were separately introduced into the spiral microchannel. As aforementioned, QDs@MIPs suspension and enrofloxacin solution were well mixed in the serpentine region, allowing QDs@MIPs particles to capture enrofloxacin molecules from the solution. Then, the flow of the mixture passed through spiral microchannel in parallel with the flow of buffer solution without any mixing. However, QDs@MIPs particles flowing through the spiral microchannel were subjected to the Dean drag force and inertial lift forces and migrated from sample flow into buffer flow and finally came out from the inner outlet. The mixtures collected from inner outlet were incubated at room temperature for 30 min to allow fluorescence signals to reach the equilibrium conditions. Then, fluorescence intensities of QDs@MIPs were measured and fluorescence quenching amounts were calculated (Figure 4.7). Fluorescence quenching amounts of QDs@MIPs gradually increased along with the increase in enrofloxacin concentration, indicating the feasibility of combining spiral microfluidic device with QDs@MIPs-based sensor for rapid separation of enrofloxacin from food matrix followed by a sensitive fluorescence determination. 108 Figure 4.7 Fluorescence quenching amounts of QDs@MIPs after passing through the spiral microchannel. Enrofloxacin standard solutions or enrofloxacin spiked swine muscle sample extractions were injected for MIPs adsorption and fluorescence quenching. (n=5) To further investigate the practical applicability of microfluidic-assisted QDs@MIPs sensing system, swine muscle samples spiked with different levels of enrofloxacin were prepared and extracted using a simple solid-liquid extraction method. Average extraction recoveries of enrofloxacin from swine muscle samples were obtained based on triplicate measurements for each spiking level. As summarized in Table 4.1, satisfactory recoveries for enrofloxacin were 109 obtained for spiked swine muscle samples, indicating that the extraction could separate most enrofloxacin from swine muscle and ensure accurate determination of trace level enrofloxacin. Since extraction applied is an ordinary solid-liquid extraction method that lacks selectivity towards enrofloxacin, interfering residues extracted from swine muscle could not be avoided and would potentially affect detection accuracy of QDs@MIPs sensor. By performing on-chip separation, enrofloxacin molecules were selectively adsorbed by QDs@MIPs particles and separated from the complex swine muscle extract to buffer solution. In this study, buffer solution applied not only worked as holding fluid for QDs@MIPs, but also served as washing buffer to clean up the interfering molecules that attached on the surface of QDs@MIPs particles via non-specific binding. Because only molecules adsorbed by QDs@MIPs via specific chemical bond reaction can induce fluorescence quenching effect to QDs, the eluted free molecules of interfering compounds in buffer solution could not contribute to any fluorescence quenching. Therefore, detection accuracy of using QDs@MIPs-based sensor for the analysis of trace level enrofloxacin in swine muscle was further improved. Fluorescence quenching amounts of QDs@MIPs for spiked swine muscle samples increased along with the increase in enrofloxacin spiking level and most values were comparable to the results obtained for enrofloxacin standard solutions at the same concentrations (Figure 4.7). When the spiking level of enrofloxacin in swine muscle was 10 µg/g, fluorescence quenching amount was lower than that of enrofloxacin standard solution probably due to low extraction efficiency for swine muscle spiked with a high level of enrofloxacin. Overall, the proposed method combines inertial separation microfluidic chip with QDs@MIPs-based fluorescence sensor and provides a novel strategy for rapid separation as well as selective and sensitive determination of enrofloxacin in food. 110 Table 4.1 Average extraction recoveries and relative standard deviations of enrofloxacin at various spiking levels for swine muscle samples. Spiked concentration (mg/kg) Recovery (%) RSD % (n=3) 0.5 110.0 8.1 1 110.2 6.3 5 103.2 9.6 10 89.0 5.0 4.6 Conclusion Inertial separation microfluidic device was investigated for the first time for rapid separation as well as selective and sensitive detection of chemical contaminant in food. Spiral microchannel was designed according to the principle of laminar flow and Dean-coupled inertial particle migration in a curvilinear microchannel. Fluorescent QDs@MIPs sensing particles with desired diameters were synthesized to achieve selective recognition and separation of enrofloxacin from contaminated swine muscle. Based on electron-transfer induced fluorescence quenching mechanism, QDs@MIPs-based sensor has demonstrated highly selective recognition and sensitive detection performance towards enrofloxacin with satisfactory accuracy and practicality. This inertial separation microfluidic device was well designed and holds the potential to separate other chemical hazards and bacteria from complex food matrices and achieve high-throughput screening. 111 Chapter 5: Conclusion Food chemical contamination is one of the major food safety concerns worldwide. Sensitive and accurate analytical methods for chemical hazards determination is of great significance to ensure food quality and safety and prevent foodborne illnesses. Traditional chemical analysis methods may be laborious, time-consuming, lack selectivity and sensitivity to specific analytes. To overcome the drawbacks of traditional analytical methods and take advantages of advanced materials and novel detection tools, this PhD thesis focuses on (i) synthesizing MIPs materials using different polymerization methods for selective recognition and separation of chemical contaminants from complex food matrices (Chapter 2, 3 & 4); (ii) combining MIPs with SERS as novel chemosensors for rapid, selective, and ultrasensitive determination of chemical contaminants in foods (Chapter 2 & 3); (iii) integrating QDs with MIPs as fluorescence sensors for rapid, selective, and sensitive determination of chemical contaminant in foods (Chapter 4); and (iv) developing a PDMS-based microfluidic device to integrate the processes of sample pretreatment, separation, and detection on a single chip and achieve the analysis of food chemical contaminant in a simple manner (Chapter 4). 5.1 Main findings In Chapter 2, a novel molecularly imprinted polymers-surface-enhanced Raman spectroscopy (MIPs-SERS)/colorimetric dual sensor was developed to determinate chlorpyrifos (CPF) in apple juice. MIPs were synthesized using bulk polymerization to rapidly and selectively adsorb and separate CPF from apple juice. A colorimetric method was developed based upon color changes of synthesized silver nanoparticles (AgNPs) by interacting with CPF, while SERS spectra were directly collected by illuminating the aggregated AgNPs using Raman laser. Colorimetric 112 method rapidly screened and semi-quantified CPF in apple juice ≥5 mg/L by naked eye, or accurately quantified CPF in apple juice ranging 0.1-10 mg/L by UV-vis spectroscopy. Principle component analysis and partial least-squares regression models (RMSEC = 0.0453, R2-C = 0.9885) were developed and validated using SERS to further quantify CPF in apple juice at extremely low concentration (0.01 mg/L). This MIPs-SERS/colorimetric dual sensor could rapidly (<25 min), accurately, and cost-efficiently determine CPF in apple juice and meet the worldwide regulation of maximum residue limit. In Chapter 3, a novel strategy was developed to hybridize molecularly imprinted polymers (MIPs) with gold nanoparticles as the functional substrate for selective SERS detection of 2,4-dichlorophenoxyacetic acid (2,4-D), a systemic herbicide that has acute toxicity and cancer risk. The core-shell AuNPs@MIPs nanoparticles were finely tailored by wrapping an ultrathin layer of MIPs on the surface of AuNPs, which allowed selective separation and concentration of 2,4-D close to AuNPs surface and ensured the enhancement of Raman signals of the analyte. Embedding of an internal standard (i.e., 4-aminothiophenol) inside AuNPs@MIPs for SERS spectral calibration enabled accurate quantification of 2,4-D concentrations. Three-dimensional finite difference time domain (3D-FDTD) simulation demonstrated the maximal electric field enhancement existed in the gap between adjacent AuNPs@MIPs with the enhancement factor as high as 5.85×106. Chemometric models established using SERS spectra showed accurate differentiation and quantification of 2,4-D at various contamination levels in milk with a limit of detection (LOD) of 0.011 µg/mL. Our approach to combine MIPs with noble metallic nanoparticles has great potential for selective and quantitative detection of analytes using SERS for practical food analysis. 113 In Chapter 4, a novel analytical approach of using quantum dots-based molecular imprinting (QDs@MIPs) fluorescence sensor combined with inertial separation microfluidic device for rapid separation and detection of enrofloxacin was developed. The microfluidic device was designed on the basis of laminar flow principle and Dean-coupled inertial migration of particles in spiral microchannel. Micro-sized QDs@MIPs sensing particles were synthesized for selective separation and sensitive detection of enrofloxacin. These microparticles were successfully separated from the original suspension solution to another parallel flowing liquid in the spiral microchannel with a separation efficiency of 97.7±1.6%. Fluorescence intensity of QDs@MIPs was quenched in the presence of enrofloxacin and showed a linearity in the range of 0.1-50 µg/mL with limit of detection of 0.059 µg/mL. On-chip analyses were performed to detect enrofloxacin in both standard samples and contaminated swine muscle. This sensing system integrates sample pretreatment, separation and detection using a single microfluidic chip to realize rapid analysis of food contaminants in a simple approach with excellent selectivity and sensitivity. 5.2 Future research directions In this thesis project, three types of MIPs materials were synthesized and combined with different detection tools as novel analytical methods to determine chemical contaminants in foods. All the developed methods demonstrated excellent performances for selective recognition, separation and detection of pesticides and veterinary drugs in various food commodities. Future studies can also be developed to improve the performances for each approach. 114 For Chapter2, MIPs material was synthesized using bulk polymerization. The obtained monolithic polymer has to be ground and sieved to get MIPs particles, which is labor-intensive and time-consuming. Besides, the affinity of this type of MIPs to target molecules is relatively low. In future, alternative MIPs preparation strategy (e.g., precipitation and sol-gel polymerization methods) may be applied to synthesize MIPs particles with a higher affinity and more uniform size and shape, which can further improve its separation efficiency towards chemical contaminants in foods. In addition, alternative metallic nanoparticles (e.g., gold nanoparticles) may be investigated for both colorimetric reaction and SERS detection, which may further improve the detection sensitivities for both methods. For the second study in Chapter 3, gold nanoparticles with diameter of 33.6 ± 5.6 nm were wrapped with an ultrathin layer (6.5 ± 1.5 nm) of MIPs shell to selectively separate and detect 2,4-D in milk sample. 3D-FDTD simulation results showed that when the distance between two adjacent AuNPs was 0 nm, the electromagnetic field intensity was the highest that result in the highest SERS enhancement. Therefore, future studies can be conducted to optimize MIPs shell wrapping conditions for further decreasing the thickness of MIPs shell. Besides, AuNPs with different diameters can be tested to evaluate SERS performances of AuNPs@MIPs with different sizes. For the last analytical approach developed in Chapter 4, commercially available silica spheres with diameters labeled in the range of 5-13 µm were used as the core to wrap QDs@MIPs fluorescent sensing material. There are many particles that are smaller than the labeled size (some of them were even at submicron in diameter), which could affect the separation efficiency 115 of inertial separation microfluidic device and result in a detection result lower than its actual value. To ensure the detection accuracy, a screening step has been performed for silica spheres to remove small size particles before use as the core for QDs@MIPs synthesis. In future studies, strategies for synthesizing silica spheres with more uniform size distribution (5-10 µm in diameter) should be developed. Currently, determination of enrofloxacin in foods was achieved by detecting and quantifying the fluorescence quenching amounts of QDs. QDs-mediated fluorescence “turn-off-on” sensors can be developed to further improve the sensitivity of the proposed analytical approach. Due to the lack of suitable instrument to monitor the variance of fluorescence intensity in real-time, current detection has to be finished by collecting the solutions that passed through the microfluidic chip and then analyzing using a commercial fluorescence spectrometer. If a small portable device can be constructed to monitor the fluorescence intensity variance at the outlet of the microfluidic chip, the detection process can be further simplified and real-time determination of chemical contaminants may be realized. 116 Bibliography (USDA), T. U. S. D. o. A. (2020). Maximum Residue Limits (MRL) Database. Retrieved from https://www.fas.usda.gov/maximum-residue-limits-mrl-database (WHO), W. H. O. (2018). Maximum Residue Limits (MRLs). Retrieved from http://www.fao.org/fao-who-codexalimentarius/codex-texts/maximum-residue-limits/en/ Agency, T. C. F. I. (2017). Imported and Manufactured Food Program Inspection Manual. Retrieved from https://inspection.canada.ca/food-safety-for-industry/archived-food-guidance/non-federally-registered/product-inspection/inspection-manual/eng/1393949957029/1393950086417?chap=0 Ahmad, O. S., Bedwell, T. S., Esen, C., Garcia-Cruz, A., & Piletsky, S. A. (2019). Molecularly Imprinted Polymers in Electrochemical and Optical Sensors. Trends in Biotechnology, 37(3), 294-309. doi:10.1016/j.tibtech.2018.08.009 Algar, W. R., Tavares, A. J., & Krull, U. J. (2010). Beyond labels: a review of the application of quantum dots as integrated components of assays, bioprobes, and biosensors utilizing optical transduction. Analytica Chimica Acta, 673(1), 1-25. doi:10.1016/j.aca.2010.05.026 Alishahi, A., Farahmand, H., Prieto, N., & Cozzolino, D. (2010). Identification of transgenic foods using NIR spectroscopy: a review. Spectrochim Acta A Mol Biomol Spectrosc, 75(1), 1-7. doi:10.1016/j.saa.2009.10.001 Allegrini, F., & Olivieri, A. C. (2014). IUPAC-consistent approach to the limit of detection in partial least-squares calibration. Analytical Chemistry, 86(15), 7858-7866. doi:10.1021/ac501786u 117 Anirudhan, T. S., & Alexander, S. (2013). Synthesis and characterization of vinyl-functionalized multiwalled carbon nanotubes based molecular imprinted polymer for the separation of chlorpyrifos from aqueous solutions. Journal of Chemical Technology & Biotechnology, 88(10), 1847-1858. doi:10.1002/jctb.4039 Anonymous. (2013). SIB - Chemical Hazards in Food. Retrieved from http://worldfoodscience.com/content/sib-chemical-hazards-food Armstrong, J. L., Dills, R. L., Yu, J., Yost, M. G., & Fenske, R. A. (2014). A sensitive LC-MS/MS method for measurement of organophosphorus pesticides and their oxygen analogs in air sampling matrices. J Environ Sci Health B, 49(2), 102-108. doi:10.1080/03601234.2014.846744 Asmolov, E. S. (1999). The inertial lift on a spherical particle in a plane Poiseuille flow at large channel Reynolds number. Journal of Fluid Mechanics, 381, 63-87. doi:Doi 10.1017/S0022112098003474 Azinheiro, S., Kant, K., Shahbazi, M. A., Garrido-Maestu, A., Prado, M., & Dieguez, L. (2020). A smart microfluidic platform for rapid multiplexed detection of foodborne pathogens. Food Control, 114, 107242. doi:ARTN 107242 10.1016/j.foodcont.2020.107242 Bélanger P, Tanguay F, Hamel M, & Phypers M. (2015). Foodborne outbreaks in Canada: 2008-2014. Foodborne Illness. Retrieved from https://www.canada.ca/en/public-health/services/reports-publications/canada-communicable-disease-report-ccdr/monthly-issue/2015-41/ccdr-volume-41-11-november-5-2015-foodborne-illness/ccdr-volume-41-11-november-5-2015-foodborne-illness.html 118 BelBruno, J. J. (2019). Molecularly Imprinted Polymers. Chem Rev, 119(1), 94-119. doi:10.1021/acs.chemrev.8b00171 Beltran, A., Marce, R. M., Cormack, P. A., & Borrull, F. (2009). Synthesis by precipitation polymerisation of molecularly imprinted polymer microspheres for the selective extraction of carbamazepine and oxcarbazepine from human urine. Journal of Chromatography A, 1216(12), 2248-2253. doi:10.1016/j.chroma.2009.01.024 Beltran, A., Marcé, R. M., Cormack, P. A. G., & Borrull, F. (2009). Synthesis by precipitation polymerisation of molecularly imprinted polymer microspheres for the selective extraction of carbamazepine and oxcarbazepine from human urine. Journal of Chromatography A, 1216(12), 2248-2253. doi:https://doi.org/10.1016/j.chroma.2009.01.024 Berger, S. A., Talbot, L., & Yao, L. S. (1983). Flow in Curved Pipes. Annual Review of Fluid Mechanics, 15(1), 461-512. doi:DOI 10.1146/annurev.fl.15.010183.002333 Bernat, A., Samiwala, M., Albo, J., Jiang, X., & Rao, Q. (2019). Challenges in SERS-based pesticide detection and plausible solutions. J Agric Food Chem, 67(45), 12341-12347. doi:10.1021/acs.jafc.9b05077 Bhagat, A. A., Kuntaegowdanahalli, S. S., & Papautsky, I. (2008). Continuous particle separation in spiral microchannels using Dean flows and differential migration. Lab Chip, 8(11), 1906-1914. doi:10.1039/b807107a Boushell, V., Pang, S., & He, L. (2017). Aptamer-Based SERS Detection of Lysozyme on a Food-Handling Surface. Journal of Food Science, 82(1), 225-231. doi:10.1111/1750-3841.13582 119 Canada, H. (2020). Health Canada's Maximum Levels for Chemical Contaminants in Foods. Retrieved from https://www.canada.ca/en/health-canada/services/food-nutrition/food-safety/chemical-contaminants/maximum-levels-chemical-contaminants-foods.html Carvalho, F. P. (2017). Pesticides, environment, and food safety. Food and Energy Security, 6(2), 48-60. doi:10.1002/fes3.108 Castro-Grijalba, A., Montes-Garcia, V., Cordero-Ferradas, M. J., Coronado, E., Perez-Juste, J., & Pastoriza-Santos, I. (2020). SERS-Based Molecularly Imprinted Plasmonic Sensor for Highly Sensitive PAH Detection. ACS Sens, 5(3), 693-702. doi:10.1021/acssensors.9b01882 Chantada-Vazquez, M. P., Sanchez-Gonzalez, J., Pena-Vazquez, E., Tabernero, M. J., Bermejo, A. M., Bermejo-Barrera, P., & Moreda-Pineiro, A. (2016). Simple and Sensitive Molecularly Imprinted Polymer - Mn-Doped ZnS Quantum Dots Based Fluorescence Probe for Cocaine and Metabolites Determination in Urine. Analytical Chemistry, 88(5), 2734-2741. doi:10.1021/acs.analchem.5b04250 Chapuis, F., Mullot, J. U., Pichon, V., Tuffal, G., & Hennion, M. C. (2006). Molecularly imprinted polymers for the clean-up of a basic drug from environmental and biological samples. Journal of Chromatography A, 1135(2), 127-134. doi:10.1016/j.chroma.2006.09.076 Chen, L., Wang, X., Lu, W., Wu, X., & Li, J. (2016). Molecular imprinting: perspectives and applications. Chemical Society Reviews, 45(8), 2137-2211. doi:10.1039/c6cs00061d Chen, L., Xu, S., & Li, J. (2011). Recent advances in molecular imprinting technology: current status, challenges and highlighted applications. Chemical Society Reviews, 40(5), 2922-2942. doi:10.1039/c0cs00084a 120 Chen, L. M., & Liu, Y. N. (2011). Surface-enhanced Raman detection of melamine on silver-nanoparticle-decorated silver/carbon nanospheres: effect of metal ions. ACS Appl Mater Interfaces, 3(8), 3091-3096. doi:10.1021/am200603y Chen, W., Ma, Y., Pan, J. M., Meng, Z. H., Pan, G. Q., & Sellergren, B. (2015). Molecularly Imprinted Polymers with Stimuli-Responsive Affinity: Progress and Perspectives. Polymers, 7(9), 1689-1715. doi:10.3390/polym7091478 Chiou, J. C., Leung, A. H. H., Lee, H. W., & Wong, W. T. (2015). Rapid testing methods for food contaminants and toxicants. Journal of Integrative Agriculture, 14(11), 2243-2264. doi:10.1016/S2095-3119(15)61119-4 Costa, J. C., Ando, R. A., Sant'Ana, A. C., Rossi, L. M., Santos, P. S., Temperini, M. L., & Corio, P. (2009). High performance gold nanorods and silver nanocubes in surface-enhanced Raman spectroscopy of pesticides. Physical Chemistry Chemical Physics, 11(34), 7491-7498. doi:10.1039/b904734d Craig, A. P., Franca, A. S., & Irudayaraj, J. (2013). Surface-enhanced Raman spectroscopy applied to food safety. Annu Rev Food Sci Technol, 4, 369-380. doi:10.1146/annurev-food-022811-101227 Craig, R. L., Bondy, A. L., & Ault, A. P. (2015). Surface Enhanced Raman Spectroscopy Enables Observations of Previously Undetectable Secondary Organic Aerosol Components at the Individual Particle Level. Analytical Chemistry, 87(15), 7510-7514. doi:10.1021/acs.analchem.5b01507 Crevillen, A. G., Avila, M., Pumera, M., Gonzalez, M. C., & Escarpa, A. (2007). Food analysis on microfluidic devices using ultrasensitive carbon nanotubes detectors. Analytical Chemistry, 79(19), 7408-7415. doi:10.1021/ac071247i 121 Croissant, J. G., Fatieiev, Y., Almalik, A., & Khashab, N. M. (2018). Mesoporous Silica and Organosilica Nanoparticles: Physical Chemistry, Biosafety, Delivery Strategies, and Biomedical Applications. Adv Healthc Mater, 7(4), 1700831. doi:10.1002/adhm.201700831 Di Carlo, D. (2009). Inertial microfluidics. Lab Chip, 9(21), 3038-3046. doi:10.1039/b912547g Di Carlo, D., Edd, J. F., Irimia, D., Tompkins, R. G., & Toner, M. (2008). Equilibrium separation and filtration of particles using differential inertial focusing. Analytical Chemistry, 80(6), 2204-2211. doi:10.1021/ac702283m Di Carlo, D., Irimia, D., Tompkins, R. G., & Toner, M. (2007). Continuous inertial focusing, ordering, and separation of particles in microchannels. Proceedings of the National Academy of Sciences of the United States of America, 104(48), 18892-18897. doi:10.1073/pnas.0704958104 Ding, S. Y., You, E. M., Tian, Z. Q., & Moskovits, M. (2017). Electromagnetic theories of surface-enhanced Raman spectroscopy. Chemical Society Reviews, 46(13), 4042-4076. doi:10.1039/c7cs00238f Du, H., Wang, X. M., Yang, Q. L., & Wu, W. (2021). Quantum dot: Lightning invisible foodborne pathogens. Trends in Food Science & Technology, 110, 1-12. doi:10.1016/j.tifs.2021.01.065 Duan, J., Yang, M., Lai, Y., Yuan, J., & Zhan, J. (2012). A colorimetric and surface-enhanced Raman scattering dual-signal sensor for Hg2+ based on Bismuthiol II-capped gold nanoparticles. Analytica Chimica Acta, 723, 88-93. doi:10.1016/j.aca.2012.02.031 Duan, N., Wu, S. J., Dai, S. L., Miao, T. T., Chen, J., & Wang, Z. P. (2015). Simultaneous detection of pathogenic bacteria using an aptamer based biosensor and dual fluorescence 122 resonance energy transfer from quantum dots to carbon nanoparticles. Microchimica Acta, 182(5-6), 917-923. doi:10.1007/s00604-014-1406-3 Duan, Z. J., Fan, L. P., Fang, G. Z., Yi, J. H., & Wang, S. (2011). Novel surface molecularly imprinted sol-gel polymer applied to the online solid phase extraction of methyl-3-quinoxaline-2-carboxylic acid and quinoxaline-2-carboxylic acid from pork muscle. Anal Bioanal Chem, 401(7), 2291-2299. doi:10.1007/s00216-011-5329-0 Eftink, M. R., & Ghiron, C. A. (1981). Fluorescence quenching studies with proteins. Analytical Biochemistry, 114(2), 199-227. doi:10.1016/0003-2697(81)90474-7 Ellis, D. I., Brewster, V. L., Dunn, W. B., Allwood, J. W., Golovanov, A. P., & Goodacre, R. (2012). Fingerprinting food: current technologies for the detection of food adulteration and contamination. Chemical Society Reviews, 41(17), 5706-5727. doi:10.1039/c2cs35138b Fang, G. Z., Fan, C., Liu, H. L., Pan, M. F., Zhu, H. D., & Wang, S. (2014). A novel molecularly imprinted polymer on CdSe/ZnS quantum dots for highly selective optosensing of mycotoxin zearalenone in cereal samples. Rsc Advances, 4(6), 2764-2771. doi:10.1039/c3ra45172k Feng, S., Gao, F., Chen, Z., Grant, E., Kitts, D. D., Wang, S., & Lu, X. (2013). Determination of alpha-tocopherol in vegetable oils using a molecularly imprinted polymers-surface-enhanced Raman spectroscopic biosensor. J Agric Food Chem, 61(44), 10467-10475. doi:10.1021/jf4038858 Feng, S., & Lu, X. (2015). Molecularly imprinted polymers integrated with surface enhanced Raman spectroscopy: Innovative chemosensors in food science. Lipid Technology, 27(1), 14-17. doi:10.1002/lite.201400073 123 Feng, S. L., Hu, Y. X., Ma, L. Y., & Lu, X. N. (2017). Development of molecularly imprinted polymers-surface-enhanced Raman spectroscopy/colorimetric dual sensor for determination of chlorpyrifos in apple juice. Sensors and Actuators B-Chemical, 241, 750-757. doi:10.1016/j.snb.2016.10.131 Feng, Y., Wang, Y., Wang, H., Chen, T., Tay, Y. Y., Yao, L., . . . Chen, H. (2012). Engineering "hot" nanoparticles for surface-enhanced Raman scattering by embedding reporter molecules in metal layers. Small, 8(2), 246-251. doi:10.1002/smll.201102215 Gao, F., Feng, S., Chen, Z., Li-Chan, E. C., Grant, E., & Lu, X. (2014). Detection and quantification of chloramphenicol in milk and honey using molecularly imprinted polymers: Canadian penny-based SERS nano-biosensor. Journal of Food Science, 79(12), N2542-2549. doi:10.1111/1750-3841.12705 Gao, F., Grant, E., & Lu, X. (2015). Determination of histamine in canned tuna by molecularly imprinted polymers-surface enhanced Raman spectroscopy. Analytica Chimica Acta, 901, 68-75. doi:10.1016/j.aca.2015.10.025 Gao, F., Hu, Y., Chen, D., Li-Chan, E. C. Y., Grant, E., & Lu, X. (2015). Determination of Sudan I in paprika powder by molecularly imprinted polymers-thin layer chromatography-surface enhanced Raman spectroscopic biosensor. Talanta, 143, 344-352. doi:10.1016/j.talanta.2015.05.003 Gao, H., Yan, C., Wu, W., & Li, J. (2020). Application of Microfluidic Chip Technology in Food Safety Sensing. Sensors (Basel), 20(6), 1792. doi:10.3390/s20061792 Garimella, S., & Sobhan, C. (2003). Transport in microchannels-a critical review. Annual Review of Heat Transfer, 13, 1-50. 124 Gillibert, R., Huang, J. Q., Zhang, Y., Fu, W. L., & de la Chapelle, M. L. (2018). Food quality control by Surface Enhanced Raman Scattering. Trac-Trends in Analytical Chemistry, 105, 185-190. doi:10.1016/j.trac.2018.05.009 Group, W. F. D. B. E. R. (2015). WHO estimates of the global burden of foodborne diseases. Retrieved from World Health Organization https://www.who.int/publications/i/item/9789241565165 Guo, L. J., Feng, J. S., Fang, Z. C., Xu, J., & Lu, X. N. (2015). Application of microfluidic "lab-on-a-chip" for the detection of mycotoxins in foods. Trends in Food Science & Technology, 46(2), 252-263. doi:10.1016/j.tifs.2015.09.005 Guo, X., Li, J., Arabi, M., Wang, X., Wang, Y., & Chen, L. (2020). Molecular-Imprinting-Based Surface-Enhanced Raman Scattering Sensors. ACS Sens, 5(3), 601-619. doi:10.1021/acssensors.9b02039 Guo, Y., Girmatsion, M., Li, H. W., Xie, Y., Yao, W., Qian, H., . . . Mahmud, A. (2020). Rapid and ultrasensitive detection of food contaminants using surface-enhanced Raman spectroscopy-based methods. Crit Rev Food Sci Nutr, 1-14. doi:10.1080/10408398.2020.1803197 Haeberle, S., & Zengerle, R. (2007). Microfluidic platforms for lab-on-a-chip applications. Lab Chip, 7(9), 1094-1110. doi:10.1039/b706364b Hajizadeh, S., Farhadi, K., Forough, M., & Sabzi, R. E. (2011). Silver nanoparticles as a cyanide colorimetric sensor in aqueous media. Analytical Methods, 3(11), 2599-2603. doi:10.1039/c1ay05567d Haupt, K., & Mosbach, K. (2000). Molecularly imprinted polymers and their use in biomimetic sensors. Chem Rev, 100(7), 2495-2504. doi:10.1021/cr990099w 125 Haynes, C. L., McFarland, A. D., & Van Duyne, R. P. (2005). Surface-enhanced Raman spectroscopy. Analytical Chemistry, 77(17), 338a-346a. doi:DOI 10.1021/ac053456d He, L., Rodda, T., Haynes, C. L., Deschaines, T., Strother, T., Diez-Gonzalez, F., & Labuza, T. P. (2011). Detection of a foreign protein in milk using surface-enhanced Raman spectroscopy coupled with antibody-modified silver dendrites. Analytical Chemistry, 83(5), 1510-1513. doi:10.1021/ac1032353 He, L. L., Lamont, E., Veeregowda, B., Sreevatsan, S., Haynes, C. L., Diez-Gonzalez, F., & Labuza, T. P. (2011). Aptamer-based surface-enhanced Raman scattering detection of ricin in liquid foods. Chemical Science, 2(8), 1579-1582. doi:10.1039/c1sc00201e Health Canada. (2014). Food Safety Hazards. Retrieved from https://inspection.canada.ca/food-safety-for-industry/archived-food-guidance/non-federally-registered/product-inspection/inspection-manual/eng/1393949957029/1393950086417?chap=5#s24c5 Health Canada. (2021). List of Maximum Residue Limits (MRLs) for Veterinary Drugs in Foods. Retrieved from https://www.canada.ca/en/health-canada/services/drugs-health-products/veterinary-drugs/maximum-residue-limits-mrls/list-maximum-residue-limits-mrls-veterinary-drugs-foods.html Hildebrandt, N., Spillmann, C. M., Algar, W. R., Pons, T., Stewart, M. H., Oh, E., . . . Medintz, I. L. (2017). Energy Transfer with Semiconductor Quantum Dot Bioconjugates: A Versatile Platform for Biosensing, Energy Harvesting, and Other Developing Applications. Chem Rev, 117(2), 536-711. doi:10.1021/acs.chemrev.6b00030 Hou, R. Y., Pang, S., & He, L. L. (2015). In situ SERS detection of multi-class insecticides on plant surfaces. Analytical Methods, 7(15), 6325-6330. doi:10.1039/c5ay01058f 126 Hu, X. G., Wang, T., Wang, L., & Dong, S. J. (2007). Surface-enhanced Raman scattering of 4-aminothiophenol self-assembled monolayers in sandwich structure with nanoparticle shape dependence: Off-surface plasmon resonance condition. Journal of Physical Chemistry C, 111(19), 6962-6969. doi:10.1021/jp0712194 Hu, Y., Feng, S., Gao, F., Li-Chan, E. C., Grant, E., & Lu, X. (2015). Detection of melamine in milk using molecularly imprinted polymers-surface enhanced Raman spectroscopy. Food Chemistry, 176, 123-129. doi:10.1016/j.foodchem.2014.12.051 Huang, A., Qiu, Z., Jin, M., Shen, Z., Chen, Z., Wang, X., & Li, J. W. (2014). High-throughput detection of food-borne pathogenic bacteria using oligonucleotide microarray with quantum dots as fluorescent labels. Int J Food Microbiol, 185, 27-32. doi:10.1016/j.ijfoodmicro.2014.05.012 Huang, X., Zou, H., Chen, X., Luo, Q., & Kong, L. (2003). Molecularly imprinted monolithic stationary phases for liquid chromatographic separation of enantiomers and diastereomers. Journal of Chromatography A, 984(2), 273-282. doi:10.1016/s0021-9673(02)01768-5 Huang, Y., Wang, X., Lai, K., Fan, Y., & Rasco, B. A. (2020). Trace analysis of organic compounds in foods with surface-enhanced Raman spectroscopy: Methodology, progress, and challenges. Compr Rev Food Sci Food Saf, 19(2), 622-642. doi:10.1111/1541-4337.12531 Inglis, D. W., Herman, N., & Vesey, G. (2010). Highly accurate deterministic lateral displacement device and its application to purification of fungal spores. Biomicrofluidics, 4(2). doi:10.1063/1.3430553 127 Jia, M., Qin, L., He, X. W., & Li, W. Y. (2012). Preparation and application of lysozyme imprinted monolithic column with dopamine as the functional monomer. Journal of Materials Chemistry, 22(2), 707-713. doi:10.1039/c1jm13134f Jiang, J., Bosnick, K., Maillard, M., & Brus, L. (2003). Single molecule Raman spectroscopy at the junctions of large Ag nanocrystals. Journal of Physical Chemistry B, 107(37), 9964-9972. doi:10.1021/jp034632u Junping, W., Mingfei, P., Guozhen, F., & Shuo, W. (2009). Preparation of a novel molecularly imprinted polymer by a sol-gel process for on-line solid-phase extraction coupled with high performance liquid chromatography to detect trace enrofloxacin in fish and chicken samples. Microchimica Acta, 166(3-4), 295-302. doi:10.1007/s00604-009-0205-8 Kambhampati, P., Child, C. M., Foster, M. C., & Campion, A. (1998). On the chemical mechanism of surface enhanced Raman scattering: Experiment and theory. Journal of Chemical Physics, 108(12), 5013-5026. doi:Doi 10.1063/1.475909 Kamel, F., & Hoppin, J. A. (2004). Association of pesticide exposure with neurologic dysfunction and disease. Environmental Health Perspectives, 112(9), 950-958. doi:10.1289/ehp.7135 Kamra, T., Zhou, T., Montelius, L., Schnadt, J., & Ye, L. (2015). Implementation of molecularly imprinted polymer beads for surface enhanced Raman detection. Analytical Chemistry, 87(10), 5056-5061. doi:10.1021/acs.analchem.5b00774 Kane, R. S., Takayama, S., Ostuni, E., Ingber, D. E., & Whitesides, G. M. (1999). Patterning proteins and cells using soft lithography. In D. F. Williams (Ed.), The Biomaterials: Silver Jubilee Compendium (pp. 161-174). Oxford: Elsevier Science. 128 Kang, Y., & Taton, T. A. (2005). Core/Shell gold nanoparticles by self-assembly and crosslinking of micellar, block-copolymer shells. Angew Chem Int Ed Engl, 44(3), 409-412. doi:10.1002/anie.200461119 Kataoka, H., Lord, H. L., & Pawliszyn, J. (2000). Applications of solid-phase microextraction in food analysis. Journal of Chromatography A, 880(1-2), 35-62. doi:10.1016/s0021-9673(00)00309-5 Kim, A., Barcelo, S. J., & Li, Z. (2015). SERS-based pesticide detection by using nanofinger sensors. Nanotechnology, 26(1), 015502. doi:10.1088/0957-4484/26/1/015502 Kneipp, K., Wang, Y., Kneipp, H., Perelman, L. T., Itzkan, I., Dasari, R., & Feld, M. S. (1997). Single molecule detection using surface-enhanced Raman scattering (SERS). Physical Review Letters, 78(9), 1667-1670. doi:DOI 10.1103/PhysRevLett.78.1667 Kuan, D. H., Wu, C. C., Su, W. Y., & Huang, N. T. (2018). A Microfluidic Device for Simultaneous Extraction of Plasma, Red Blood Cells, and On-Chip White Blood Cell Trapping. Scientific Reports, 8(1), 15345. doi:10.1038/s41598-018-33738-8 Kumar, N., Bansal, A., Sarma, G. S., & Rawal, R. K. (2014). Chemometrics tools used in analytical chemistry: An overview. Talanta, 123, 186-199. doi:https://doi.org/10.1016/j.talanta.2014.02.003 Kuntaegowdanahalli, S. S., Bhagat, A. A., Kumar, G., & Papautsky, I. (2009). Inertial microfluidics for continuous particle separation in spiral microchannels. Lab Chip, 9(20), 2973-2980. doi:10.1039/b908271a Lakshmi, D., Bossi, A., Whitcombe, M. J., Chianella, I., Fowler, S. A., Subrahmanyam, S., . . . Piletsky, S. A. (2009). Electrochemical sensor for catechol and dopamine based on a 129 catalytic molecularly imprinted polymer-conducting polymer hybrid recognition element. Analytical Chemistry, 81(9), 3576-3584. doi:10.1021/ac802536p Lang, T. T., Pang, S., & He, L. L. (2015). Integration of colorimetric and SERS detection for rapid screening and validation of melamine in milk. Analytical Methods, 7(15), 6426-6431. doi:10.1039/c5ay00955c Langer, J., Jimenez de Aberasturi, D., Aizpurua, J., Alvarez-Puebla, R. A., Auguie, B., Baumberg, J. J., . . . Liz-Marzan, L. M. (2020). Present and Future of Surface-Enhanced Raman Scattering. Acs Nano, 14(1), 28-117. doi:10.1021/acsnano.9b04224 Le Ru, E. C., Blackie, E., Meyer, M., & Etchegoin, P. G. (2007). Surface Enhanced Raman Scattering Enhancement Factors: A Comprehensive Study. The Journal of Physical Chemistry C, 111(37), 13794-13803. doi:10.1021/jp0687908 Lee, W. J., Blair, A., Hoppin, J. A., Lubin, J. H., Rusiecki, J. A., Sandler, D. P., . . . Alavanja, M. C. (2004). Cancer incidence among pesticide applicators exposed to chlorpyrifos in the Agricultural Health Study. J Natl Cancer Inst, 96(23), 1781-1789. doi:10.1093/jnci/djh324 Li, H. B., & Qu, F. G. (2007). Synthesis of CdTe quantum dots in sol-gel-derived composite silica spheres coated with calix[4]arene as luminescent probes for pesticides. Chemistry of Materials, 19(17), 4148-4154. doi:10.1021/cm0700089 Li, J. F., Huang, Y. F., Ding, Y., Yang, Z. L., Li, S. B., Zhou, X. S., . . . Tian, Z. Q. (2010). Shell-isolated nanoparticle-enhanced Raman spectroscopy. Nature, 464(7287), 392-395. doi:10.1038/nature08907 130 Li, J. F., Zhang, Y. J., Ding, S. Y., Panneerselvam, R., & Tian, Z. Q. (2017). Core-Shell Nanoparticle-Enhanced Raman Spectroscopy. Chem Rev, 117(7), 5002-5069. doi:10.1021/acs.chemrev.6b00596 Li, N., Gu, Y., Gao, M. M., Wang, Z. L., Xiao, D. L., Li, Y., . . . He, H. (2014). Label-free silver nanoparticles for visual colorimetric detection of etimicin. Analytical Methods, 6(19), 7906-7911. doi:10.1039/c4ay01439a Li, W., Ma, C., Zhang, L., Chen, B., Chen, L., & Zeng, H. (2019). Tuning Localized Surface Plasmon Resonance of Nanoporous Gold with a Silica Shell for Surface Enhanced Raman Scattering. Nanomaterials (Basel), 9(2), 251. doi:10.3390/nano9020251 Li, X., Feng, S., Hu, Y., Sheng, W., Zhang, Y., Yuan, S., . . . Lu, X. (2015). Rapid Detection of Melamine in Milk Using Immunological Separation and Surface Enhanced Raman Spectroscopy. Journal of Food Science, 80(6), C1196-1201. doi:10.1111/1750-3841.12876 Liao, W., & Lu, X. N. (2016). Determination of chemical hazards in foods using surface-enhanced Raman spectroscopy coupled with advanced separation techniques. Trends in Food Science & Technology, 54, 103-113. doi:10.1016/j.tifs.2016.05.020 Lin, G. F., Wang, Y. H., Li, G. C., Bai, W., Zhang, H., & Wang, S. C. (2014). Construction and Application of Molecularly Imprinted Film Sensor on Determination of Chlorpyrifos in Water. Advanced Materials Research, 936, 843-849. doi:10.4028/www.scientific.net/AMR.936.843 Lin, Z. S., & He, L. L. (2019). Recent advance in SERS techniques for food safety and quality analysis: a brief review. Current Opinion in Food Science, 28, 82-87. doi:10.1016/j.cofs.2019.10.001 131 Liu, G., Huang, X., Li, L., Xu, X., Zhang, Y., Lv, J., & Xu, D. (2019). Recent Advances and Perspectives of Molecularly Imprinted Polymer-Based Fluorescent Sensors in Food and Environment Analysis. Nanomaterials (Basel), 9(7), 1030. doi:10.3390/nano9071030 Liu, H., Fang, G., & Wang, S. (2014). Molecularly imprinted optosensing material based on hydrophobic CdSe quantum dots via a reverse microemulsion for specific recognition of ractopamine. Biosens Bioelectron, 55, 127-132. doi:10.1016/j.bios.2013.11.064 Liu, H., Liu, D., Fang, G., Liu, F., Liu, C., Yang, Y., & Wang, S. (2013). A novel dual-function molecularly imprinted polymer on CdTe/ZnS quantum dots for highly selective and sensitive determination of ractopamine. Analytica Chimica Acta, 762, 76-82. doi:10.1016/j.aca.2012.11.047 Liu, H., Zhou, K., Wu, D., Wang, J., & Sun, B. (2016). A novel quantum dots-labeled on the surface of molecularly imprinted polymer for turn-off optosensing of dicyandiamide in dairy products. Biosens Bioelectron, 77, 512-517. doi:10.1016/j.bios.2015.10.007 Liu, J., Yang, M., & Huai, L. F. (2010). Molecularly imprinted solid-phase extraction for chlorpyrifos determination in water samples. Environment Materials and Environment Management Pts 1-3, 113-116, 947-951. doi:10.4028/www.scientific.net/AMR.113-116.947 Liu, J. M., Wei, S. Y., Liu, H. L., Fang, G. Z., & Wang, S. (2017). Preparation and Evaluation of Core(-)Shell Magnetic Molecularly Imprinted Polymers for Solid-Phase Extraction and Determination of Sterigmatocystin in Food. Polymers (Basel), 9(10). doi:10.3390/polym9100546 132 Liu, M., Gao, Z., Yu, Y., Su, R., Huang, R., Qi, W., & He, Z. (2018). Molecularly Imprinted Core-Shell CdSe@SiO2/CDs as a Ratiometric Fluorescent Probe for 4-Nitrophenol Sensing. Nanoscale Res Lett, 13(1), 27. doi:10.1186/s11671-018-2440-6 Liu, Y., Hoshina, K., & Haginaka, J. (2010). Monodispersed, molecularly imprinted polymers for cinchonidine by precipitation polymerization. Talanta, 80(5), 1713-1718. doi:10.1016/j.talanta.2009.10.011 Lofgreen, J. E., & Ozin, G. A. (2014). Controlling morphology and porosity to improve performance of molecularly imprinted sol-gel silica. Chemical Society Reviews, 43(3), 911-933. doi:10.1039/c3cs60276a Loren, A., Engelbrektsson, J., Eliasson, C., Josefson, M., Abrahamsson, J., Johansson, M., & Abrahamsson, K. (2004). Internal standard in surface-enhanced Raman spectroscopy. Analytical Chemistry, 76(24), 7391-7395. doi:10.1021/ac0491298 Lu, X., & Rasco, B. A. (2012). Determination of antioxidant content and antioxidant activity in foods using infrared spectroscopy and chemometrics: A review. Crit. Rev. Food Sci. Nutr., 52(10), 853-875. doi:10.1080/10408398.2010.511322 Lu, X. N., Al-Qadiri, H. M., Lin, M. S., & Rasco, B. A. (2011). Application of Mid-infrared and Raman Spectroscopy to the Study of Bacteria. Food and Bioprocess Technology, 4(6), 919-935. doi:10.1007/s11947-011-0516-8 Luo, X., Zhao, X., Wallace, G. Q., Brunet, M. H., Wilkinson, K. J., Wu, P., . . . Masson, J. F. (2021). Multiplexed SERS Detection of Microcystins with Aptamer-Driven Core-Satellite Assemblies. ACS Appl Mater Interfaces, 13(5), 6545-6556. doi:10.1021/acsami.0c21493 133 Ma, G., & Chen, L. (2013). Determination of Chlorpyrifos in Rice Based on Magnetic Molecularly Imprinted Polymers Coupled with High-Performance Liquid Chromatography. Food Analytical Methods, 7(2), 377-388. doi:10.1007/s12161-013-9636-2 Mark, D., Haeberle, S., Roth, G., von Stetten, F., & Zengerle, R. (2010). Microfluidic lab-on-a-chip platforms: requirements, characteristics and applications. Chemical Society Reviews, 39(3), 1153-1182. doi:10.1039/b820557b Mattiasson, B. (2015). MIPs as Tools in Environmental Biotechnology. In B. Mattiasson & L. Ye (Eds.), Molecularly Imprinted Polymers in Biotechnology (pp. 183-205). Cham: Springer International Publishing. Medintz, I. L., & Mattoussi, H. (2009). Quantum dot-based resonance energy transfer and its growing application in biology. Physical Chemistry Chemical Physics, 11(1), 17-45. doi:10.1039/b813919a Medintz, I. L., Uyeda, H. T., Goldman, E. R., & Mattoussi, H. (2005). Quantum dot bioconjugates for imaging, labelling and sensing. Nat Mater, 4(6), 435-446. doi:10.1038/nmat1390 Mehendale, S. S., Jacobi, A. M., & Shah, R. K. (2000). Fluid Flow and Heat Transfer at Micro- and Meso-Scales With Application to Heat Exchanger Design. Applied Mechanics Reviews, 53(7), 175-193. doi:10.1115/1.3097347 Mehrzad-Samarin, M., Faridbod, F., Dezfuli, A. S., & Ganjali, M. R. (2017). A novel metronidazole fluorescent nanosensor based on graphene quantum dots embedded silica molecularly imprinted polymer. Biosens Bioelectron, 92, 618-623. doi:10.1016/j.bios.2016.10.047 134 Mi, F. L., Wu, S. J., Zhong, W. Q., & Huang, C. Y. (2015). Preparation of a silver nanoparticle-based dual-functional sensor using a complexation-reduction method. Physical Chemistry Chemical Physics, 17(33), 21243-21253. doi:10.1039/c4cp05012f Michalet, X., Pinaud, F. F., Bentolila, L. A., Tsay, J. M., Doose, S., Li, J. J., . . . Weiss, S. (2005). Quantum dots for live cells, in vivo imaging, and diagnostics. Science, 307(5709), 538-544. doi:10.1126/science.1104274 Moein, M. M., Abdel-Rehim, A., & Abdel-Rehim, M. (2019). Recent Applications of Molecularly Imprinted Sol-Gel Methodology in Sample Preparation. Molecules, 24(16), 2889. doi:10.3390/molecules24162889 Morini, G. L. (2004). Laminar-to-turbulent flow transition in microchannels. Microscale Thermophysical Engineering, 8(1), 15-30. doi:10.1080/10893950490272902 Mosbach, K. (1994). Molecular imprinting. Trends Biochem Sci, 19(1), 9-14. doi:10.1016/0968-0004(94)90166-x Movasaghi, Z., Rehman, S., & Rehman, I. U. (2007). Raman spectroscopy of biological tissues. Applied Spectroscopy Reviews, 42(5), 493-541. doi:10.1080/05704920701551530 Musarurwa, H., Chimuka, L., Pakade, V. E., & Tavengwa, N. T. (2019). Recent developments and applications of QuEChERS based techniques on food samples during pesticide analysis. Journal of Food Composition and Analysis, 84, 103314. doi:ARTN 103314 10.1016/j.jfca.2019.103314 Narenderan, S. T., Meyyanathan, S. N., & Babu, B. (2020). Review of pesticide residue analysis in fruits and vegetables. Pre-treatment, extraction and detection techniques. Food Res Int, 133, 109141. doi:10.1016/j.foodres.2020.109141 135 Nie, S., & Emory, S. R. (1997). Probing Single Molecules and Single Nanoparticles by Surface-Enhanced Raman Scattering. Science, 275(5303), 1102-1106. doi:10.1126/science.275.5303.1102 Nikoleli, G.-P., Nikolelis, D. P., Siontorou, C. G., Karapetis, S., & Varzakas, T. (2018). Chapter Two - Novel Biosensors for the Rapid Detection of Toxicants in Foods. In F. Toldrá (Ed.), Advances in Food and Nutrition Research (Vol. 84, pp. 57-102): Academic Press. Nishat, S., Jafry, A. T., Martinez, A. W., & Awan, F. R. (2021). Paper-based microfluidics: Simplified fabrication and assay methods. Sensors and Actuators B-Chemical, 336, 129681. doi:ARTN 129681 10.1016/j.snb.2021.129681 Nivedita, N., Ligrani, P., & Papautsky, I. (2017). Dean Flow Dynamics in Low-Aspect Ratio Spiral Microchannels. Scientific Reports, 7(1), 44072. doi:10.1038/srep44072 Ookawara, S., Street, D., & Ogawa, K. (2006). Numerical study on development of particle concentration profiles in a curved microchannel. Chemical Engineering Science, 61(11), 3714-3724. doi:10.1016/j.ces.2006.01.016 Osborne, B. G. (2006). Near-Infrared Spectroscopy in Food Analysis. In Encyclopedia of Analytical Chemistry. Otles, S., & Kartal, C. (2016). Solid-Phase Extraction (SPE): Principles and Applications in Food Samples. Acta Scientiarum Polonorum: Technologia Alimentaria, 15(1), 5-15. doi:10.17306/J.AFS.2016.1.1 Ouyang, L., Ren, W., Zhu, L. H., & Irudayaraj, J. (2017). Prosperity to challenges: recent approaches in SERS substrate fabrication. Reviews in Analytical Chemistry, 36(1). doi:ARTN 20160027 136 10.1515/revac-2016-0027 Pan, M., Li, R., Xu, L., Yang, J., Cui, X., & Wang, S. (2018). Reproducible Molecularly Imprinted Piezoelectric Sensor for Accurate and Sensitive Detection of Ractopamine in Swine and Feed Products. Sensors (Basel), 18(6), 1870. doi:10.3390/s18061870 Pang, S., Labuza, T. P., & He, L. (2014). Development of a single aptamer-based surface enhanced Raman scattering method for rapid detection of multiple pesticides. Analyst, 139(8), 1895-1901. doi:10.1039/c3an02263c Parak, W. J., Pellegrino, T., & Plank, C. (2005). Labelling of cells with quantum dots. Nanotechnology, 16(2), R9-R25. doi:10.1088/0957-4484/16/2/R01 Park, J. D., & Zheng, W. (2012). Human exposure and health effects of inorganic and elemental mercury. Journal of Preventive Medicine and Public Health. Yebang Uihakhoe Chi, 45(6), 344-352. doi:10.3961/jpmph.2012.45.6.344 Park, Y. M., Park, J., Lim, S. Y., Kwon, Y., Bae, N. H., Park, J. K., & Lee, S. J. (2021). Integrated pumpless microfluidic chip for the detection of foodborne pathogens by polymerase chain reaction and electrochemical analysis. Sensors and Actuators B-Chemical, 329, 129130. doi:ARTN 129130 10.1016/j.snb.2020.129130 Perez-Jimenez, A. I., Lyu, D., Lu, Z., Liu, G., & Ren, B. (2020). Surface-enhanced Raman spectroscopy: benefits, trade-offs and future developments. Chem Sci, 11(18), 4563-4577. doi:10.1039/d0sc00809e Pham, T. B., Hoang, T. H. C., Pham, V. H., Nguyen, V. C., Nguyen, T. V., Vu, D. C., . . . Bui, H. (2019). Detection of Permethrin pesticide using silver nano-dendrites SERS on optical 137 fibre fabricated by laser-assisted photochemical method. Scientific Reports, 9(1), 12590. doi:10.1038/s41598-019-49077-1 Pietrzyk, A., Suriyanarayanan, S., Kutner, W., Chitta, R., Zandler, M. E., & D'Souza, F. (2010). Molecularly imprinted polymer (MIP) based piezoelectric microgravimetry chemosensor for selective determination of adenine. Biosens Bioelectron, 25(11), 2522-2529. doi:10.1016/j.bios.2010.04.015 Pilot, R. (2018). SERS detection of food contaminants by means of portable Raman instruments. Journal of Raman Spectroscopy, 49(6), 954-981. doi:10.1002/jrs.5400 Pratiwi, R., Megantara, S., Rahayu, D., Pitaloka, I., & Hasanah, A. N. (2018). Comparison of Bulk and Precipitation Polymerization Method of Synthesis Molecular Imprinted Solid Phase Extraction for Atenolol using Methacrylic Acid. Journal of Young Pharmacists, 11(1), 12-16. doi:10.5530/jyp.2019.11.3 Qian, K., Fang, G., He, J., Pan, M., & Wang, S. (2010). Preparation and application of a molecularly imprinted polymer for the determination of trace metolcarb in food matrices by high performance liquid chromatography. Journal of Separation Science, 33(14), 2079-2085. doi:10.1002/jssc.200900877 Qian, K., Fang, G. Z., & Wang, S. (2013). Highly sensitive and selective novel core-shell molecularly imprinted polymer based on NaYF4: Yb3+, Er3+ upconversion fluorescent nanorods. Rsc Advances, 3(12), 3825-3828. doi:10.1039/c3ra23003a Qin, D., Xia, Y., & Whitesides, G. M. (2010). Soft lithography for micro- and nanoscale patterning. Nature Protocols, 5(3), 491-502. doi:10.1038/nprot.2009.234 Radišić, M., Grujić, S., Vasiljević, T., & Laušević, M. (2009). Determination of selected pesticides in fruit juices by matrix solid-phase dispersion and liquid chromatography–138 tandem mass spectrometry. Food Chemistry, 113(2), 712-719. doi:10.1016/j.foodchem.2008.07.103 Ranz, A., Maier, E., Motter, H., & Lankmayr, E. (2008). Extraction and derivatization of polar herbicides for GC-MS analyses. Journal of Separation Science, 31(16-17), 3021-3029. doi:10.1002/jssc.200800290 Rao, J., Dragulescu-Andrasi, A., & Yao, H. (2007). Fluorescence imaging in vivo: recent advances. Current Opinion in Biotechnology, 18(1), 17-25. doi:10.1016/j.copbio.2007.01.003 Ratnaseelan, A. M., Tsilioni, I., & Theoharides, T. C. (2018). Effects of Mycotoxins on Neuropsychiatric Symptoms and Immune Processes. Clinical Therapeutics, 40(6), 903-917. doi:10.1016/j.clinthera.2018.05.004 Refaat, D., Aggour, M. G., Farghali, A. A., Mahajan, R., Wiklander, J. G., Nicholls, I. A., & Piletsky, S. A. (2019). Strategies for Molecular Imprinting and the Evolution of MIP Nanoparticles as Plastic Antibodies-Synthesis and Applications. International Journal of Molecular Sciences, 20(24), 6304. doi:10.3390/ijms20246304 Ren, X., & Chen, L. (2015). Quantum dots coated with molecularly imprinted polymer as fluorescence probe for detection of cyphenothrin. Biosens Bioelectron, 64, 182-188. doi:10.1016/j.bios.2014.08.086 Resch-Genger, U., Grabolle, M., Cavaliere-Jaricot, S., Nitschke, R., & Nann, T. (2008). Quantum dots versus organic dyes as fluorescent labels. Nat Methods, 5(9), 763-775. doi:10.1038/nmeth.1248 139 Russom, A., Gupta, A. K., Nagrath, S., Di Carlo, D., Edd, J. F., & Toner, M. (2009). Differential inertial focusing of particles in curved low-aspect-ratio microchannels. New J Phys, 11, 75025. doi:10.1088/1367-2630/11/7/075025 Safavieh, M., Ahmed, M. U., Tolba, M., & Zourob, M. (2012). Microfluidic electrochemical assay for rapid detection and quantification of Escherichia coli. Biosens Bioelectron, 31(1), 523-528. doi:10.1016/j.bios.2011.11.032 Sass, J. (2015). Highly hazardous pesticide, chlorpyrifos, doesn't look so bad when I wear my "EPA-Dow AgroSciences" rosy colored safety goggles. Retrieved from https://www.nrdc.org/experts/jennifer-sass/highly-hazardous-pesticide-chlorpyrifos-doesnt-look-so-bad-when-i-wear-my-epa Shen, W., Lin, X., Jiang, C., Li, C., Lin, H., Huang, J., . . . Ren, B. (2015). Reliable Quantitative SERS Analysis Facilitated by Core-Shell Nanoparticles with Embedded Internal Standards. Angew Chem Int Ed Engl, 54(25), 7308-7312. doi:10.1002/anie.201502171 Shende, C., Inscore, F., Sengupta, A., Stuart, J., & Farquharson, S. (2010). Rapid extraction and detection of trace Chlorpyrifos-methyl in orange juice by surface-enhanced Raman spectroscopy. Sensing and Instrumentation for Food Quality and Safety, 4(3-4), 101-107. doi:10.1007/s11694-010-9100-6 Shi, R., Liu, X., & Ying, Y. (2018). Facing Challenges in Real-Life Application of Surface-Enhanced Raman Scattering: Design and Nanofabrication of Surface-Enhanced Raman Scattering Substrates for Rapid Field Test of Food Contaminants. J Agric Food Chem, 66(26), 6525-6543. doi:10.1021/acs.jafc.7b03075 Shi, X., Meng, Y., Liu, J., Sun, A., Li, D., Yao, C., . . . Chen, J. (2011). Group-selective molecularly imprinted polymer solid-phase extraction for the simultaneous determination 140 of six sulfonamides in aquaculture products. Journal of Chromatography. B: Analytical Technologies in the Biomedical and Life Sciences, 879(15-16), 1071-1076. doi:10.1016/j.jchromb.2011.03.019 Smith, A. M., Duan, H., Mohs, A. M., & Nie, S. (2008). Bioconjugated quantum dots for in vivo molecular and cellular imaging. Adv Drug Deliv Rev, 60(11), 1226-1240. doi:10.1016/j.addr.2008.03.015 Song, H., & Liu, J. (2018). GC-O-MS technique and its applications in food flavor analysis. Food Res Int, 114, 187-198. doi:10.1016/j.foodres.2018.07.037 Song, J. T., Zhang, X. S., Qin, M. Y., & Zhao, Y. D. (2015). One-pot two-step synthesis of core-shell mesoporous silica-coated gold nanoparticles. Dalton Trans, 44(17), 7752-7756. doi:10.1039/c5dt00912j Song, X. L., Xu, S. F., Chen, L. X., Wei, Y. Q., & Xiong, H. (2014). Recent Advances in Molecularly Imprinted Polymers in Food Analysis. Journal of Applied Polymer Science, 131(16). doi:ARTN 40766 10.1002/app.40766 Spivak, D. A. (2005). Optimization, evaluation, and characterization of molecularly imprinted polymers. Adv Drug Deliv Rev, 57(12), 1779-1794. doi:10.1016/j.addr.2005.07.012 Squires, T. M., & Quake, S. R. (2005). Microfluidics: Fluid physics at the nanoliter scale. Reviews of Modern Physics, 77(3), 977-1026. doi:DOI 10.1103/RevModPhys.77.977 Stachniuk, A., & Fornal, E. (2016). Liquid Chromatography-Mass Spectrometry in the Analysis of Pesticide Residues in Food. Food Analytical Methods, 9(6), 1654-1665. doi:10.1007/s12161-015-0342-0 141 States, T. C. f. D. C. a. P. o. t. U. (2018). Estimates of Foodborne Illness in the United States. Retrieved from Centers for Disease Control and Prevention: https://www.cdc.gov/foodborneburden/index.html Stiles, P. L., Dieringer, J. A., Shah, N. C., & Van Duyne, R. P. (2008). Surface-enhanced Raman spectroscopy. Annu Rev Anal Chem (Palo Alto Calif), 1, 601-626. doi:10.1146/annurev.anchem.1.031207.112814 Su, D., Yang, X., Xia, Q., Zhang, Q., Chai, F., Wang, C., & Qu, F. (2014). Folic acid functionalized silver nanoparticles with sensitivity and selectivity colorimetric and fluorescent detection for Hg2+ and efficient catalysis. Nanotechnology, 25(35), 355702. doi:10.1088/0957-4484/25/35/355702 Sun, D. W., Huang, L., Pu, H., & Ma, J. (2021). Introducing reticular chemistry into agrochemistry. Chemical Society Reviews, 50(2), 1070-1110. doi:10.1039/c9cs00829b Sun, Y., & Xia, Y. (2003). Gold and silver nanoparticles: a class of chromophores with colors tunable in the range from 400 to 750 nm. Analyst, 128(6), 686-691. doi:10.1039/b212437h Tang, Y., Gao, Z., Wang, S., Gao, X., Gao, J., Ma, Y., . . . Li, J. (2015). Upconversion particles coated with molecularly imprinted polymers as fluorescence probe for detection of clenbuterol. Biosens Bioelectron, 71, 44-50. doi:10.1016/j.bios.2015.04.005 Team, T. H. E. (2018). Worst Foodborne Illness Outbreaks in Recent U.S. History. Judith Marcin. Retrieved from https://www.healthline.com/health/worst-foodborne-illness-outbreaks 142 Tharmaraj, V., & Yang, J. (2014). Sensitive and selective colorimetric detection of Cu(2+) in aqueous medium via aggregation of thiomalic acid functionalized Ag nanoparticles. Analyst, 139(23), 6304-6309. doi:10.1039/c4an01449a The Huy, B., Seo, M. H., Zhang, X., & Lee, Y. I. (2014). Selective optosensing of clenbuterol and melamine using molecularly imprinted polymer-capped CdTe quantum dots. Biosens Bioelectron, 57, 310-316. doi:10.1016/j.bios.2014.02.041 Thygesen, L. G., Lokke, M. M., Micklander, E., & Engelsen, S. B. (2003). Vibrational microspectroscopy of food. Raman vs. FT-IR. Trends in Food Science & Technology, 14(1), 50-57. doi:Pii S0924-2244(02)00243-1 Doi 10.1016/S0924-2244(02)00243-1 Tian, J., Bai, J., Peng, Y., Qie, Z., Zhao, Y., Ning, B., . . . Gao, Z. (2015). A core-shell-structured molecularly imprinted polymer on upconverting nanoparticles for selective and sensitive fluorescence sensing of sulfamethazine. Analyst, 140(15), 5301-5307. doi:10.1039/c5an00579e Tully, E., Hearty, S., Leonard, P., & O'Kennedy, R. (2006). The development of rapid fluorescence-based immunoassays, using quantum dot-labelled antibodies for the detection of Listeria monocytogenes cell surface proteins. International Journal of Biological Macromolecules, 39(1-3), 127-134. doi:10.1016/j.ijbiomac.2006.02.023 Uçar, A., Yilmaz, M. V., & Çakiroglu, F. P. (2016). Food Safety – Problems and Solutions. In H. A. Makun (Ed.), Significance, Prevention and Control of Food Related Diseases. IntechOpen: IntechOpen. Walekar, L. S., Kondekar, U. R., Gore, A. H., Pawar, S. P., Sudarsan, V., Anbhule, P. V., . . . Kolekar, G. B. (2014). Ultrasensitive, highly selective and naked eye colorimetric 143 recognition of D-penicillamine in aqueous media by CTAB capped AgNPs: applications to pharmaceutical and biomedical analysis. Rsc Advances, 4(102), 58481-58488. doi:10.1039/c4ra05741d Walsh, R., Osmani, Q., Hughes, H., Duggan, P., & McLoughlin, P. (2011). Synthesis of imprinted beads by aqueous suspension polymerisation for chiral recognition of antihistamines. Journal of Chromatography. B: Analytical Technologies in the Biomedical and Life Sciences, 879(30), 3523-3530. doi:10.1016/j.jchromb.2011.09.036 Wan, F., Shi, H., Chen, W., Gu, Z., Du, L., Wang, P., . . . Huang, Y. (2017). Charge Transfer Effect on Raman and Surface Enhanced Raman Spectroscopy of Furfural Molecules. Nanomaterials (Basel), 7(8), 210. doi:10.3390/nano7080210 Wang, P., Dai, W., Ge, L., Yan, M., Ge, S., & Yu, J. (2013). Visible light photoelectrochemical sensor based on Au nanoparticles and molecularly imprinted poly(o-phenylenediamine)-modified TiO2 nanotubes for specific and sensitive detection chlorpyrifos. Analyst, 138(3), 939-945. doi:10.1039/c2an36266j Wang, X., Huang, S. C., Hu, S., Yan, S., & Ren, B. (2020). Fundamental understanding and applications of plasmon-enhanced Raman spectroscopy. Nature Reviews Physics, 2(5), 253-271. doi:10.1038/s42254-020-0171-y Wang, Y. C., Wang, N. W., Ni, X. N., Jiang, Q. Q., Yang, W. M., Huang, W. H., & Xu, W. Z. (2015). A core-shell CdTe quantum dots molecularly imprinted polymer for recognizing and detecting p-nitrophenol based on computer simulation. Rsc Advances, 5(90), 73424-73433. doi:10.1039/c5ra06889d 144 Wang, Z. H., Qiu, T., Guo, L. H., Ye, J., He, L. F., & Li, X. Y. (2018). The synthesis of molecular recognition polymer particles via miniemulsion polymerization. Reactive & Functional Polymers, 126, 1-8. doi:10.1016/j.reactfunctpolym.2018.02.013 Warkiani, M. E., Guan, G., Luan, K. B., Lee, W. C., Bhagat, A. A., Chaudhuri, P. K., . . . Han, J. (2014). Slanted spiral microfluidics for the ultra-fast, label-free isolation of circulating tumor cells. Lab Chip, 14(1), 128-137. doi:10.1039/c3lc50617g Warkiani, M. E., Khoo, B. L., Wu, L., Tay, A. K., Bhagat, A. A., Han, J., & Lim, C. T. (2016). Ultra-fast, label-free isolation of circulating tumor cells from blood using spiral microfluidics. Nature Protocols, 11(1), 134-148. doi:10.1038/nprot.2016.003 Wei, H., Abtahi, S. M. H., & Vikesland, P. J. (2015). Plasmonic colorimetric and SERS sensors for environmental analysis. Environmental Science-Nano, 2(2), 120-135. doi:10.1039/c4en00211c Whitesides, G. M. (2006). The origins and the future of microfluidics. Nature, 442(7101), 368-373. doi:10.1038/nature05058 Willets, K. A., & Van Duyne, R. P. (2007). Localized surface plasmon resonance spectroscopy and sensing. Annual Review of Physical Chemistry, 58, 267-297. doi:10.1146/annurev.physchem.58.032806.104607 Wolfbeis, O. S. (2015). An overview of nanoparticles commonly used in fluorescent bioimaging. Chemical Society Reviews, 44(14), 4743-4768. doi:10.1039/c4cs00392f Wu, G. H., Dong, C., Li, Y. L., Wang, Z. Q., Gao, Y. X., Shen, Z. Y., & Wu, A. G. (2015). A novel AgNPs-based colorimetric sensor for rapid detection of Cu2+ or Mn2+ via pH control. Rsc Advances, 5(26), 20595-20602. doi:10.1039/c5ra00001g 145 Wu, M., & Algar, W. R. (2015). Acceleration of proteolytic activity associated with selection of thiol ligand coatings on quantum dots. ACS Appl Mater Interfaces, 7(4), 2535-2545. doi:10.1021/am507466b Wu, Y. T., Zhang, Y. H., Zhang, M., Liu, F., Wan, Y. C., Huang, Z., . . . Lu, B. (2014). Selective and simultaneous determination of trace bisphenol A and tebuconazole in vegetable and juice samples by membrane-based molecularly imprinted solid-phase extraction and HPLC. Food Chemistry, 164, 527-535. doi:10.1016/j.foodchem.2014.05.071 Wu, Z., Willing, B., Bjerketorp, J., Jansson, J. K., & Hjort, K. (2009). Soft inertial microfluidics for high throughput separation of bacteria from human blood cells. Lab Chip, 9(9), 1193-1199. doi:10.1039/b817611f Wulff, G. (1995). Molecular Imprinting in Cross-Linked Materials with the Aid of Molecular Templates - a Way Towards Artificial Antibodies. Angewandte Chemie-International Edition in English, 34(17), 1812-1832. doi:DOI 10.1002/anie.199518121 Xiao, L., Zhang, Z., Wu, C., Han, L., & Zhang, H. (2017). Molecularly imprinted polymer grafted paper-based method for the detection of 17beta-estradiol. Food Chemistry, 221, 82-86. doi:10.1016/j.foodchem.2016.10.062 Xie, X., Pu, H., & Sun, D. W. (2018). Recent advances in nanofabrication techniques for SERS substrates and their applications in food safety analysis. Crit Rev Food Sci Nutr, 58(16), 2800-2813. doi:10.1080/10408398.2017.1341866 Xiong, D., & Li, H. (2008). Colorimetric detection of pesticides based on calixarene modified silver nanoparticles in water. Nanotechnology, 19(46), 465502. doi:10.1088/0957-4484/19/46/465502 146 Xu, F., Ren, K., Yang, Y. Z., Guo, J. P., Ma, G. P., Liu, Y. M., . . . Li, X. B. (2015). Immunoassay of chemical contaminants in milk: A review. Journal of Integrative Agriculture, 14(11), 2282-2295. doi:10.1016/S2095-3119(15)61121-2 Xu, S., & Lu, H. (2015). One-pot synthesis of mesoporous structured ratiometric fluorescence molecularly imprinted sensor for highly sensitive detection of melamine from milk samples. Biosens Bioelectron, 73, 160-166. doi:10.1016/j.bios.2015.05.064 Xu, S., Lu, H., Li, J., Song, X., Wang, A., Chen, L., & Han, S. (2013). Dummy molecularly imprinted polymers-capped CdTe quantum dots for the fluorescent sensing of 2,4,6-trinitrotoluene. ACS Appl Mater Interfaces, 5(16), 8146-8154. doi:10.1021/am4022076 Yang, H. H., Zhou, W. H., Guo, X. C., Chen, F. R., Zhao, H. Q., Lin, L. M., & Wang, X. R. (2009). Molecularly imprinted polymer as SPE sorbent for selective extraction of melamine in dairy products. Talanta, 80(2), 821-825. doi:10.1016/j.talanta.2009.07.067 Yang, L., & Li, Y. (2006). Simultaneous detection of Escherichia coli O157:H7 and Salmonella Typhimurium using quantum dots as fluorescence labels. Analyst, 131(3), 394-401. doi:10.1039/b510888h Yang, Y., Wang, Z., Niu, H., & Zhang, H. (2016). One-pot synthesis of quantum dot-labeled hydrophilic molecularly imprinted polymer nanoparticles for direct optosensing of folic acid in real, undiluted biological samples. Biosens Bioelectron, 86, 580-587. doi:10.1016/j.bios.2016.07.056 Yao, Y., Zhou, Y., Dai, J., Yue, S., & Xue, M. (2014). Host-guest recognition-induced color change of water-soluble pillar[5]arene modified silver nanoparticles for visual detection of spermine analogues. Chem Commun (Camb), 50(7), 869-871. doi:10.1039/c3cc48358d 147 Yaseen, T., Pu, H. B., & Sun, D. W. (2018). Functionalization techniques for improving SERS substrates and their applications in food safety evaluation: A review of recent research trends. Trends in Food Science & Technology, 72, 162-174. doi:10.1016/j.tifs.2017.12.012 Yi, Z., Yi, Y., Luo, J. S., Ye, X., Wu, P. H., Ji, X. C., . . . Tang, Y. J. (2015). Experimental and simulative study on surface enhanced Raman scattering of rhodamine 6G adsorbed on big bulk-nanocrystalline metal substrates. Rsc Advances, 5(3), 1718-1729. doi:10.1039/c4ra06141a Yu, J. L., Wang, X. Y., Kang, Q., Li, J. H., Shen, D. Z., & Chen, L. X. (2017). One-pot synthesis of a quantum dot-based molecular imprinting nanosensor for highly selective and sensitive fluorescence detection of 4-nitrophenol in environmental waters. Environmental Science-Nano, 4(2), 493-502. doi:10.1039/c6en00395h Yu, W. W., Qu, L. H., Guo, W. Z., & Peng, X. G. (2003). Experimental determination of the extinction coefficient of CdTe, CdSe, and CdS nanocrystals. Chemistry of Materials, 15(14), 2854-2860. doi:10.1021/cm034081k Yuan, S., Deng, Q., Fang, G., Wu, J., Li, W., & Wang, S. (2014). Protein imprinted ionic liquid polymer on the surface of multiwall carbon nanotubes with high binding capacity for lysozyme. Journal of Chromatography. B: Analytical Technologies in the Biomedical and Life Sciences, 960, 239-246. doi:10.1016/j.jchromb.2014.04.021 Zaidi, S. A. (2020). Molecular imprinting: A useful approach for drug delivery. Materials Science for Energy Technologies, 3, 72-77. doi:10.1016/j.mset.2019.10.012 Zhang, D. R., Pu, H. B., Huang, L. J., & Sun, D. W. (2021). Advances in flexible surface-enhanced Raman scattering (SERS) substrates for nondestructive food detection: 148 Fundamentals and recent applications. Trends in Food Science & Technology, 109, 690-701. doi:10.1016/j.tifs.2021.01.058 Zhang, S. Z., Wang, Y., Onck, P., & den Toonder, J. (2020). A concise review of microfluidic particle manipulation methods. Microfluidics and Nanofluidics, 24(4). doi:ARTN 24 10.1007/s10404-020-2328-5 Zhang, W., Tang, S., Jin, Y., Yang, C., He, L., Wang, J., & Chen, Y. (2020). Multiplex SERS-based lateral flow immunosensor for the detection of major mycotoxins in maize utilizing dual Raman labels and triple test lines. Journal of Hazardous materials, 393, 122348. doi:10.1016/j.jhazmat.2020.122348 Zhang, Z. (2017). Chapter 1. Raman Spectroscopic Sensing in Food Safety and Quality Analysis. In Sensing Techniques for Food Safety and Quality Control (pp. 1-16): The Royal Society of Chemistry. Zhang, Z., Li, J., Wang, X., Shen, D., & Chen, L. (2015). Quantum dots based mesoporous structured imprinting microspheres for the sensitive fluorescent detection of phycocyanin. ACS Appl Mater Interfaces, 7(17), 9118-9127. doi:10.1021/acsami.5b00908 Zhao, C., Zhao, T., Liu, X., & Zhang, H. (2010). A novel molecularly imprinted polymer for simultaneous extraction and determination of sudan dyes by on-line solid phase extraction and high performance liquid chromatography. Journal of Chromatography A, 1217(45), 6995-7002. doi:10.1016/j.chroma.2010.09.005 Zhao, Y., Tan, G., Wang, M., Lin, H., He, L., Li, L., & Wang, B. (2019). Application of Immunoassays for Rapid Monitor of Carbofuran Residue in Vegetables. Journal of Food Science, 84(11), 3296-3302. doi:10.1111/1750-3841.14826 149 Zheng, J., & He, L. (2014). Surface-Enhanced Raman Spectroscopy for the Chemical Analysis of Food. Compr Rev Food Sci Food Saf, 13(3), 317-328. doi:10.1111/1541-4337.12062 Zhou, J. W., Zou, X. M., Song, S. H., & Chen, G. H. (2018). Quantum Dots Applied to Methodology on Detection of Pesticide and Veterinary Drug Residues. J Agric Food Chem, 66(6), 1307-1319. doi:10.1021/acs.jafc.7b05119 Zhou, Q., Li, X., Fan, Q., Zhang, X., & Zheng, J. (2006). Charge transfer between metal nanoparticles interconnected with a functionalized molecule probed by surface-enhanced Raman spectroscopy. Angew Chem Int Ed Engl, 45(24), 3970-3973. doi:10.1002/anie.200504419 Zhou, T., Zhang, K., Kamra, T., Bulow, L., & Ye, L. (2015). Preparation of protein imprinted polymer beads by Pickering emulsion polymerization. J Mater Chem B, 3(7), 1254-1260. doi:10.1039/c4tb01605j
Thesis/Dissertation
eng
Food Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Lu, Xiaonan
Scaman, Christine H.
Development of novel molecularly imprinting chemosensors for the detection of chemical hazards in foods
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