Land and Food Systems, Faculty of
DSpace
UBCV
Wang, Kaidi
Master of Science - MSc
University of British Columbia
Rabid and accurate identification of Arcobacter species is of great importance because these bacteria have been considered as emerging foodborne pathogens and potential zoonotic agents. Raman spectroscopy has the ability to differentiate bacteria based upon Raman scattering spectral features of bacterial whole cells, which is fast, reagentless, and easy to perform. Thus, we aimed to detect and discriminate Arcobacter at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with chemometric analysis. A total of 82 isolates of 18 Arcobacter species from clinical, environmental and agri-food sources in both Canada and Germany were included. The genus Arcobacter could be successfully differentiated from closely related genera Campylobacter and Helicobacter using Raman spectroscopy via a principal component analysis model. We also determined that bacterial cultivation time and temperature did not significantly influence the spectral reproducibility and the discrimination capability of Raman spectroscopy. For the identification of Arcobacter to the species level, an overall accuracy of 94.13% was achieved for all 18 Arcobacter species by using Raman spectroscopy in combination with machine learning using a convolutional neural network. Furthermore, a back-propagation neural network was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass with an accuracy of over 99%. Finally, Raman spectroscopy showed the ability to detect trace level (10°-10¹ CFU/mL) of Arcobacter from food sample (i.e., milk) after enrichment. The knowledge received from this study can be applied to further investigate the epidemiology of Arcobacter in the food chain.
DETECTION AND SPECIATION OF ARCOBACTER BACTERIA USING RAMAN SPECTROSCOPY by Kaidi Wang B.Sc., Zhejiang University, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Food Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2019 © Kaidi Wang, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled: Detection and speciation of Arcobacter bacteria using Raman spectroscopy submitted by Kaidi Wang in partial fulfillment of the requirements for the degree of Master of Science in Food Science Examining Committee: Xiaonan Lu Supervisor Keng Chou Supervisory Committee Member Supervisory Committee Member Carolina Tropini Additional Examiner Additional Supervisory Committee Members: Supervisory Committee Member Supervisory Committee Member iii Abstract Rabid and accurate identification of Arcobacter species is of great importance because these bacteria have been considered as emerging foodborne pathogens and potential zoonotic agents. Raman spectroscopy has the ability to differentiate bacteria based upon Raman scattering spectral features of bacterial whole cells, which is fast, reagentless, and easy to perform. Thus, we aimed to detect and discriminate Arcobacter at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with chemometric analysis. A total of 82 isolates of 18 Arcobacter species from clinical, environmental and agri-food sources in both Canada and Germany were included. The genus Arcobacter could be successfully differentiated from closely related genera Campylobacter and Helicobacter using Raman spectroscopy via a principal component analysis model. We also determined that bacterial cultivation time and temperature did not significantly influence the spectral reproducibility and the discrimination capability of Raman spectroscopy. For the identification of Arcobacter to the species level, an overall accuracy of 94.13% was achieved for all 18 Arcobacter species by using Raman spectroscopy in combination with a convolutional neural network. Furthermore, a Raman spectroscopy-based back-propagation neural network model was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass with accuracy over 99%. Finally, Raman spectroscopy showed the ability to detect trace level (100-101 CFU/mL) of Arcobacter from real food sample (i.e., milk) after enrichment. In conclusion, a rapid, robust and reliable analytical approach for the identification and speciation of Arcobacter bacteria using Raman spectroscopy has been developed. The knowledge received from this study can be applied to further investigate the epidemiology of Arcobacter in the food chain. iv Lay Summary The genus Arcobacter is an emerging foodborne pathogen and becomes more important in the recent decades. This genus consists of over 27 species that have been isolated from humans, animals, water and food sources. However, the incidence of Arcobacter species is probably underestimated mainly due to the limitations in the current detection and characterization techniques. Raman spectroscopy is a vibrational spectroscopic technique that can provide biochemical fingerprinting information of bacterial cells, thus has the potential to detect and discriminate various Arcobacter species. By using 82 strains of 18 Arcobacter species, we developed a fast, easy-to-perform and reliable method for the identification of Arcobacter to the species level using Raman spectroscopy and mathematical models. This thesis project provides a promising tool for rapid detection of Arcobacter in the food chain, which has the potential to be employed as a standard method for other bacterial detection. v Preface Subsection 2.8, Raman spectral baseline correction, establishment of back-propagation neural network and convolutional neural network were conducted using the Matlab codes written by Dr. Lei Chen in Lu Food Safety and Health Engineering Lab at the University of British Columbia. The author, Kaidi Wang, carried out the experimental work, tested the performance of the models and wrote the thesis. The rest of this work was completed solely by the author, Kaidi Wang, under the guidance of Dr. Xiaonan Lu. The work is original and has not been previously published. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures .................................................................................................................................x List of Abbreviations ................................................................................................................. xiii Acknowledgements .................................................................................................................... xiv Dedication ................................................................................................................................... xvi Chapter 1: Introduction ................................................................................................................1 Arcobacter....................................................................................................................... 1 Taxonomic classification ............................................................................................ 1 Physical description .................................................................................................... 3 Clinical importance ..................................................................................................... 4 1.1.3.1 Arcobacter in humans ......................................................................................... 4 1.1.3.2 Arcobacter in animals ......................................................................................... 6 Transmission and reservoirs ....................................................................................... 7 1.1.4.1 Routes of transmission ........................................................................................ 7 1.1.4.2 Arcobacter in water............................................................................................. 8 1.1.4.3 Arcobacter in foods............................................................................................. 9 Detection and identification of Arcobacter ................................................................... 11 vii Raman spectroscopy ..................................................................................................... 14 Mechanism ................................................................................................................ 14 Instrumentation of Raman spectroscopy ................................................................... 16 Advantages of Raman spectroscopy ......................................................................... 18 Application in bacterial detection ............................................................................. 19 Data analysis of Raman spectra .................................................................................... 22 Pre-processing ........................................................................................................... 22 Chemometric models ................................................................................................ 24 1.4.2.1 Principle component analysis ........................................................................... 26 1.4.2.2 Hierarchical cluster analysis ............................................................................. 27 1.4.2.3 Back-propagation neural network ..................................................................... 27 1.4.2.4 Convolutional neural network........................................................................... 29 Hypothesis and objectives............................................................................................. 30 Chapter 2: Materials and methods .............................................................................................31 Bacterial strains and growth conditions ........................................................................ 31 Sample preparation ....................................................................................................... 34 Raman spectroscopic instrumentation .......................................................................... 34 Identification of Arcobacter at genus level ................................................................... 35 Influence of cultivation condition on Raman spectral reproducibility ......................... 36 Identification of a specific Arcobacter species in a bacterial mixture .......................... 36 Detection of Arcobacter in real food sample ................................................................ 37 Raman spectral analyses ............................................................................................... 38 Pre-processing of Raman spectra .............................................................................. 38 viii Classification chemometric models .......................................................................... 39 Chapter 3: Results........................................................................................................................43 Identification of genus Arcobacter from other closely related bacteria ....................... 43 The influence of cultivation conditions on Raman spectral reproducibility ................. 45 Raman spectra from different Arcobacter species ........................................................ 48 Identification of Arcobacter species to the species level .............................................. 51 Sensitivity of Raman spectroscopy to detect a specific Arcobacter within a mixture.. 55 Detection of Arcobacter in milk ................................................................................... 57 Chapter 4: Discussion ..................................................................................................................62 Chapter 5: Conclusion .................................................................................................................70 Study summary ............................................................................................................. 70 Future directions ........................................................................................................... 71 References .....................................................................................................................................73 Appendix .......................................................................................................................................87 ix List of Tables Table 2.1 Summary of bacterial strains used in this study. .......................................................... 32 Table 3.1 Inter-group Mahalanobis distance between different genera in principal component analysis model (Figure 3.1)........................................................................................................... 44 Table 3.2 Inter-group Mahalanobis distance between different Arcobacter species cultivated under different conditions in principal component analysis model (Figure 3.2). ......................... 48 Table 3.3 Raman bands observed in the spectra of Arcobacter species and their tentative assignments. .................................................................................................................................. 51 Table 3.4 Cross-validation results of convolutional neural network model for the classification of Arcobacter bacteria at species level (n=240). ............................................................................... 54 Table 3.5 Detection of Arcobacter in milk after enrichment using Raman spectroscopy. ........... 61 x List of Figures Figure 1.1 A schematic illustration of a typical Raman spectroscopic system. Image is referenced from the work conducted by Butler and colleagues (Butler et al., 2016). .................................... 17 Figure 1.2 Schematic illustration of a back-propagation neural network model containing input, hidden (with “n” neurons) and output layers. Figure is adapted from a previous work conducted by Z.-Y. Zhang and Wang (Z.-Y. Zhang & Wang, 2014) ............................................................ 28 Figure 1.3 Schematic illustration of a convolutional neural network containing two convolutional hidden layers. Image is adapted from a previous work conducted by Albelwi and Mahmood (Albelwi & Mahmood, 2017). ...................................................................................................... 30 Figure 3.1 Principal component analysis plot of Raman spectra from representative bacterial strains of 3 different genera Arcobacter (blue), Campylobacter (red) and Helicobacter (yellow). A. butzleri CCUG 30485, A. skirrowii CCUG 10374, A. cryaerophilus DSM 7289, C. jejuni ATCC 33560, C. coli 314 and H. pylori ATCC 43504 were used as the representative strains of different genera. ............................................................................................................................ 44 Figure 3.2 Influence of bacterial cultivation time (A) and incubation temperature (B) on Raman spectral reproducibility. (A) Principal component analysis (PCA) plot was constructed using Raman spectra of 3 Arcobacter species with different cultivation times ranging from 36 h to 72 h. (B) PCA plot was constructed using Raman spectra of 3 Arcobacter species cultivated at two different temperatures of 30°C and 37°C. Tight clusters were formed for each species with xi different cultivation time or temperature, indicating the discriminatory capacity of Raman spectroscopic-based method is not significantly affected by either culture time or temperature. 47 Figure 3.3 Average Raman spectra of 18 different Arcobacter species recorded with an excitation wavelength of 785 nm. Spectral regions with distinctive visible variations between different species were highlighted in shadow grey. Major Raman bands were identified and their tentative band assignments are summarized in Table 3.3. ........................................................................... 50 Figure 3.4 Prediction of the actual ratio of a specific Arcobacter species within a bacterial mixture using back-propagation neural network (BPNN). A. butzleri was mixed with a bacterial cocktail of A. aquimarinus, A. trophiarum and A. lanthieri at different ratios of 5%, 25%, 50%, 75% and 100% by biomass, respectively. BPNN model was evaluated with “leave-one-out” cross-validation and red dots represent the relative error of the predicted ratio compared to the actual ratio of A. butzleri (n=10). R2: coefficient of determination; RMSE: root mean square error. .............................................................................................................................................. 56 Figure 3.5 Comparison of Raman spectra collected from A. butzleri in milk with different enrichment times as well as in pure bacterial culture. Initial spiking concentration of A. butzleri was 5.7×100 CFU/mL and the spiked milk was mixed with MH broth at a ratio of 1:9 (v/v) for enrichment. Shaded areas indicate the marked spectral variations among different Raman spectra. .......................................................................................................................................... 59 xii Figure 3.6 Principal component analysis plot of Raman spectra for A. butzleri in milk with different enrichment times. Initial spiking concentration of A. butzleri was 5.7×100 CFU/mL and the spiked milk was mixed with MH broth at a ratio of 1:9 (v/v) for enrichment. Dots labeled with number in different colors represent Raman spectra collected from A. butzleri in milk at different time points (h) and red pentagrams represent Raman spectra of A. butzleri in pure bacterial culture. Arrows indicate that Raman spectral features of Arcobacter in milk changed in a specific trend along with the enrichment time. .......................................................................... 60 xiii List of Abbreviations 3D 3-dimentional ANN Artificial neural network BPNN Back-propagation neural network CNN Convolutional neural network CCD charge-coupled device ddH2O sterile double-deionized water HCA hierarchical cluster analysis MALDI-TOF MS matrix-assisted laser desorption ionization–time-of-flight mass spectrometry MD Mahalanobis distance NIR near-infrared PC principle component PCA principal component analysis PCR polymerase chain reaction PLSR partial least squares regression R2 coefficient of determination RMSE root mean square error SVM support vector machines UHT ultra-high temperature UTI urinary tract infection UV ultraviolet xiv Acknowledgements First and foremost, I would like to express my sincere gratitude to my supervisor, Dr. Xiaonan Lu, for offering continuous guidance to my project, for teaching me knowledge and skills, for all the support and encouragement through my master’s study. Thanks for always cheering me up when I was stressed out and thanks for the faith in my abilities. I highly appreciate the opportunity to continue my Ph.D. study under his supervision. Secondly, I want to thank my committee members Dr. Susan Bach and Dr. Keng C. Chou, for their time commitment and valuable suggestions to my research project. My special thanks also go to Dr. Izar Khan and Dr. Greta Gölz, who provided the bacterial strains and gave professional comments on my experimental design. I also would like to acknowledge Dr. Lei Chen for his great assistance and support in data analysis using Matlab software. I am grateful for the financial support from Mitacs, UBC Affiliated Fellowships and UBC Faculty of Land and Food Systems. I would also like to thank all the Lu lab members for their help in many ways and all the laugher we have shared. Meeting such wonderful people has made my study journey colorful and memorable. xv My special thanks are owed to my best friend, Mei Geng. Thanks for always sharing my pleasure and pain, supporting me whenever possible and treating me like a family during the last ten years. Last but not least, I offer my heartfelt thanks to my boyfriend, my little sister and my beloved parents, for their endless love, support and belief in me. Thank you for being my biggest cheerleaders, my motivation and my strength. xvi Dedication This work is dedicated to my family for their endless love, support and encouragement. 1 Chapter 1: Introduction Arcobacter The genus Arcobacter has been considered as an emerging foodborne pathogen and potential zoonotic agent in the recent years (Jyothsna, Rahul, Ramaprasad, Sasikala, & Ramana, 2013). This genus currently comprises over 27 published species isolated from various environmental, animal, agri-food, and human sources (González, Morejón, & Ferrús, 2017; Pérez-Cataluña, Salas-Massó, Diéguez, et al., 2018; Whiteduck-Léveillée et al., 2015; Z. Zhang, Yu, Wang, Yu, & Zhang, 2016). The most frequently reported species are A. butzleri, A. cryaerophilus, and A. skirrowii, which have been associated with human gastroenteritis and bacteraemia, as well as abortion, infertility and mastitis in animals (Shah et al., 2013; Vandamme, Vancanneyt, et al., 1992). Arcobacter is presumably transmitted to humans by the contaminated foods and water, with poultry and pork products being identified as the most important reservoirs. However, there is limited data related to the epidemiology, virulence genetics as well as infectious dose of Arcobacter. Taxonomic classification The Arcobacter genus belongs to the family Campylobactereaceae with the closely related genus Campylobacter. Helicobacter within the family Helicobacteraceae is another closely related genus. Both Campylobacteraceae and Helicobacteraceae belong to the same order Campylobacterales. The bacteria in Arcobacter genus were previously classified as aerotolerant Campylobacter with the ability to grow at lower temperatures. The genus Arcobacter was formally proposed in 1991, consisting of two species A. nitrofigilis and A. 2 cryaerophilus (Vandamme et al., 1991). Then, two more species A. butzleri obtained from humans and A. skirrowii isolated from the fecal sample of lambs were included in this genus in 1992 (Vandamme, Vancanneyt, et al., 1992). Another two species were described in 2005. A. halophilus was firstly isolated from a hypersaline lagoon (Donachie, Bowman, On, & Alam, 2005) and A. cibarius was recovered from the broiled carcasses (Houf et al., 2005). Since 2008, a number of new species have been recognized progressively, reaching to a total number of 27 in 2017. Most species were isolated from livestock, shellfish, marine environments and sewage. For example, A. trophiarum was recovered from feces of pigs and cattle (De Smet, Vandamme, et al., 2011; Whiteduck-Léveillée et al., 2015); A. mytili (Collado, Cleenwerck, Van Trappen, De Vos, & Figueras, 2009), A. bivalviorum (Levican et al., 2012), A. ebronensis (Levican, Rubio-Arcos, Martinez-Murcia, Collado, & Figueras, 2015) and A. lekithochrous (Diéguez, Balboa, Magnesen, & Romalde, 2017) were isolated from shellfish; A. pacificus (Z. Zhang et al., 2016) and A. aquimarinus (Levican et al., 2015) were described from seawater; A. cloacae and A. defluvii were isolated from sewage (Levican, Collado, & Figueras, 2013). The detailed information was summarized in a recent review paper (Pérez-Cataluña, Salas-Massó, Diéguez, et al., 2018). The taxonomic assignation of the genus Arcobacter is mainly based upon the similarity of 16S rRNA gene (cut-off value of 95%), while phylogeny on the basis of housekeeping genes (rpoB, gyrB and hsp60) has been also used as an additional tool for the assignation at species level (Pérez-Cataluña, Salas-Massó, Diéguez, et al., 2018). It was reported that the Arcobacter genus might be subdivided into two or more genera in the future based upon the 3 genetic differences of 16S rRNA between some strains as well as their variable ecological niches (Banting & Figueras, 2017). Physical description Arcobacter species are Gram-negative and non-spore-forming bacteria. They are curved, spiral or helical-shaped cells of 0.2-0.9 µm wide and 0.5-3 µm long (Vandamme, Vancanneyt, et al., 1992). Arcobacter species have motility by means of single polar flagellum at one or both ends of the cell. They exhibit aerotolerance of at least 5% O2 and are able to grow both aerobically and microaerobically. Arcobacter species can survive at 15°C to 42°C with the optimal growth at 30°C in microaerobic conditions. All species are oxidase positive, but some species are lack of catalase. The capability of aerotolerance and growth at a relatively lower temperature (15°C-30°C) leads to the differentiation of Arcobacter from Campylobacter (Collado & Figueras, 2011). However, the original description of the genus Arcobacter has been emended due to the incorporation of new species with atypical characteristics. For example, one recently identified species A. anaerophilus is lack of motility and obligate anaerobic (Jyothsna et al., 2013). A. mytili was the first specie of this genus that is unable to hydrolyze indoxyl acetate (Collado et al., 2009). A. halophilus is another atypical member, which is obligate halophilic and grows in the media supplemented with at least 2% NaCl (w/v) (Donachie et al., 2005). The specific culture methods should be optimized to enable the description of novel Arcobacters. 4 Clinical importance 1.1.3.1 Arcobacter in humans Among all the Arcobacter species, 4 species namely A. butzleri, A. cryaerophilus, A. skirrowii and A. thereius have been isolated from humans (Banting & Figueras, 2017). These species have been associated with gastrointestinal diseases in humans with chronic watery diarrhea as the main symptom (Collado & Figueras, 2011). Cases of enteritis, colitis and bacteremia attributed to Arcobacter have been reported in the previous studies (Van den Abeele, Vogelaers, Van Hende, & Houf, 2014). Recently, a patient tested positive for Arcobacter also showed the symptoms of fever and abdominal pain (Yap, Kwan, To, & Chan, 2013). There is limited data regarding the prevalence of Arcobacter in humans. In two large studies performed in Belgium, Arcobacter was recovered from human fecal samples at rates ranging from 0.2% (Vandenberg et al., 2004) to 1.3% (Van den Abeele et al., 2014) for over 47,000 patients. The isolation of Arcobacter in 8% of traveller’s diarrheal stools was reported in another study (Jiang et al., 2010). Several studies have also identified Arcobacter in feces of healthy people. For example, A. cryaerophilus was isolated from 1.4% of healthy workers in a slaughterhouse (Houf & Stephan, 2007). A higher prevalence of Arcobacter was observed when using molecular detection methods than the cultural methods probably due to the presence of some uncultured strains/species. For example, 0.4% of 500 stool samples showed positive results for A. butzleri by using PCR, but were failed to be recovered by the culture-based methods (de Boer et al., 2013). In population studies, the prevalence of Arcobacter in diarrheic stool samples in South Africa varied from 0.1% using the cultural methods 5 (Lastovica & le Roux, 2000) to 12.9% using the molecular detection methods (Samie, Obi, Barrett, Powell, & Guerrant, 2007). Since the methods used in different studies were not consistent, cautions should be taken for result interpretation. Although the prevalence varies geographically in different studies, A. butzleri is the most frequently reported species, followed by A. cryaerophilus and A. skirrowii (On et al., 2002). Several studies indicated that A. butzleri was the 4th most prevalent Campylobacter-related bacteria isolated from patients with diarrhea (Cai, Ju, & Zhang, 2014; Collado, Gutiérrez, González, & Fernández, 2013; Ferreira, Júlio, Queiroz, Domingues, & Oleastro, 2014). In another study performed in Canada, incidence rates of A. butzleri in diarrheic and non-diarrheic individuals were 56.7% and 45.5% by using molecular detection method, respectively (Webb et al., 2016). A. butzleri and A. cryaerophilus are likely the most clinically significant Arcobacter species based upon the detection rates in humans to date (Banting & Figueras, 2017). Regarding A. skirrowii, it was first detected from an elderly patient with chronic diarrhoea in 2004 (Wybo, Breynaert, Lauwers, Lindenburg, & Houf, 2004), and then only reported in two clinical cases so far (Patyal, Rathore, Mohan, Dhama, & Kumar, 2011; Samie et al., 2007). Recently, A. thereius was recovered from fecal samples of two patients with enterocolitis (Van den Abeele et al., 2014). The pathogenicity and virulence mechanisms of Arcobacter species have not been well understood. In vitro human and animal cell culture assays indicated that Arcobacter species can adhere to and invade several cell lines and cause cytotoxicity (Collado & Figueras, 2011; Ferreira, Queiroz, Oleastro, & Domingues, 2016). Several studies also demonstrated that 6 Arcobacter has the potential to cause tight junction dysfunction and subsequently induce diarrhea (Bücker, Troeger, Kleer, Fromm, & Schulzke, 2009; Karadas et al., 2016). Presumptive genes, such as cadF, cj1349, ciaB, mviN and hecB, have been identified in a variety of Arcobacter species (Ferreira et al., 2016), although there is no clear relationship between the presence of these genes and the pathogenic capacity. More work is required to better understand the human clinical outcomes and pathogenic mechanisms of Arcobacter. 1.1.3.2 Arcobacter in animals Arcobacter has been isolated from various animals. It has been associated with enteritis, diarrhea and mastitis in animals, and may play a role in animal reproduction problems, such as infertility and abortions in cattle, pigs and sheep (Ferreira et al., 2016). A. cryaerophilus, A. butzleri, and A. skirrowii are the predominant species related to animal infections (Collado & Figueras, 2011). Recently, A. thereius (Houf et al., 2009) was also identified in livers and kidneys of pigs with abortions. Arcobacter has also been frequently isolated from the feces and intestinal tracts of healthy livestock animals, including pigs, cattle, horses and sheep (Hsu & Lee, 2015). Apart from the aforementioned three commonly reported zoonotic species, A. lanthieri (Whiteduck-Léveillée et al., 2016), A. trophiarum (De Smet, De Zutter, et al., 2011), A. suis (Levican, Alkeskas, Günter, Forsythe, & Figueras, 2013), A. cibarius (Chinivasagam, Corney, Wright, Diallo, & Blackall, 2007) and A. thereius (Houf et al., 2009) have been isolated from pig manure, meat or organs, indicating that pigs have a high probability of Arcobacter contamination. Furthermore, Arcobacter has also been detected in water samples and 7 facilities of livestock farms (Giacometti, Lucchi, et al., 2015). Multiple locations in dairy farms and different types of dairy products, such as water buffalo milk, raw milk and cheese, have been associated with Arcobacter contamination as well (Giacometti, Salas-Massó, Serraino, & Figueras, 2015; Hsu & Lee, 2015). Another significant source of Arcobacter is poultry, such as chicken, ducks, turkeys and geese (Hsu & Lee, 2015). The prevalence rate of Arcobacter in chicken meat/carcasses can reach to 96% reported in a previous study (Son, Englen, Berrang, Fedorka-Cray, & Harrison, 2007). Although there is no disease linked to Arcobacter in poultry, consumption of chicken contaminated by Arcobacter has been considered as the reason of some foodborne outbreaks (Lappi et al., 2013). Transmission and reservoirs 1.1.4.1 Routes of transmission Although formal transmission linkages are not confirmed yet, consumption of Arcobacter-contaminated foods and water are the likely routes of transmission to humans and animals (Banting & Figueras, 2017). Arcobacter species have been recovered from both patients and/or contaminated drinking water/foods in several outbreaks (Fong et al., 2007; Lappi et al., 2013). Contact with pet cats and dogs is suggested as a possible transmission route of Arcobacter infections due to the presence of A. butzleri and A. cryaerophilus in these pet animals (Fera, La Camera, Carbone, Malara, & Pennisi, 2009; Houf, De Smet, Baré, & Daminet, 2008). In addition, all strains isolated from fecal samples of patients showed identical phenotype and genotype in an Arcobacter-associated outbreak, indicating that 8 Arcobacter might be transmitted from person to person (Vandamme, Pugina, et al., 1992). However, more extensive surveillance studies are required to confirm the transmission routes of Arcobacter in order to reduce the foodborne and waterborne infections. 1.1.4.2 Arcobacter in water Water is an important transmission vehicle of Arcobacter. Arcobacter has been isolated from a variety of water environments, including drinking water, seawater, ground water, recreational water and sewage water. The global prevalence of Arcobacter in water has been detailed reviewed by various studies (Banting & Figueras, 2017; Collado & Figueras, 2011; Hsu & Lee, 2015). Sewage water is suggested as an important reservoir for Arcobacter. The presence of a great diversity of Arcobacter species in sewage water was reported with the prevalence ranging from 66.7% to 100% (Hsu & Lee, 2015). Seawater is another major source of Arcobacter especially the halophilic species, such as A. halophilus and A. marinus (Wirsen et al., 2002). Arcobacter has been related to several waterborne outbreaks. The first one was an outbreak of gastroenteritis occurred in the US, where A. butzleri was the only pathogen isolated from the contaminated well water (Rice, Rodgers, Wesley, Johnson, & Tanner, 1999). Arcobacter-contaminated well water was also associated with an outbreak causing 1450 cases of cramps and diarrhea (Fong et al., 2007). In another two waterborne outbreaks related to the contamination of drinking water system (Jalava et al., 2014; Kopilović, Ucakar, Koren, Krek, & Kraigher, 2008), Arcobacter was isolated from the tested water and/or stool samples along with multiple other pathogens. 9 It has been hypothesized that Arcobacter species are natural residents in the water environments (Fera et al., 2004). However, the occurrence of Arcobacter was closely associated with the faecal pollution, which could be a possible source of water contamination (González, Botella, Montes, Moreno, & Ferrus, 2007). Extreme rainfall might assist in the transport of Arcobacter from waste water into groundwater causing more human exposure. In addition, ineffective disinfection of drinking water has been considered as the cause of Arcobacter-related outbreaks as well (Rice et al., 1999). 1.1.4.3 Arcobacter in foods Consumption of the contaminated food products have been hypothesized as an important transmission route of Arcobacter. The prevalence of Arcobacter in various food products has been investigated, including poultry, beef, pork, dairy products, lamb, seafood and vegetables. The detailed information has been summarized in several previous works (Banting & Figueras, 2017; Hsu & Lee, 2015). Arcobacter was frequently identified in the intestinal tract and fecal samples of different animals as well as many food products of animal origin. Among these food products, poultry has the highest prevalence, followed by pork and beef (Banting & Figueras, 2017). It has been suggested that meat was probably contaminated by Arcobacter during slaughtering process when the carcasses contact with the contaminated fecal samples (Aydin, Gümüşsoy, Atabay, Ica, & Abay, 2007). Arcobacter has also been frequently identified in dairy products with the prevalence varied from 5.8% to 56% (Hsu & Lee, 2015). Most commonly identified species (e.g., A. butzleri, A. cryaerophilus and A. skirrowii) and other recently identified 10 species (e.g., A. nitrofigilis, A. cloacae and A. cibarius) were isolated from cow milk, water buffalo milk and cheese (Yesilmen, Vural, Erkan, & Yildirim, 2014). In addition, A. butzleri was isolated from environmental samples in artisanal dairy plant (Giacometti et al., 2013). Seafood is another potential reservoir of Arcobacter contamination. It has been reported that A. butzleri and A. molluscorum were the two major species isolated from various shellfish, such as mussels, oysters and clams (Collado & Figueras, 2011). Fresh produce, which are also usually used for raw consumption, could be a potential transmission route due to either irrigation or washing with Arcobacter-contaminated water (González & Ferrús, 2011). Arcobacter has been identified not only in raw food products but also in cooked meals. In a restaurant in Bangkok, the risk of exposure to Arcobacter per meal was 13% (Teague et al., 2010). Generally, A. butzleri is the most prevalent species identified in foods, followed by A. cryaerophilus and A. skirrowii (Collado & Figueras, 2011). The high occurrence rate of Arcobacter in food products might be explained by various factors. For example, Arcobacter can survive in the environment with high sodium chloride concentration and low temperature (15°C). They are able to attach to different types of surfaces as a result of their biofilm-forming ability and tolerance to desiccation (Collado & Figueras, 2011; Ferreira, Fraqueza, Queiroz, Domingues, & Oleastro, 2013). Ho and coauthors demonstrated that some Arcobacter isolates could survive in tap water at 52°C for 3 min, indicating that cross-contamination might occur during processing stages in poultry slaughterhouse (Ho, Lipman, & Gaastra, 2008). Taken together, the high prevalence of Arcobacter in waterbodies 11 indicates that more thorough treatment and monitoring for this emerging pathogen are required to ensure food safety and public health. Detection and identification of Arcobacter Although various culture-based and molecular-based methods have been employed for the identification and detection of Arcobacter, a standardized reference method has not been established yet (Banting & Figueras, 2017). Rapid and reliable identification methods are highly required to conduct the epidemiological surveillance, determine the transmission routes and investigate the global distribution of Arcobacter. Conventional bacterial identification methods generally require the growth of selective bacterial cultures under a specific condition and the subsequent morphological and biochemical evaluations of the isolated cells. The isolation procedures for Arcobacter bacteria usually include the enrichment step using selective media for at least 48 h, followed by cultivation on blood agar for another 48 to 72 h (Collado & Figueras, 2011). Arcobacter-specific broth supplemented with cefoperazone, amphotericin B, and teicoplanin is one of the most commonly used selective media for the isolation of Arcobacter (Atabay & Corry, 1997). The presumptive colonies are then selected and tested by morphological and biochemical assays, which are relatively laborious, costly and time consuming (i.e., at least 4 to 5 days) (Shah, Saleha, Murugaiyah, & Zakaria, 2011). Besides, either false negative or inconsistent results have been reported for the identification of Arcobacter using the classical biochemical tests (Debruyne, Gevers, & Vandamme, 2008). For example, Arcobacter and Campylobacter could not be differentiated in some cases using phenotypic methods due to 12 the high similarities in their phenotypic characteristics (González, Moreno, Gonzalez, Hernández, & Ferrus, 2006; Yan et al., 2000). The low sensitivity and specificity of culture-based methods probably result in under-estimation of the actual incidences of Arcobacter infections. To improve the sensitivity and reduce the detection time required by using the conventional methods, a variety of molecular methods have been developed for the identification of Arcobacter, such as polymerase chain reaction (PCR) (Houf, Tutenel, De Zutter, Van Hoof, & Vandamme, 2000), real-time PCR (Brightwell et al., 2007), sequencing of 16S rRNA (S. Lau, Woo, Teng, Leung, & Yuen, 2002), rpoB, rpoC (Morita et al., 2004) and gyrA genes (Abdelbaqi et al., 2007), 16S rRNA-restriction fragment length polymorphism (Figueras, Levican, & Collado, 2012), loop mediated isothermal amplification (X. Wang, Seo, Lee, & Choi, 2014) and microarray techniques (Quiñones, Parker, Janda, Miller, & Mandrell, 2007). However, most of these methods only target three prevalent species, namely A. butzleri, A. cryaerophilus and A. skirrowii, and are not able to accurately detect all the described Arcobacter species (Collado & Figueras, 2011). One of the most commonly used methods is the multiplex-PCR (m-PCR) developed by Houf and coauthors (Houf et al., 2000) and revised by Douidah and coworkers (Douidah, De Zutter, Vandamme, & Houf, 2010), which can detect several Arcobacter species simultaneously by using different pairs of species-specific primers targeting the 16S or 23S rRNA gene. However, misidentifications were observed in some closely related species or the recently proposed species (Collado et al., 2009; Collado et al., 2010; Scullion, Harrington, & Madden, 2006). Currently, DNA sequencing of specific genes (e.g., rpoB, gyrB and cpn60) is the most reliable and accurate 13 method for the speciation of Arcobacter (Banting & Figueras, 2017). However, it is relatively time-consuming and expensive. In addition, these molecular methods have other disadvantages, such as complicated sample preparation, laborious optimization of protocols, insufficient reproducibility or limited analytical capacity and they usually require highly trained personnel (González et al., 2017). Matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF MS) is another rapid method recently used for the identification of Arcobacter (Alispahic et al., 2010). This approach is based on the analysis of macromolecular compositions of the whole cells reflected in the MALDI-TOF MS. However, this technique usually requires expensive chemicals in specific sample preparation steps. In addition, the expression of the proteins may depend on the culture conditions or other biological processes (Hrabák, Chudáčková, & Walková, 2013). Vibrational spectroscopy (e.g., Raman spectroscopy and infrared spectroscopy) has emerged as an effective tool for rapid detection and identification of bacteria (Lu, Al-Qadiri, Lin, & Rasco, 2011). Spectral bands in the vibrational spectra are molecular specific and provide detailed and direct information about the biochemical compositions of a sample, such as a bacterium (Movasaghi, Rehman, & Rehman, 2007). Raman spectroscopy shows some major advantages over infrared spectroscopy for bacterial identification, such as less interference of water molecule and more spectral features that can be detected within the same wavenumber region (Lu, Al-Qadiri, et al., 2011). Thus, it has been more commonly used for the discrimination of different bacteria. The global spectral fingerprinting feature of bacterial cells generated by Raman spectroscopy demonstrates high discriminatory power and reproducibility for biological samples (Jarvis & Goodacre, 2004). In addition, Raman 14 spectroscopy is rapid, non-destructive, easy to perform and reagentless (Pahlow et al., 2015). Taken together, Raman spectroscopy shows a great potential as a rapid and reliable analytical technique for the identification and speciation of Arcobacter. More detailed information of Raman spectroscopy will be introduced in the following section. Raman spectroscopy Raman spectroscopy has been considered as a powerful analytical technique for biological applications. Raman spectroscopy is a vibrational spectroscopic technique that characterizes the changes in the polarizability of functional groups with different vibrational modes (Lu, Al-Qadiri, et al., 2011). It can provide comprehensive biochemical and compositional information of a sample without any labeling. Raman spectroscopy distinguishes itself from other established spectroscopic techniques because it is rapid, easy to conduct and non-invasive (Butler et al., 2016). Applications of this technique in biological studies have increased significantly in the recent years, especially for the detection and characterization of bacteria. Mechanism Raman spectroscopic technique is based upon the inelastic scattering (or Raman scattering) of a monochromatic light, which is usually generated from a laser source in the near-IR, visible or UV range (Butler et al., 2016). Raman scattering describes the loss or gain of photon energy due to the interaction of light with samples. The difference of energy between the incident and Raman-scattered photon is termed as Raman shift or wavenumber (represented as cm-1), which is associated with the vibrational modes of a specific chemical 15 bond (or a functional group) in the molecule (Choo‐Smith et al., 2002; Z. Huang et al., 2003). Raman spectrum is a plot of scattered intensity as a function of Raman shift measured by the detector. Raman shift is unique for individual molecule and thus Raman spectra can provide broad and detailed information about the biochemical compositions of the sample. Typically, Raman spectral wavenumber region between 400 and 1,800 cm−1 is defined as “fingerprint” region because the bands in this region denote most of the information of the whole bacterial cell, including proteins, lipids, nucleic acids and carbohydrates (Talari, Movasaghi, Rehman, & Rehman, 2015). Specifically, the wavenumber region between 1,500 and 1,700 cm-1 is associated with proteins; wavenumber between 1,500 and 1,200 cm-1 is a mixed region of proteins and fatty acids; carbohydrates are related to wavenumber region between 470-1,200 cm−1; spectral bands at 980, 1,080 and 1,240 cm−1 provide information of phosphate groups of DNA and other molecules (Clemens, Hands, Dorling, & Baker, 2014). In addition, vibrations of CH, NH and OH stretching in lipids and proteins are associated with the bands at higher wavenumbers (2,700–3,500 cm−1) (Talari et al., 2015). By measuring the Raman spectral patterns of the sample, a comprehensive biochemical fingerprint of the bacterial cells can be obtained and investigated. In studies associated with Raman spectroscopy, the accurate detection and reliable interpretation of Raman spectra are of great importance. Although various equipment or experimental procedures were used in different studies, spectral interpretation of comparable regions in the collected spectra showed high similarity. The assignment of the major Raman bands in biological samples has been investigated by a number of studies (De Gelder, De 16 Gussem, Vandenabeele, & Moens, 2007; Ellis, Cowcher, Ashton, O'Hagan, & Goodacre, 2013; Harz et al., 2005; W. E. Huang, Li, Jarvis, Goodacre, & Banwart, 2010; Jarvis, Brooker, & Goodacre, 2004; Maquelin et al., 2000; Maquelin et al., 2002), which serves as a reference library for spectral interpretation in the microbiological analysis. The most frequently reported Raman bands in bacterial cells are summarized in a review paper (Talari et al., 2015). Instrumentation of Raman spectroscopy Raman scattering is inherently weak, which occurs with an extremely low probability (~1 in 108) compared to its counterpart Rayleigh scattering where scattered photon has the same energy as the incident photon (Conroy, Ryder, Leger, Hennessey, & Madden, 2005). The notable rise in the application of Raman spectroscopy is mainly due to the technological advances, such as high-energy laser sources, sensitive detectors, high-throughput optics and efficient Rayleigh filters (Chase, 1994). In addition, the developed microscope enables the optical magnification and visualization of samples, facilitating the further application of Raman spectroscopic technique in microbiological studies (Y. Zhang, Hong, & Cai, 2010). Similar to other optical spectroscopic instruments, a typical Raman spectroscopic system consists of three major parts: a laser source, a light delivery system and a detector (Pence & Mahadevan-Jansen, 2016). A schematic illustration is shown in Figure 1.1. There are several types of laser sources available for the Raman systems, among which diode laser is commonly used in biological analysis attributed to their high energy efficiency (Angel, Carrabba, & Cooney, 1995). The wavelength of the laser is critical for the performance of 17 Raman spectroscopic detection. Different wavelengths of light have been applied, ranging from ultraviolet (UV) (>200 nm) through visible and to near-infrared (NIR) (<1100 nm) (Butler et al., 2016). NIR laser sources with the wavelength of 785 nm and 633 nm have been commonly used in Raman spectroscopy. These wavelengths provide relatively high scattering intensity, low fluorescence interference and sufficient detector efficiency (Draux et al., 2009; Swain & Stevens, 2007). The use of visible lasers in the blue and green (in particular at 532 nm) is increasing, as it demonstrates improved signal-to-noise ratio (SNR) (Byrne et al., 2015; Swain & Stevens, 2007). For the detection of very fluorescent materials, a 1064-nm laser is usually applied to avoid fluorescence interference (Asher & Johnson, 1984). However, high-energy illumination may be destructive to samples due to the effect of photobleaching. It is suggested to use shorter exposure time wherever possible to reduce the photobleaching of biological samples (Butler et al., 2016). Figure 1.1 A schematic illustration of a typical Raman spectroscopic system. Image is referenced from the work conducted by Butler and colleagues (Butler et al., 2016). To measure the weak signal of Raman scattering, a highly sensitive detector is required in the Raman system. Charge-coupled device (CCD) has been commonly included in the Raman 18 spectroscopic system, which is a multichannel array consisting of thousands of pixels (Cooper et al., 1994). Pixels can collect charges from the scattered photons, which is proportional to the Raman scattering intensity. These charges were then measured by pixels and finally read out by computers to generate a Raman spectrum (Matousek & Morris, 2010). Various types of CCDs are commercially available for different studies. A high quantum efficiency of 90% in the NIR region and a high SNR can be achieved by CCD detector, providing a sensitive test of Raman signal (Pence & Mahadevan-Jansen, 2016). There are a range of other instrument choices that contribute to the wide application of Raman spectroscopic techniques, such as filters, spectrographs and microscopes. Detailed information can be referred in the previous studies (Butler et al., 2016; Pence & Mahadevan-Jansen, 2016). Advantages of Raman spectroscopy Raman spectroscopy is a molecular-specific technique, allowing the sensitive investigation of functional groups, bonding types, and molecular conformations. In the vibrational spectra, the spectral bands are usually narrow, easy to resolve and provide detailed biochemical and structural information of samples without labeling (Lu, Al-Qadiri, et al., 2011). Reproducible spectra can be collected in several minutes or seconds, making Raman spectroscopy an extremely rapid detection technique. In addition, the operation of Raman spectroscopic analysis is straightforward and easy-to-conduct, which does not require complicated sample preparation and highly trained personals. In addition, the optical nature of Raman spectroscopic technique enables the detection in a non-invasive manner (Lorenz, Wichmann, 19 Stöckel, Rösch, & Popp, 2017). Based on these advantages, applications of Raman spectroscopy in microbiological studies have been rapidly increasing. Application in bacterial detection Rapid detection and identification of bacteria in various sample matrices is of critical importance. As aforementioned, Raman spectroscopy is a good alternative to rapid detection and characterization of bacteria compared to the conventional biochemical testes and molecular techniques. The “whole microorganism fingerprinting” provided by the Raman spectral patterns have shown good reproducibility and high discriminatory power for bacterial samples (Stöckel, Kirchhoff, Neugebauer, Rösch, & Popp, 2016). They can differentiate bacteria at species or even strains level based upon the variations in the spectral features corresponding to the specific biochemical compositions. Numerous studies have used Raman spectroscopy for bacterial detection and identification over the past decades. Raman spectroscopy coupled with multivariate statistical analysis have been used to study a number of pathogens to assist in clinical diagnostics, such as Listeria monocytogenes (Green, Chan, Luo, Dan, & Lin, 2009; J. Wang et al., 2015), Legionella (Kusić, Kampe, Rösch, & Popp, 2014), Campylobacter (Lu et al., 2012), Salmonella (Assaf, Cordella, & Thouand, 2014), Escherichia coli O157:H7 (Efrima & Zeiri, 2009), Pseudomonas aeruginosa (W. E. Huang, Bailey, Thompson, Whiteley, & Spiers, 2007), Staphylococcus aureus (Nicolaou, Xu, & Goodacre, 2011), Brucella (Meisel et al., 2012), and Acinetobacter (Maquelin, Dijkshoorn, van der Reijden, & Puppels, 2006). Aside from the common pathogens, some desired bacteria such as Lactobacilli used in fermentation 20 foods have also been characterized using Raman spectroscopy (Gaus et al., 2006). To further explore the performance of Raman spectroscopy, several representative studies have been cited and discussed below. Raman spectroscopy usually yields high discrimination ability for the classification of different bacteria. Kusic and coworkers evaluated the efficacy of Raman spectroscopy for the differentiation of 22 Legionella species and other common waterborne pathogens, such as E. coli, Klebsiella pneumoniae, and P. aeruginosa (Kusić et al., 2015). By means of a multiclass support vector machine (SVM) model, the tested bacteria were discriminated at genera, species and then subspecies level with a high accuracy. In another study, a total of 200 strains from 11 different Campylobacter species were identified to species level using a supervised dendrogram model (Lu et al., 2012). Intra- and interlaboratory test with different instrumentations demonstrated a high reproducibility of this spectroscopic approach. The detection procedures could be completed within 2 h when a validated classification model has been constructed. The effects of different cultivation conditions on the distinction capacity of Raman spectroscopic technique have been evaluated. Eight Staphylococcus strains cultivated under different conditions of nutrient, incubation temperature and culture age, were analyzed by micro-Raman spectroscopy (Harz et al., 2005). Although different cultivation conditions showed slight influence on Raman spectral features, tested bacteria were correctly identified on the strain level with an average recognition rate of 94.1%. These results indicated that 21 Raman spectroscopy is a robust and reliable approach for the identification of bacteria under different culturing conditions with stable discrimination ability. Further studies investigated the influence of food matrix on the performance of Raman spectroscopy. For instance, six closely related Listeria species were successfully classified from both pure media and milk samples using a Raman system with a 785-nm laser (J. Wang et al., 2015). Identification rate of 98.33% for L. monocytogenes in media and 96.11% in milk were achieved, respectively. The whole detection process could be finished within a few hours and only required simple sample preparation. Sensitivity is a critical parameter for the detection of bacteria. Raman spectroscopy could achieve single-cell determination, which allows the detection of trace amount of bacteria without cultivation steps. In one study conducted by Meisel and coauthors (Meisel et al., 2012), 20 different species or subspecies of Brucella, Escherichia, Pseudomonas and Yersinia were selected and measured from agar plates or directly from spiked milk samples. By analyzing the single-cell Raman spectra, accuracies ranging from 94–100% were obtained for all investigated species. In another study, a direct Raman spectroscopic identification of urinary tract infection (UTI) related pathogens using a single-cell typing method was reported for the first time (Kloß et al., 2013). A total of 11 UTI species of E. coli and E. faecalis were included to establish a reference database, in which single-cell spectra from different species were successfully classified with an accuracy of 92.1%. Based on the reliable database, UTI species in 10 real urine samples from patients were all correctly detected without any culture steps. 22 Since Raman spectroscopy provides unbiased biochemical information of bacteria cells, it shows flexibility for the study of the targeted bacteria in various states. Raman spectroscopy exhibits a great potential for the detection of viable but non-culturable bacteria (Krause, Rösch, Radt, & Popp, 2008), which can not be detected using conventional culture-based method. In addition, Raman spectroscopy has also been employed for the study of pathogenic bacterial spores (Kong, Setlow, & Li, 2013; Stoeckel et al., 2009) and characterization of microbial biofilms (Ivleva, Wagner, Horn, Niessner, & Haisch, 2008; Samek et al., 2015). Data analysis of Raman spectra Since Raman spectral features are attributed to the complicated biomolecules of whole bacterial cells, advanced data analysis tools are required to interpret the biochemical information from the high-dimensional datasets. Raman spectroscopic data analysis usually consists of two steps: 1) data pre-processing and 2) classification using chemometric models. Pre-processing Raman spectral quality might be affected by a variety of noise effects, such as background fluorescence from biological samples, thermal fluctuation of detectors and cosmic noise (Lasch, 2012). Spectral data pre-processing can be applied to the raw spectra to reduce these undesirable variations due to noise effects, which is significantly important for the construction of the subsequent chemometric models. It has been reported that quantitative and classification models established based upon pre-processed data generally show a better 23 performance than those using the raw spectral data (Heraud, Wood, Beardall, & McNaughton, 2006; Kohler, Kristian Afseth, & Martens, 2006). The first step is to visually assess the raw data set, in which obvious outliers with cosmic rays, substantial spectral contamination or very low SNR can be removed (Naumann, 2008). Baseline correction is a crucial step to minimize the background and enhance spectral quality. Polynomial fitting is one of the most commonly used procedures for baseline subtraction, which can efficiently remove unknown background noise while keep the important Raman spectral bands (Lieber & Mahadevan-Jansen, 2003; Mazet, Carteret, Brie, Idier, & Humbert, 2005). In addition, first- or second-order derivation of raw spectral data is another mathematical approach to abolish the baseline slopes and resolve overlapped peaks (Savitzky & Golay, 1964). Cautions should be taken when baseline subtraction is applied as these processes might also degrade true Raman spectral features (Byrne, Knief, Keating, & Bonnier, 2016). Raman spectra are also influenced by sample and experimental variations, such as sample thickness and cell density, resulting in varied overall signal intensities not related to biochemical changes of the tested samples (Byrne et al., 2016). Therefore, Raman spectra can be subjected to normalization to minimize the spot to spot variation due to physical inhomogeneity. Vector normalization and min-max normalization have been widely employed in Raman spectroscopic studies (Lasch, 2012). Normalization based on a typical Raman peak (e.g., Amide I peak) is another popular method, in which the value of this reference peak is scaled to 1 (Baker et al., 2014). Unlike baseline correction, normalization 24 does not substantially change spectral features. It is recommended that normalization should be applied after baseline removal. Chemometric models Advanced multivariate analytical techniques are required to interpret the high dimensional Raman data, so that meaningful information can be extracted and subtle differences in the spectral fingerprints can be distinguished. Multivariate statistical analysis can reduce the multi-dimensional spectral data down to a few key variables that account for most spectral variance (Lu, Al-Qadiri, et al., 2011). This dimension-reduction or feature-extraction leads to a decrease of computational burden while maintains the important features of the original dataset, providing representative information for the following classification or quantification. Classification of samples can be achieved by using various chemometric models based upon the minor differences of the spectral features. Chemometrics are generally categorized to unsupervised model and supervised methods (Byrne et al., 2016). Unsupervised classification aims to evaluate whether clustering exists in a dataset based on the spectral variances alone. No prior class membership information of samples is required in the calculations (Diem, Romeo, Boydston-White, Miljković, & Matthäus, 2004). Principal component analysis (PCA), hierarchical cluster analysis (HCA) and k-means are the commonly used unsupervised clustering methods, which are able to separate different biological samples by means of cluster plots or dendrograms (Byrne et al., 2016). 25 To achieve better discrimination of the spectra, more sophisticated supervised models are usually required. These supervised methods aim to construct models using analytical measurements to predict the class membership of future unknow samples (Ellis & Goodacre, 2006). In contrast to unsupervised methods, supervised classification models require the prior class labels of samples to generate the desired outputs. Specifically, a set of training data with known class assignments are firstly used to establish a calibration model, followed by cross-validation using a separate “testing dataset” to test the reliability of the model. In the prediction step, the models are used to predict the interested value of unknown sample based on the measurement (Goodacre, 2003). Common supervised calibration model such as discriminant function analysis , soft independent modeling of class analog, support vector machines and partial least squares regression (PLSR) are frequently used in vibrational spectroscopy-based bacterial detection (Ellis & Goodacre, 2006). Conventional chemometric techniques are often limited by strict assumptions such as normality, linearity and variable independence. However, there are problems where the relationships are quite dynamic and difficult to explain using the conventional supervised models. In our case, it is the relationship between high-dimensional Raman spectra and the information of bacterial samples. Recently, the increasing application of novel machine learning techniques provides a robust analytical method to capture various relationships and allows the users to quickly and easily model the complicated phenomena (Kalogirou, 2000). Artificial neural network (ANN) is based on artificial intelligence techniques and basically used to relate inputs to the corresponding outputs without knowing specific relationship between them (G. Li & Shi, 2010). ANN consists of a number of artificial neural cells that 26 are interconnected by the connection weight similar to the synapse of the human nervous system (Yin, Mao, & Hua, 2011). This model can learn the hidden patterns or relationships between the input and output from previously recorded data (training process) and use them to forecast future values (prediction process) (More & Deo, 2003). They perform as “black box” models and able to handle complex problems without pre-assuming the specific mathematical equations. In addition, neural networks are fault-tolerant, robust and can generate accurate predictions at high speed with a well-trained model (Rumelhart, Hinton, & Williams, 1985). ANNs have been employed in a variety of applications, such as pattern recognition, weather or market forecasting, control systems and optimization and decision making (W. Liu et al., 2017). Recently, the use of ANN in vibrational spectroscopic interpretation is also increasing, as the discriminatory ability of spectroscopy for microbial detection can be significantly improved by combining it with ANN (Bosch et al., 2008; Grunert et al., 2013). Several unsupervised and supervised chemometric models used in this thesis project are briefly introduced in the following sections. 1.4.2.1 Principle component analysis PCA is a powerful feature-extraction technique for spectral analysis. This method can effectively reduce the multidimensional data into a few orthogonal variables named principal components (PCs) that account for the majority of spectral variances (Jolliffe, 2011). Each PC is constructed from linear combinations of the original variables. A new row space can be established in which observations are plotted using the first two or three PCs rather than the 27 original measured variables to visually exhibit the variability of the original dataset. These new defined 2- or 3-dimonsional scatter plots are used for the classification and discrimination of samples based upon the differences of their Raman spectral features (Abdi & Williams, 2010). Loading plots calculate the weights of spectral features in an individual PC, in which both positive and negative values explain the contribution of a specific spectral feature to the classification models (Abdi & Williams, 2010). 1.4.2.2 Hierarchical cluster analysis HCA is an unsupervised model that describes the interpoint distances between all of the samples in a dendrogram regardless of the dimensionality of the original dataset (Byrne et al., 2016). Briefly, this method treats each Raman spectrum as a separate cluster and groups the closet clusters together in an iterative way until there is only one cluster remining. Then, the results are present in the form of a 2-dimentioanal dendrogram (or tree diagram) showing the linkage between clusters, where all the data points are organized according to their similarities. Different approaches, such as single vs. centroid linking, Euclidean vs. Mahalanobis distance, have been utilized to measure the distance between clusters (Kassambara, 2017). 1.4.2.3 Back-propagation neural network Back-propagation neural network (BPNN) is one of the most popular neural networks (J. Li, Cheng, Shi, & Huang, 2012). A schematic diagram of a typical multilayer BPNN architecture is illustrated in Figure 1.2. A representative collection of input-output examples is used as a training set. In the supervised learning algorithm, each cycle or epoch (i.e. the 28 whole training process of a new training data) consists a forward activation flow of outputs and the backwards error propagation of weight adjustments (Mouazen, Kuang, De Baerdemaeker, & Ramon, 2010). More specifically, input patterns are presented to the network via the input layer, which link to one or more hidden layers made up of the interconnected neurons. Each neuron contains the weighted sum of its inputs via a neuron activation function. The output signals of the hidden layers are calculated and transferred to the output layer to generate a possible output. This output value is compared with the actual one and then appropriate adjustment to the connection weights are made to minimize the error. This learning process is repeated to achieve the lowest possible error. BPNN has been used to develop classification and calibration models for the analysis of vibrational spectra (He, Li, & Deng, 2007; Mouazen et al., 2010), and usually a higher accuracy could be achieved compared to other chemometric models. Figure 1.2 Schematic illustration of a back-propagation neural network model containing input, hidden (with “n” neurons) and output layers. Figure is adapted from a previous work conducted by Z.-Y. Zhang and Wang (Z.-Y. Zhang & Wang, 2014) 29 1.4.2.4 Convolutional neural network Convolutional neural network (CNN) is another powerful neural network architecture. It has been widely applied to image processing, speech recognition, and natural language processing (Krizhevsky, Sutskever, & Hinton, 2012; LeCun, Bengio, & Hinton, 2015). Recent studies showed that CNN could be used for the classification of vibrational spectroscopic data with a high accuracy (Acquarelli et al., 2017; J. Liu et al., 2017). Typically, the architecture of a CNN model consists an input layer, a series of convolutional layers and pooling layers, followed by fully connected layers and an output layer (J. Liu et al., 2017). A typical schematic diagram of CNN is shown in Figure 1.3. Convolutional layer with kernels is the first layer to perform feature extraction from an input data with different levels of abstraction. The role of the pooling layer is to extract the most important structures and reduce the dimension of the representation with minor distortions. The fully connected layers and the output layer utilizing Softmax activation functions serve as a classifier to assign an object into a specific class based upon the features selected by the convolutional and pooling layers (J. Liu et al., 2017). The major advantage of CNN compared to other chemometrics is that it can combine the automatic feature extraction with classification (J. Liu et al., 2017). A well-trained CNN classification model can take an input spectrum, process it and classify it under certain categories with a high accuracy. CNN is also computationally efficient, making them universally attractive for the analysis of high-dimensional data. 30 Figure 1.3 Schematic illustration of a convolutional neural network containing two convolutional hidden layers. Image is adapted from a previous work conducted by Albelwi and Mahmood (Albelwi & Mahmood, 2017). Hypothesis and objectives Hypothesis: Raman spectroscopy has the ability to detect and speciate Arcobacter bacteria. Objective: To detect and discriminate different Arcobacter species using Raman spectroscopy coupled with various chemometric models. 31 Chapter 2: Materials and methods Bacterial strains and growth conditions A total of 82 Arcobacter strains representing 18 Arcobacter species were investigated in this study, including A. butzleri, A. cryaerophilus, A. skirrowii, A. trophiarum, A. molluscorum, A. venerupis, A. bivalviorum, A. thereius, A. cibarius, A. nitrofigilis, A. ellisii, A. mytili, A. defluvii, A. faecis, A. lanthieri, A. aquimarinus, A. halophilus, and A. marius. The detailed information of bacterial strains is summarized in Table 2.1. The Arcobacter strains were isolated and provided by Ottawa Research and Development Centre at Agriculture and Agri-Food Canada (Ottawa, ON, Canada) and Arcobacter Research Lab at Freie Universität Berlin (Berlin, Germany). C. jejuni ATCC 33560, C. coli 314 and H. pylori ATCC 43504 were obtained from Washington State University (Pullman, WA, US). The bacteria with strain number of ATCC, LMG, CCUG, DSM were reference strains originally purchased from the American Type Culture Collection (Manassas, VA, US), the Laboratorium voor Microbiologie (Ghent, Belgium), the Culture Collection University of Gothenburg (Gothenburg, Sweden), and the German Collection of Microorganisms and Cell Cultures (Braunschweig, Germany), respectively. All the other strains were isolated from different environmental, clinical, animal and agri-food sources in Canada, Germany and United States by these aforementioned laboratories. All of the strains were identified to the species level by using PCR (data not shown). Bacterial strains were routinely cultured on Mueller-Hinton (MH) agar (Sigma-Aldrich, St. Louis, MO, US) supplemented with 5% (v/v) sheep blood (Alere, Waltham, MA, US) except 32 for A. halophilus and A. marius, which were grown on Marine agar (Becton Dickinson, Franklin Lakes, NJ, US). Strains of A. skirrowii, A. halophilus, Campylobacter and Helicobacter were incubated at 37°C and all the other strains were cultivated at 30°C under microaerobic condition (10% CO2) for 24-72 h. All of the strains were stored at -80°C in glycerol stocks containing 40% (v/v) glycerol and 20% (v/v) sheep blood. Table 2.1 Summary of bacterial strains used in this study. Bacteria species Strain Source Location A. butzleri AF 1428 Chicken and pig tank Canada A. butzleri AW 0986 Little Castor River Canada A. butzleri CCUG 30485 Human Germany A. butzleri 01115 Human Germany A. butzleri 00952 Poultry Germany A. butzleri 01976 Fish Germany A. butzleri 00951 Poultry Germany A. butzleri 01941 Poultry Germany A. butzleri 01968 Poultry Germany A. butzleri 01975 Fish Germany A. butzleri 00947 Minced pork Germany A. butzleri 00948 Water Germany A. butzleri 00955 Poultry Germany A. butzleri 00956 Poultry Germany A. butzleri 01673 Pig Germany A. butzleri 00986 Bovine Germany A. butzleri 2619 Beef Germany A. butzleri 2540 Mussel Germany A. butzleri 2561 Squid Germany A. butzleri 2653 Human Germany A. butzleri 2654 Human Germany A. butzleri 2527 Human Germany A. cryaerophilus AW 4120 South Nation River Canada A. cryaerophilus DSM 7289 Bovine Germany A. cryaerophilus 00814 Water Germany A. cryaerophilus 00972 Poultry Germany A. cryaerophilus 00973 Poultry Germany A. cryaerophilus Water Germany A. cryaerophilus 00981 Miced meat Germany A. cryaerophilus 00983 Bovine Germany 33 Bacteria species Strain Source Location A. cryaerophilus 00985 Bovine Germany A. cryaerophilus 00986 Bovine Germany A. cryaerophilus 01317 Human Germany A. cryaerophilus 02479 Beef Germany A. cryaerophilus 02480 Pork Germany A. cryaerophilus 2504 Shrimp Germany A. cryaerophilus 2517 Mussel Germany A. cryaerophilus 2537 Octopus Germany A. cryaerophilus 2650 Human Germany A. cryaerophilus 2651 Human Germany A. cryaerophilus 1899 Cow tank (Ontario) Canada A. cryaerophilus 1917 Cow tank (Quebec) Canada A. cryaerophilus 1949 Pig tank (Alberta) Canada A. cryaerophilus 2652 Human Germany A. skirrowii CCUG 10374 Sheep Germany A. skirrowii AF 1896 Cow tank (Ontario) Ottawa A. skirrowii AF 1901 Cow tank (Ontario) Ottawa A. skirrowii 0639 Water Germany A. cibarius LMG 21996 (T) Broiler carcasses Ottawa A. thereius LMG 24486 (T) Organs of aborted pigs Ottawa A. lanthieri AW 0110 MST-SNC-9-184 Ottawa A. lanthieri AF 0643 WWTP (DIS-4) Ottawa A. lanthieri AF 0644 WWTP (DIS-4) Ottawa A. lanthieri AF 1680 Female pig pre-tank Ottawa A. lanthieri AF 1682 Female pig pre-tank Ottawa A. lanthieri AF 1700 Female pig pre-tank Ottawa A. aquimarinus 2516 Mussel Germany A. aquimarinus 2522 Mussel Germany A. aquimarinus 2523 Shrimp Germany A. aquimarinus 2524 Clam Germany A. aquimarinus 2526 Clam Germany A. aquimarinus 2528 Mussel Germany A. faecis AF 1033 Human fecal sample septic Canada A. faecis AF 1044 Human fecal sample septic Canada A. faecis AF 1064 Human fecal sample septic Canada A. faecis AW 1119 MST-SNC-9-139(4) Canada A. faecis AW 1161 MST-SNC-9-152(8) Canada A. faecis AW 1164 MST-SNC-9-152(8) Canada A. venerupis LMG 26156 (T) Shellfish Canada A. venerupis 2490 Shrimp Germany A. venerupis 2493 Mussel Germany A. venerupis 2545 Octopus Germany A. venerupis 2547 Squid Germany A. venerupis 2548 Squid Germany 34 Bacteria species Strain Source Location A. venerupis 2555 Octopus Germany A. nitrofigilis ATCC 33309 Roots Canada A. mytili 24559 (T) Musses Canada A. trophiarum LMG 25534 (T) Feces of fattening pigs Canada A. halophilus BAA-1022 Hypersaline Lagoon Canada A. marius 25770 (T) Mix seawater, starfish and seaweed Canada A. bivalviorum LMG 26154 (T) Shellfish Canada A. molluscorum LMG 25693 (T) Mussels and oysters Canada A. defluvii 25694 (T) Sewage Canada A. ellisii 26155 (T) Musses Canada C. jejuni F38011 Human US C. coli 314 Pig US H. pylori ATCC 43504 Human Australia Sample preparation Bacterial cells were collected from agar plates using a 10-μL sterile inoculation loop and then suspended in 1 mL of sterile double-deionized water (ddH2O). After centrifugation at 15,000 ×g for 2 min, the supernatant was discarded and the cell pellet was washed another two times with ddH2O to remove the residue of the growth media. Finally, the resulting bacterial cell pellet was resuspended in 5 μL of sterile distilled water. An aliquot of 1 μL of this suspension was transferred on to a gold-coated microscope slide (Sigma-Aldrich, St. Louis, MO, US) and dried at 22°C for 20 min before Raman spectral collection. Raman spectroscopic instrumentation A Renishaw inVia confocal Raman spectroscopic system (Renishaw, Gloucestershire, UK) equipped with a 785-nm NIR diode laser and a Leica microscope was used in this study. The spectrometer has an entrance slit of 50 µm, a focal length of 300 mm and is equipped with a 1200-lines/mm grating. Rayleigh scattering was eliminated by filters and Raman scattered 35 signals was collected by using a CCD array detector (578- by 385-pixel), with a pixel size of 22 µm. The gold-coated microarray slide covered with bacterial samples were mounted onto the microscope stage. A 50× Nikon magnification objective (NA = 0.75, WD= 0.37) focused the laser light on the bacterial sample with a laser power of approximately 25 mW. All Raman spectroscopic measurements were performed in the wavenumber range of 3,200 to 200 cm-1 with a spectral resolution of 1 cm-1. The exposure time for each bacterial sample was 10 s, which was sufficient to obtain spectra with a good SNR without causing visible sample damage. The WiRE 3.0 software (Renishaw, Gloucestershire, UK) was employed to control the Raman system for spectral collection. For the spatially resolved measurement, an x/y/z motorized stage was used. Three independent batches of each bacterial species were determined to take into account the biological variability and possible daily variations during the spectral collection. Identification of Arcobacter at genus level To evaluate the detection capability of Raman spectroscopy, we firstly employed it to differentiate genus Arcobacter from other closely related bacteria. Campylobacter and Helicobacter are under the same family Campylobacteraceae as Arcobacter. These 3 genera showed considerable amount of physiological similarities making the discrimination more complicated. Representative bacterial strains belonging to Arcobacter [A. butzleri CCUG 30485, A. skirrowii CCUG 10374 and A. cryaerophilus DSM 7289], Campylobacter (C. jejuni ATCC 33560 and C. coli 314) and Helicobacter (H. pylori ATCC 43504) were 36 identified using Raman spectroscopy coupled with various classification chemometric models. A. butzleri, A. skirrowii and A. cryaerophilus are three most prevalent Arcobacter species that have been associated with human infections (Banting & Figueras, 2017). Within the genus Campylobacter, C. jejuni and C. coli are most frequently isolated from food products and diarrheal patients, accounting for 90% to 95% of all campylobacteriosis cases (Pitkänen & Hänninen, 2017). The typical species of genus Helicobacter is H. pylori, which infects up to 50% of human populations (Peleteiro, Bastos, Ferro, & Lunet, 2014). Therefore, these species were selected as the representative species of each genus. Sample preparation and Raman spectral collection were conducted as aforementioned. Influence of cultivation condition on Raman spectral reproducibility In order to know whether cell cultivation time and temperature are critical factors for Raman spectral reproducibility, Arcobacter species grown under different cultivation conditions were investigated. Specifically, A. butzleri CCUG 30485, A. trophiarum LMG 25534 (T) and A. mytili LMG 24559 (T) were selected as the representative strains. Bacterial biomass of each strain was collected at different incubation times of 36, 48, 60 and 72 h for Raman spectroscopic analysis. In addition, these strains were cultivated at two different temperatures of 37°C and 30°C and subsequently tested by Raman spectroscopy. The experiments were repeated at least in three times. Identification of a specific Arcobacter species in a bacterial mixture In the real world, multiple bacteria can co-exist in a given environment or agri-food sample. Thus, it is crucial that a technique can identify a specific bacterium within a bacterial 37 mixture. To simulate the natural conditions and determine the spectral selectivity, Raman spectroscopy was applied to detect a specific Arcobacter specie in a mixture of other Arcobacter species. A. butzleri CCUG 30485 was selected as the targeted bacterium as this specie has been considered as a serious microbial hazard to human health. Three randomly selected Arcobacter species A. skirrowii AF 1896, A. trophiarum LMG 25534 (T) and A. mytili LMG 24559 (T) were mixed together with equal biomass to form a bacterial cocktail as the background. A. butzleri CCUG 30485 was then mixed with this prepared cocktail of three strains at different ratios of 5%, 25%, 50 %, 75% and 100% by biomass to form a series of bacterial mixtures. Raman spectra of these bacterial mixtures were collected and analyzed to determine if different ratios of A. butzleri CCUG 30485 in the mixed bacterial population could be identified according to the corresponding Raman spectral features. Detection of Arcobacter in real food sample To further investigate the influence of food matrix on the robustness of Raman spectroscopic method, we tested the performance of Raman spectroscopy for the detection of Arcobacter in real food samples. Milk was selected as a representative food sample as Arcobacter has been frequently identified from raw milk (Hsu & Lee, 2015; Scullion et al., 2006; Yesilmen et al., 2014). Ultra-high temperature (UHT)-treated milk [fat content 2% (w/v)] was purchased from a local grocery store in Vancouver, Canada. A. butzleri CCUG 30485 was used as a representative strain based on its clinical importance. Overnight bacterial culture was 10-fold serially diluted and inoculated into UHT-treated milk reaching to a final concentration of 105, 104, 103, 102, 101 and 100 CFU/mL, respectively. The initial concentration of Arcobacter in the spiked milk samples was confirmed by using the conventional plating assay. Enrichment 38 step was performed to ensure enough bacterial biomass in milk to generate Raman spectra. Specifically, the spiked milk sample was mixed with MH broth at the volume ratio of 1:9 and incubated at 30°C with an orbital shaking at 175 rpm. After enrichment for 0, 8, 16, 24 and 28 h respectively, 10 mL of each spiked milk samples were collected for Raman spectroscopic analysis. Bacterial cells in each milk sample were harvested by centrifugation at 15,000 ×g for 2 min at 22°C. The supernatant was discarded and the cell pellet was washed 4 times using phosphate buffer saline (PBS) containing 0.5% (w/v) tween 20 as previously described (J. Wang et al., 2015). During the washing steps, most of the residues of milk components in the pellet could be removed. Finally, the purified bacterial pellet was resuspended in 5 µL of sterile ddH2O and prepared for Raman spectral collection as aforementioned. The experiment was performed in three independent replicates. Raman spectral analyses Raman spectral analyses consist of two major steps: pre-processing and classification using various chemometric models. Spectral analyses were performed using Matlab 2018b software (Mathworks, Matick, MA, USA). Pre-processing of Raman spectra Firstly, baseline correction was performed for all of the collected raw Raman spectra to reduce different types of background effects, including fluorescence of the microbial samples, thermal fluctuations on the CCD detector and cosmic noise (Lasch, 2012). Briefly, a polynomial fitting was employed to generate an individual background for each Raman spectrum, which was then subtracted from the original Raman spectrum. Subsequently, 39 baseline-corrected Raman spectra were normalized based upon the intensity of the phenylalanine peak at 1,002 cm-1 according to previous studies (Moor et al., 2014; Oshima, Shinzawa, Takenaka, Furihata, & Sato, 2010). Normalization can minimize the effect of the different focal volumes of bacterial samples on Raman spectra (Byrne et al., 2016), which is necessary for the following chemometric analyses. Classification chemometric models After pre-processing, Raman spectra with wavenumber region from 200 to 3200 cm-1 were used for the construction of chemometric models. In this study, different types of classification and calibration models were established for the detection and speciation of Arcobacter. PCA was employed to identify Arcobacter at genus level. PCA is an unsupervised method that can effectively reduce the multidimensional data into a few PCs while maintaining the majority of spectral variances (Jolliffe, 2011). Raman spectra of representative strains belonging to 3 different genera were processed by PCA and the score plots were created by using the first three PCs. The differences and similarities between Raman spectra from different bacteria were visually shown in a 3-dimonsional (3D) plot. A loading plot of the first PC was constructed to describe the weights of each wavenumber, indicating the contribution of the corresponding chemicals to the differentiation of Arcobacter and closely related Campylobacter and Helicobacter. Mahalanobis distance (MD) between different clusters was calculated to further validate the separation of different genera. This distance is defined as MD=√(𝑥1 − 𝑥2)𝑇𝑆−1(𝑥1 − 𝑥2) , where 𝑥1 and 𝑥2 are the mean vectors of two 40 clusters and 𝑆 represents the within-cluster covariance (De Maesschalck, Jouan-Rimbaud, & Massart, 2000). Therefore, MD represents the distance between the centers of two clusters in number of within-group standard deviations. Two clusters could be considered to be significantly different from each other when their MD is larger than 3 (Yi, Sun, Zhu, Liu, & Lu, 2017). Another two PCA-based models were used to evaluate the influence of cultivation time and temperature on Raman spectral reproducibility. Raman spectra collected from 3 Arcobacter strains with different culture ages were analyzed by PCA and scores plot of the first 3 PCs were drawn to exhibit the relationship of these spectral profiles. Similarly, the collected spectra of bacterial cells cultivated at different temperatures were also subjected to PCA and another 3D model was established. The corresponding MDs between clusters in two PCA-based models were calculated, respectively. For the speciation of Arcobacter, CNN was built to identify 18 different species of Arcobacter based upon their spectral features. CNN is one of the most popular deep learning techniques that performs automatic feature extraction in combination with classification (Krizhevsky et al., 2012; LeCun et al., 2015). Recent studies indicated that CNN used for the classification of Raman spectroscopic data could simplify model development while achieve a higher accuracy compared with other commonly used machine learning techniques, such as SVM (Acquarelli et al., 2017; J. Liu et al., 2017). In the current study, CNN was composed of 3 convolutional layers with 32 filters of size [30, 1], 64 filters of size [20, 1] and 64 filters of size [10, 1], respectively, as well as 18 fully connected layers. The initial 41 learning rate factor for weights was set as 0.01 and the maximum number of epochs was 50. The reliability of the CNN for Raman spectral classification of different Arcobacter species was evaluated by “leave-one-out” cross validation (Wong, 2015). Briefly, the model left out one observation in each group as the validation set and employed the remaining observations as the training set at a time. “Leave-one-out” cross validation provides unbiased estimation for the actual accuracy of a classification algorithm (Wong, 2015). A calibration model BPNN was established to determine the ratio of a specific Arcobacter strain within a bacterial mixture. BPNN is another powerful neural network architecture in machine learning (J. Li et al., 2012), which can correlate input vectors to the corresponding output vectors without knowing the specific relationship between them. BPNN has been used to develop vibrational spectroscopic-based calibration models for the prediction of sample properties with a high accuracy (He et al., 2007; Mouazen et al., 2010). BP training is a gradient-descent algorithm, consisting of a forward activation flow of outputs and the backwards error propagation of weight adjustments to reduce the total error to an acceptable range adjustments (Mouazen et al., 2010). The BPNN used in the current study was composed of three layers: input layer, hidden layer and output layer that contains 9 neurons, 3 neurons and 1 neuron, respectively. The relationships between input (i.e., Raman spectral features of bacterial mixture) and output (i.e., the ratio of A. butzleri in the mixture) can be learned from the recorded data (training dataset) and used to predict the future values. The error level was set as 1%, indicating that the difference between the predicted value and the true value should be less than 1% after the model was well-trained. The performance of the 42 BPNN model was also evaluated by leave-one-out cross-validation (Wong, 2015) and coefficient of determination (R2) and root mean square error (RMSE)were calculated. Finally, the detection of Arcobacter in the spiked milk at different incubation times were analyzed using a 2D PCA. Raman spectra of A. butzleri in pure culture were also included in this model to exhibit the influence of food matrix on the spectral patterns. 43 Chapter 3: Results Identification of genus Arcobacter from other closely related bacteria Generally, the cell chemical compositional variances among different genera are more obvious than those among different species within the same genus. Therefore, we firstly determined if Raman spectroscopy has the ability to discriminate Arcobacter from other bacteria at genus level. Arcobacter, Campylobacter and Helicobacter are 3 closely related genera with high structural and morphological similarity (On, 2001). Both Helicobacter and Arcobacter were considered as atypical Campylobacter before the new genus being proposed. Representative species were selected from each genus. Raman spectra of each strain were collected and used for the construction of the 3D PCA model. As shown in Figure 3.1, each genus forms a tight cluster in the 3D plot that can be visually distinguished from each other, indicating that the differentiation of genus Arcobacter from other bacteria can be simply achieved by using Raman spectroscopic-based PCA model. Additionally, the MDs between every two genera were calculated and are summarized in Table 3.1. Since clusters with interclass distance >3 could be considered as significantly separated from each other, the MD values ranging from 13.90 to 19.61 further confirmed the well discrimination of 3 different genera. The first 3 PCs represent a total of 92% of spectral variance of the entire spectral dataset with the first PC contributing to 62.67%, suggesting that most variance of the bacterial compositions is represented in the PCA plot. The loading plot of the first PC was constructed to determine the specific biochemical components that contributed significantly to the classification of Arcobacter and other closely related bacteria (Figure S.1). Raman spectral bands with a high loading value (>0.05) were located at 778, 1136 and 1540 cm-1, which are assigned to uracil-based ring breathing mode in nucleic acid (Kendall 44 et al., 2010), C-C skeletal stretching (Nawaz, Bonnier, Meade, Lyng, & Byrne, 2011) and amide carbonyl group vibrations and aromatic hydrogens in amide II (Katainen et al., 2007), respectively. Therefore, the classification of genus Arcobacter was mainly based upon the structural or compositional variations of nucleic acids and proteins in the bacterial cells. Figure 3.1 Principal component analysis plot of Raman spectra from representative bacterial strains of 3 different genera Arcobacter (blue), Campylobacter (red) and Helicobacter (yellow). A. butzleri CCUG 30485, A. skirrowii CCUG 10374, A. cryaerophilus DSM 7289, C. jejuni ATCC 33560, C. coli 314 and H. pylori ATCC 43504 were used as the representative strains of different genera. Table 3.1 Inter-group Mahalanobis distance between different genera in principal component analysis model (Figure 3.1). 45 Bacterial genus Mahalanobis distance Arcobacter -Campylobacter 13.90 Arcobacter-Helicobacter 19.61 Campylobacter-Helicobacter 19.17 The influence of cultivation conditions on Raman spectral reproducibility Various cultivation conditions (e.g., nutrient, culture time and incubation temperature) of Arcobacter in real-world situations might influence the growth of bacteria and consequently affect the Raman spectral reproducibility (Harz et al., 2005). The 18 different Arcobacter species used in this study require different optimal temperatures (i.e., 30 or 37°C) and varied cultivation times (i.e., 24 to 72h) to form bacterial colonies on the agar plates. Thus, the effect of different cultivation conditions on the spectral reproducibility and discrimination capability of Raman spectroscopy were investigated. We randomly selected A. butzleri CCUG 30485, A. mytili 24559 (T) and A. trophiarum LMG 25534 (T) as the representative species. Multiple Raman spectra were collected for each species grown with different cultivation temperatures and culturing times. A PCA model (Figure 3.2A) was established using the first three PCs to differentiate 3 Arcobacter species cultivated on MH agar for different time periods (36, 48 and 60 h for rapidly growing species A. butzleri and A. mytili; 48, 60 and 72 h for slowly growing species A. trophiarum). Individual tight cluster was formed for each species and the MDs measured between each two species were all larger than 3 (Table 3.2), indicating the significant distinctions between different species. Thus, cultivation time is not a critical factor that affects the reproducibility and discriminatory power of Raman spectra. Similarly, Raman spectra from 3 Arcobacter species cultured at different temperatures were well separated in 3 distinctive clusters on the basis of bacterial species (Figure 3.2B) with inter-group distances ranging from 6.49 to 27.42 (Table 3.2). For 46 each species, spectra collected both at 30°C and 37°C were clustered in a single group and the intra-group variation is not visually distinguished, indicating that the reproducibility of Raman spectra was not significantly influenced by incubation temperature. Based on the reproducible Raman spectra of Arcobacter collected under various cultivated conditions, robust and reliable spectroscopic-based chemometric models can be established for the detection and identification of Arcobacter species. 47 Figure 3.2 Influence of bacterial cultivation time (A) and incubation temperature (B) on Raman spectral reproducibility. (A) Principal component analysis (PCA) plot was constructed using Raman spectra of 3 Arcobacter species with different cultivation times 48 ranging from 36 h to 72 h. (B) PCA plot was constructed using Raman spectra of 3 Arcobacter species cultivated at two different temperatures of 30°C and 37°C. Tight clusters were formed for each species with different cultivation time or temperature, indicating the discriminatory capacity of Raman spectroscopic-based method is not significantly affected by either culture time or temperature. Table 3.2 Inter-group Mahalanobis distance between different Arcobacter species cultivated under different conditions in principal component analysis model (Figure 3.2). Arcobacter species Mahalanobis distance Under different cultivation times Under different cultivation temperatures A. butzleri- A. mytili 23.95 9.19 A. butzleri- A. trophiarum 22.84 27.42 A. mytili- A. trophiarum 18.26 6.49 Raman spectra from different Arcobacter species Raman spectra of 82 bacterial strains representing 18 different Arcobacter species (Table 2.1) were collected and analyzed. Average Raman spectra were calculated based upon all preprocessed spectra of each species within the database and are displayed in Figure 3.3. Each Arcobacter species shows a unique Raman spectral pattern. Raman spectral fingerprinting region (i.e., 400–1800 cm−1) and wavenumber region between 2,800 and 3,100 cm-1 represent the biochemical compositions of whole bacterial cells, such as proteins, lipids, polysaccharides and nucleic acids (Butler et al., 2016). Raman bands observed in the spectra of Arcobacter species and their tentative assignments are summarized in Table 3.3. Several bands were shown in all Arcobacter species. For examples, the broad band in the wavenumber around 2930 cm−1 is attributed to C-H stretching vibration in all essential 49 molecules containing CH bindings (Koljenović, Schut, Vincent, Kros, & Puppels, 2005). Thus, the band intensity of wavenumber region between 2800 to 3100 cm−1 usually indicates the total bacterial biomass of the tested biological sample. The bands observed at 1242 cm-1 and 1660 cm-1 represent the vibrations originating from amide III (Lakshmi et al., 2002) and amide I (Naumann, 1998), respectively. Another prominent band at 1445 cm-1 might be due to CH2 deformation modes of proteins and lipids (D. P. Lau et al., 2003). Raman signal at 1002 cm-1 is assigned to phenylalanine (Stone, Kendall, Shepherd, Crow, & Barr, 2002), which is commonly present in the biological samples. Some minor bands at 619, 641, 1030 cm-1 are also corresponding to the chemical bonds of proteins (Bhattacharjee, Kumar, Maru, Ingle, & Krishna, 2014; Chan et al., 2006). In addition, uracil ring stretching modes at 780 cm-1 (Kendall et al., 2010), guanine or phosphodiester vibrations at 722, 1095, 1333 and 1577 cm-1 are features correlated to nucleic acids (Binoy et al., 2004; Liu et al., 2008; Ruiz‐Chica, Medina, Sanchez‐Jimenez, & Ramirez, 2004). The bands located at 824 and 850 cm-1 are related to polysaccharides and phospholipids on the bacterial cell wall (Gniadecka, Wulf, Nymark Mortensen, Faurskov Nielsen, & Christensen, 1997; Ruiz‐Chica et al., 2004). The position and relative intensities of these bands were only slightly different between the spectra of all investigated Arcobacter species. In comparison, two additional dominant bands at 1133 and 1542 cm-1 were observed in the spectra of A. cryaerophilus, A. aquimarinus, A. mytili and A. trophiarum while not present in other species. These two bands could be assigned to fatty acids and amide II (N. Huang et al., 2011), respectively. Shaded areas in gray in Figure 3.3 indicate the visible spectral variations between different Arcobacter species, which could be used for the discrimination of Arcobacter bacteria at 50 species level. The significant differences observed between spectra were attributed to nucleic acids (e.g., 700-800 cm-1, 1,050-1,170 cm-1) and proteins (e.g., 1,500-1,600 cm-1) in the bacterial cells. Figure 3.3 Average Raman spectra of 18 different Arcobacter species recorded with an excitation wavelength of 785 nm. Spectral regions with distinctive visible variations between different species were highlighted in shadow grey. Major Raman bands were identified and their tentative band assignments are summarized in Table 3.3. 51 Table 3.3 Raman bands observed in the spectra of Arcobacter species and their tentative assignments. Wavenumber /cm-1 Assignment Reference 619 C-C twisting (protein) (Chan et al., 2006) 641 C-S stretching and C-C twisting of proteins; tyrosine (Chan et al., 2006) 722 Nucleic acids (Binoy et al., 2004) 780 Uracil-based ring breathing mode (Kendall et al., 2010) 824 Phosphodiester (Ruiz‐Chica et al., 2004) 850 Single-bond stretching vibrations for the amino acids and valine and polysaccharides (Gniadecka et al., 1997) 1002 Phenylalanine (Stone et al., 2002) 1030 C-C stretch of protein (Bhattacharjee et al., 2014) Phenylalanine of collagen (Cheng, Liu, Liu, & Lin, 2005) 1095 Phosphodioxy group (PO2− in nucleic acids) (Liu et al., 2008) 1242 Amide III (β sheet and random coils) (Lakshmi et al., 2002) 1333 Guanine (Ruiz‐Chica et al., 2004) 1445 CH2 bending mode of proteins and lipids (D. P. Lau et al., 2003) 1577 Guanine (Ruiz‐Chica et al., 2004) 1660 Amide I (Naumann, 1998) 2930 C-H stretch (Koljenović et al., 2005) 1133 C-C skeletal stretching (Nawaz et al., 2011) Fatty acid (Krafft, Neudert, Simat, & Salzer, 2005) 1542 Amide II (Stone, Kendall, Smith, Crow, & Barr, 2004) Identification of Arcobacter species to the species level In order to build up a database for the identification of Arcobacter bacteria at species level, 18 different Arcobacter species (82 strains) isolated from various sources in both Germany and Canada were included in the speciation study. The identity of these isolates was confirmed by using PCR. As shown in Figure 3.3, the average spectra of most Arcobacter species exhibit a high level of similarities except for several species with additional Raman 52 bands. Thus, spectral deconvolution methods to distinguish different Arcobacter species based upon the minor variations in the corresponding Raman spectral features are highly required. In the current study, a supervised CNN model using 3 convolutional layers was established and cross-validated to classify different Arcobacter species. Twenty-one spectra of each species were used to establish the model and each spectrum is an average of 5 Raman spectra collected from independent triplicates to reduce the operational variations. Leave-one-out cross validation (n=240) was performed to evaluate the stability of the established model and the classification results are summarized in Table 3.4. An overall accuracy of 94.13% was achieved for the discrimination of 18 Arcobacter species using CNN. The recognition accuracy varied among different species from 80.4% to 100%. Specifically, 100% accuracy was obtained to identify A. trophiarum and A. bivalviorum, indicating that all the tested spectra of these species were consistently assigned to the correct species during cross-validation. Eight species, namely A. thereius, A. aquimarinus, A. nitrofigilis, A. mytili, A. halophilus, A. marius, A. defluvii, and A. ellisii demonstrated a high accuracy that was ≥95%. Besides, accuracy ranging from 90% to 95% was observed for A. cryaerophilus (93.8%), A. cibarius (94.6%), A. lanthieri (91.7%) and A. molluscorum (94.2%). Compared to these species, a lower but acceptable accuracy was observed for 4 species: 80.4% for A. butzleri, 89.2% for A. skirrowii, 83.8% for A. faecis, and 81.7% for A. venerupis. Most of the incorrect classifications occurred between A. butzleri and A. skirrowii, where 19 out of 240 spectra of A. butzleri were misclassified as A. skirrowii while 25 out of 240 spectra of A. skirrowii were mislabelled as A. butzleri. A. butzleri was also misidentified as A. lanthieri and vice versa. Besides, mislabelled A. faecis strains were mainly predicted as A. cibarius, and A. venerupis was incorrectly assigned to A. marius. The misclassification might be due 53 to the high physiological similarities between these species (Banting & Figueras, 2017; Collado & Figueras, 2011; Pérez-Cataluña, Salas-Massó, Dieguez, et al., 2018). In addition, the high specificity ranging from 80.1% to 100% was achieved for all Arcobacter species. Based on the high accuracy and specificity of the validation results, it can be concluded that Raman spectroscopy in combination with CNN could be an effective approach for the identification of Arcobacter to the species level.54 Table 3.4 Cross-validation results of convolutional neural network model for the classification of Arcobacter bacteria at species level (n=240). Predicted species True speciesa Abut Acry Aski Acib Athe Alan Aaqu Afae Aven Anit Amyt Atro Ahal Amar Abiv Amol Adef Aell Accuracy (%) Abut 193 19 1 16 9 1 1 80.4 Acry 225 1 2 10 2 93.8 Aski 25 1 214 89.2 Acib 5 227 6 1 1 94.6 Athe 1 239 99.6 Alan 20 220 91.7 Aaqu 1 238 1 99.2 Afae 1 13 6 201 7 6 4 2 83.8 Aven 5 6 4 196 18 7 4 81.7 Anit 1 239 99.6 Amyt 3 236 1 98.3 Atro 240 100 Ahal 4 235 1 97.9 Amar 1 3 236 98.3 Abiv 240 100 Amol 1 1 6 6 226 94.2 Adef 1 6 5 228 95.0 Aell 3 4 233 97.1 Specificity (%) 80.1 97.8 88.1 94.2 98.0 88.7 99.2 88.5 96.6 97.6 100 95.6 96.7 88.4 97.6 92.6 98.7 97.1 Overall accuracy 94.13% a Abut: A. butzleri, Acry: A. cryaerophilus, Aski: A. skirrowii, Acib: A. cibarius, Athe: A. thereius, Alan: A. lanthieri, Aaqu: A. aquimarinus, Afae: A. faecis, Aven: A. venerupis, Anit: A. nitrofigilis, Amyt: A. mytili, Atro: A. trophiarum, Ahal: A. halophilus, Amar: A. marius, Abiv: A. bivalviorum, Amol: A. molluscorum, Adef: A. defluvii, Aell: A. ellisi 55 Sensitivity of Raman spectroscopy to detect a specific Arcobacter within a mixture When it comes to analyzing real food, environmental or clinical samples, multiple bacteria might co-exist in one sample, making the identification of the targeted bacteria within the bacterial mixture to be more complicated. To mimic the real situations and determine the sensitivity and selectivity of Raman spectroscopy for such an application, we investigated the capability of Raman spectroscopy for the detection of a specific Arcobacter species within a mixture of other bacterial species. A. butzleri was selected as the targeted species because it is the most clinically important species within genus Arcobacter (Banting & Figueras, 2017). The targeted strain (i.e., A. butzleri CCUG 30485) was mixed with a prepared Arcobacter cocktail composed of 3 other Arcobacter species [A. aquimarinus 2528, A. trophiarum LMG 25534 (T) and A. lanthieri AF 1680] at different ratios ranging from 5 to 100% by biomass to form new bacterial mixtures. These mixtures were tested using Raman spectroscopy and then analyzed by chemometric models. A BPNN model was established and cross-validated to correlate the ratio of the targeted Arcobacter species within the mixture and its corresponding Raman spectral patterns. As shown in Figure 3.4, a clear linear relationship (blue line) between the predicted ratio (predicted based upon the Raman spectral features) and the real ratio of the targeted Arcobacter species was observed, demonstrating a reliable correlation of Raman spectral features and the ratio of A. butzleri within the mixture. The red dots represent the relative error of each predicted ratio generated by this model compared to the real ratio, which were all lower than 1% (value was read according to the red y-axis on the right). Thus, the accuracy of this model for the prediction of the targeted bacterial percentage within a bacterial mixture was higher than 99%. In addition, a high R2 (>0.99) and a low RMSE (<0.1) were obtained for the validated BPNN. According to the previous 56 studies (Kuang, Tekin, & Mouazen, 2015; Lu et al., 2012), R2 >0.95 and RMSE <1 indicate a good calibration model used for quantitative prediction. Figure 3.4 Prediction of the actual ratio of a specific Arcobacter species within a bacterial mixture using back-propagation neural network (BPNN). A. butzleri was mixed with a bacterial cocktail of A. aquimarinus, A. trophiarum and A. lanthieri at different ratios of 5%, 25%, 50%, 75% and 100% by biomass, respectively. BPNN model was evaluated with “leave-one-out” cross-validation and red dots represent the relative error of the predicted ratio compared to the actual ratio of A. butzleri (n=10). R2: coefficient of determination; RMSE: root mean square error. 57 Detection of Arcobacter in milk We further evaluated this method for the detection of Arcobacter in real food samples. Sterile UHT milk was spiked with Arcobacter at different concentrations, followed by enrichment in MH broth. Using milk samples spiked with 5.7 CFU/mL of Arcobacter as an example, the detection result could be confirmed by visualization of Raman spectra and using PCA model. We compared the Raman spectra collected from Arcobacter in milk with initial concentration of 5.7 CFU/mL after different enrichment times and the pure bacterial culture as the positive control (Figure 3.5). Raman bands at 722, 779, 1095, 1133 and 1577 cm-1 appeared and their intensities increased over time. These bands were also identified in pure Arcobacter culture and assigned to nucleic acids of the bacterial cells (Talari et al., 2015). Therefore, these bands are considered as signature Raman bands of Arcobacter. In comparison, the relative intensity of Raman bands at 849 cm-1 and 946 cm-1 decreased after bacterial enrichment. These two bands are derived from single bond stretching vibrations from amino acids and polysaccharides (Gniadecka et al., 1997), which might be attributed to the interference signal from milk residues. After 28 h, there was no visible difference between Raman spectra of Arcobacter in milk and that in the pure culture, indicating that Arcobacter could be identified in milk. In addition, a 2D PCA model was constructed to compare these Raman spectra and further validate the detection results (Figure 3.6). Raman spectra collected at different time points formed different clusters based on the spectral features. Arrows indicate that the Raman spectral features of Arcobacter in milk changed in a specific trend along with the enrichment time. Spectra collected from 16 h to 28 h followed a downward left trajectory and located closer to the pure Arcobacter spectra (positive control), indicating the higher similarities among spectra. The spectra collected at 28 h were 58 overlapped with the positive control, which was consistent to the result displayed in Figure 3.5. Therefore, Arcobacter in the milk samples with initial concentration of 5.7 CFU/mL were successfully identified by using Raman spectroscopy after enrichment for 28 h. The enrichment time periods required for the detection of Arcobacter in milk with different initial bacterial concentrations are summarized in Table 3.5. Arcobacter could be detected from milk within a shorter enrichment time when the inoculation concentration increased. Taken together, Raman spectroscopy has the potential to detect the trace level of Arcobacter in milk after enrichment. 59 Figure 3.5 Comparison of Raman spectra collected from A. butzleri in milk with different enrichment times as well as in pure bacterial culture. Initial spiking concentration of A. butzleri was 5.7×100 CFU/mL and the spiked milk was mixed with MH broth at a ratio of 1:9 (v/v) for enrichment. Shaded areas indicate the marked spectral variations among different Raman spectra. 60 Figure 3.6 Principal component analysis plot of Raman spectra for A. butzleri in milk with different enrichment times. Initial spiking concentration of A. butzleri was 5.7×100 CFU/mL and the spiked milk was mixed with MH broth at a ratio of 1:9 (v/v) for enrichment. Dots labeled with number in different colors represent Raman spectra collected from A. butzleri in milk at different time points (h) and red pentagrams represent Raman spectra of A. butzleri in pure bacterial culture. Arrows indicate that Raman spectral features of Arcobacter in milk changed in a specific trend along with the enrichment time. 61 Table 3.5 Detection of Arcobacter in milk after enrichment using Raman spectroscopy. Spiking concentration (CFU/mL) a Enrichment time (h) Assigned to b 5.7×100 28 Arcobacter 5.7×101 28 Arcobacter 8.9×102 28 Arcobacter 6.4×103 24 Arcobacter 6.4×104 20 Arcobacter 6.4×105 16 Arcobacter a The initial spiking concentration of Arcobacter in milk was determined by using the conventional plating assay and calculated based upon three independent replicates. b Raman spectra collected from Arcobacter in milk samples were overlapped with the spectra of Arcobacter in pure culture in the principal component analysis plot (e.g., Figure 3.6). 62 Chapter 4: Discussion Arcobacter species are becoming increasingly important as they have been considered as emerging foodborne and waterborne pathogens (Collado & Figueras, 2011). The most frequently reported species, including A. butzleri, A. cryaerophilus, A. skirrowii and A. cibarius, are of great clinical importance because they have been associated with various illnesses in human and animals (Shah et al., 2013; Vandamme, Vancanneyt, et al., 1992). However, there is limited data related to the epidemiology of Arcobacter probably due to the lack of appropriate detection and typing methods. Conventional culture-based methods are usually time-consuming and laborious (Shah et al., 2011). Molecular-based and immunological detection methods have been commonly used, but most of these only target on several Arcobacter species and require complicated sample preparation procedures, expensive reagents and highly trained personnel (Banting & Figueras, 2017). Therefore, a rapid and reliable detection method for Arcobacter is highly required. Raman spectroscopy has been emerged as an effective analytical technique for clinical microbiology. Raman spectroscopy is a vibrational spectroscopic technique that can provide comprehensive biochemical and compositional information of bacterial cells without any labeling (Butler et al., 2016). Furthermore, this method is rapid, easy to conduct and non-invasive (Lorenz et al., 2017), making itself an attractive method for biological applications and clinical use. Raman spectroscopy has been widely used to detect various pathogens to assist in clinical diagnostics, such as Listeria monocytogenes (Green et al., 2009; J. Wang et al., 2015), Campylobacter (Lu et al., 2012), Salmonella (Assaf et al., 2014), Escherichia coli O157:H7 (Efrima & Zeiri, 2009) and Staphylococcus aureus (Nicolaou et al., 2011). To the best of our knowledge, there is no study that has applied Raman spectroscopy for Arcobacter 63 identification. In the current study, we employed Raman spectroscopy coupled with various chemometric models to detect and identify Arcobacter to the species level, which has a high potential to be used for the epidemiological surveillance of Arcobacter bacteria. In the first step, we evaluated whether identification of Arcobacter from other closely related pathogens was possible. As shown in Figure 3.1, Raman spectra from Arcobacter, Campylobacter and Helicobacter are successfully separated into 3 groups in PCA plot and the corresponding inter-group MD (>3) further confirmed the discrimination among three genera (Table 3.1). Campylobacter and Helicobacter are phenotypically and genetically similar to Arcobacter and also prevalently present in the common Arcobacter reservoirs, such as chicken and raw milk (On, 2001; Silva et al., 2011). Misidentifications of Arcobacter to the other two genera especially Campylobacter have been reported in conventional biochemical assays as well as PCR-based methods (Banting & Figueras, 2017; Collado & Figueras, 2011). In the current study, Raman spectroscopy in combination with PCA was capable of reliable identification of Arcobacter to the genus level. Since PCA is an unsupervised model, it clusters the spectra in an unbiased manner, which is based on the spectral variances alone without knowing the class membership (Abdi & Williams, 2010). The classification results of PCA are fully consistent to the taxonomic relationship, indicating that Raman spectral features show high discrimination power for bacterial typing. A loading plot (Figure A.1) of the first PC used in the PCA model demonstrated that Raman spectral features corresponding to nucleic acids and amine II were the key features that determine the identification of genus Arcobacter to other closely related genera. This agreed with other studies (Lu, Al-Qadiri, et al., 2011; Lu et al., 2012) that using Raman 64 spectroscopy for bacterial identification, where variances of secondary structure of nucleic acids or proteins in the bacterial cells dominated the classification among different microorganisms. Spectral reproducibility is a critical factor to the reliability of Raman spectroscopic classification models. Experiments were usually performed using bacteria cultivated under the recommended conditions, but the cultivation conditions of Arcobacter under real environmental situations are generally unknown, such as nutrition, temperature, humidity or cultivation age. A varying cultivation condition might result in variations in the biochemical composition of microbial cells (Phillips & Duggan, 2002) and subsequently affect the reproducibility of Raman spectra (Harz et al., 2005; Lu, Rasco, et al., 2011). Thus, we evaluated the influence of cultivation time and temperature on the Raman spectral reproducibility of some representative Arcobacter species and employed a PCA model to evaluate the discrimination capability of Raman spectroscopic-based approach. As shown in Figure 3.2, Raman spectra of 3 different Arcobacter species cultivated for various time periods (i.e., 36 to 72 h) or at different temperatures (i.e., 30 or 37°C) were clearly classified into distinct groups based upon the bacterial species, which was confirmed by calculating the inter-group MDs (Table 3.2). Thus, neither the cultivation time nor incubation temperature did significantly influence the spectral reproducibility of Arcobacter cells and discrimination capacity of the Raman spectroscopic-based PCA model. Similar results were reported by Harz and coauthors (Harz et al., 2005) that 8 Staphylococcus strains cultivated with different nutrients, incubation temperatures and culture ages were analyzed and correctly identified to the strain level by Raman spectroscopy with chemometric models. In another study (van de 65 Vossenberg et al., 2013), Raman spectra of Legionella, E. coli and coliform strains were reproducible under various cultivation conditions with respect to cultivation age, temperature and water types. Therefore, Raman spectroscopy has the potential to be used as a robust and reliable approach for the identification of Arcobacter species under different cultivation conditions. Due to the high natural diversity of the genus Arcobacter, it was imperative to further evaluate the discrimination capability of Raman spectroscopy for different Arcobacter species. Raman spectral profiles of 82 strains representing18 different Arcobacter species were collected and analyzed to establish a comprehensive and reliable reference database. By using a CNN model, identification accuracy ranging from 80.4% to 100% were obtained for all of the investigated Arcobacter species (Table 3.4). CNN is one of the most powerful deep learning models that combines feature extraction and subsequent classification in a single algorithm. It has been demonstrated that CNN could be effectively used to classify vibrational spectroscopic data with a superior performance compared to other frequently used classification algorithms (e.g., SVM) (Acquarelli et al., 2017; J. Liu et al., 2017). However, since CNN is a “black-box” technique, it is not possible to determine the specific molecules that responsible for the discrimination of different Arcobacter species. Several regions with spectral variations among different bacterial spectra were highlighted in grey (Figure 3.3), which might contribute to the identification of Arcobacter to the species level. For example, two dominant bands representing fatty acids and amide II were only observed in the spectra of A. cryaerophilus, A. aquimarinus, A. mytili and A. trophiarum, which clearly distinguished them from other species. Thus, a high identification accuracy (i.e., 93.8 to 66 100%) was achieved for these four species. Misidentifications most frequently occurred between A. butzleri and A. skirrowii, and therefore the identification accuracy of A. butzleri and A. skirrowii in the CNN model was 80.4% and 89.2%, respectively. It has also been reported that A. skirrowii was misidentified as A. butzleri and vice versa by using PCR-denaturing gradient gel electrophoresis (Petersen, Harrington, Kortegaard, & On, 2007), because they generated equal patterns in this detection methods. A phylogenetic analysis based on the core genome containing 286 genes revealed the high genotypic similarity between A. skirrowii and A. butzleri (Pérez-Cataluña, Salas-Massó, Dieguez, et al., 2018). High phenotypic and genotypic similarity were also reported between A. faecis and A. cibarius, A. marius and A. venerupis (Pérez-Cataluña, Salas-Massó, Dieguez, et al., 2018), which might explain the confusion with these species in CNN model. No standardized reference method for the detection of Arcobacter has been proposed so far. Although a variety of PCR-based assays has been described for the identification and speciation of Arcobacter targeting various genes (Banting & Figueras, 2017), the discrimination power of these assays is limited and the majority of these methods target only several major species (e.g., A. butzleri, A. cryaerophilus and A. skirrowii). In the current study, a total of 18 Arcobacter species were correctly discriminated by Raman spectroscopy, covering a broad fraction of diversity among Arcobacter genus. By enlarging the reference database, Raman spectroscopic technique has the potential to speciate all of the Arcobacter bacteria. The ability to detect a specific Arcobacter species within a bacterial mixture is of great importance, especially for the identification of bacteria in real world conditions with unknown bacterial compositions. We used a BPNN model to predict the real ratio of the 67 selected A. butzleri within a background bacterial mixture composed of 3 other Arcobacter species based upon the Raman spectral features of these composites. A good correlation between the actual ratio of the targeted A. butzleri and corresponding Raman spectral features was observed (Figure 3.4) and the accuracy of bacterial prediction was higher than 99%, indicating that Raman spectroscopy could identify a particular Arcobacter species with a high sensitivity and selectivity. The selection of appropriate calibration method is one of the important factors that influences the detection accuracy of vibrational spectroscopy. According to a recent study (Mouazen et al., 2010), BPNN achieved a better performance compared to other calibration models, such as principal component regression and PLSR, for a measurement using infrared spectroscopy. Thus, BPNN was used in the present study instead of the commonly used PLSR, and the high R2 (>0.99) and low RMSE (<0.1) obtained further validated the feasibility of BPNN for Raman spectroscopy-based quantitative prediction. Since clinical and environmental samples may contain several different Arcobacter species (Samie et al., 2007), reliable identification of a specific species in a bacterial mixture can contribute to more accurate epidemiological surveillance. The ability of Raman spectroscopy for the detection of trace amount of Arcobacter in milk was also investigated. Milk was spiked with Arcobacter at different concentrations and tested after different enrichment time periods. By comparing the Raman spectra collected from Arcobacter-spiked milk with those of pure Arcobacter culture (Figure 3.5), signature Raman bands at 722, 779, 1095, 1333, 1577 cm-1 corresponding to nucleic acids could be identified in the spectra of milk sample after enrichment. At the early stage of enrichment, bacterial concentration was low and therefore Raman signal from Arcobacter was weak and 68 significantly affected by the signals from the milk components. After a certain enrichment time, Raman signal generated from the bacterial cells increased as bacterial population increased. Finally, Raman signal intensity from Arcobacter was strong enough to mask the interference signals from the milk components and dominate the spectral features. Therefore, Raman spectra collected from Arcobacter-spiked milk sample with enrichment time of 28 h showed highly similar patterns to the spectra of pure Arcobacter culture. The location of Raman spectra in the PCA model (Figure 3.6) further confirmed the identification of Arcobacter from milk. Raman spectra collected after a longer enrichment time were located closer to the pure Arcobacter spectra, indicating that the spectral features are dominated by Arcobacter rather than the milk components. After a certain enrichment time (i.e., 28 h for initial concentration of 5.7 CFU/mL of Arcobacter in milk), milk sample could not be separated from pure Arcobacter culture in the PCA space, suggesting the positive results for the detection of Arcobacter in milk. A shorter enrichment time was required to detect a relatively high concentration of Arcobacter in milk because enough bacterial cells could be generated in a shorter time with a high starting concentration. In addition, centrifugation and washing steps were used as sample preparation procedures before Raman spectroscopic analysis, which was not only able to isolate bacteria from milk but also served as a concentration method to increase the bacterial concentration in the tested sample for spectral collection. This procedure is easy to perform and does not require highly trained personnel, which is suitable for rapid detection of pathogens in food samples. Other foodborne pathogens, such as Brucella (Meisel et al., 2012), Staphylococcus aureus (Nicolaou et al., 2011) and Listeria monocytogenes (J. Wang et al., 2015) were also directly identified from 69 milk using Raman spectroscopy. Thus, Raman spectroscopy is a promising alternative to detecting bacteria in milk without a demanding process for sample preparation. In this study, the entire identification process for one sample requires less than one hour including sample preparation and Raman spectroscopic analyses when the classification model has been established. Thus, it is considered as a rapid detection tool compared to the conventional biochemical assay (5 to 7 days) and genotyping methods, such as pulsed-field gel electrophoresis (3 to 4 days) and multilocus sequence typing (>24 h). In addition, Raman typing is easy-to-conduct, noninvasive, does not require any reagent and shows a high discrimination power. These advantages make Raman spectroscopy a powerful tool for bacterial epidemiology. 70 Chapter 5: Conclusion Study summary In this study, we developed a rapid and accurate approach for the detection of Arcobacter bacteria using Raman spectroscopy coupled with chemometric models. Firstly, we identified Arcobacter to the genus level. Raman spectra collected from representative species of 3 closely related genera, Arcobacter, Campylobacter and Helicobacter were well separated in a 3D PCA model and the variations of nucleic acids and proteins in bacterial cells contributed most to the discrimination of Arcobacter from other bacteria. Besides, bacterial cultivation time and temperature did not significantly influence Raman spectral reproducibility and the discrimination capability of Raman spectroscopic-based PCA model. Three Arcobacter species cultivated under different incubation temperatures (i.e., 30°C and 37°C) or different time periods (i.e., 36 to 72 h) were clustered in a single group in the PCA model and the intra-group variation was not visually distinguished. Furthermore, Raman spectroscopy achieved accurate and reliable speciation of Arcobacter bacteria. Each Arcobacter species showed unique Raman spectral patterns and minor spectral variations between different Arcobacter species were observed. By using Raman spectroscopy combined with a CNN model, a total of 82 Arcobacter strains representing 18 Arcobacter species were successfully identified to species level with a high overall accuracy of 94%. The sensitivity and selectivity of Raman spectroscopy for the detection of a specific Arcobacter within a mixed bacterial population were also validated. The actual ratio of A. butzleri in a bacterial mixture ranging from 5% to 100% by biomass could be determined via a Raman spectroscopy-based BPNN with the accuracy higher than 99%. Finally, Raman 71 spectroscopy showed the ability to detect trace level (100-101 CFU/mL) of Arcobacter from milk after enrichment with simple sample preparation procedures. To the best of our knowledge, this is the first study of using Raman spectroscopy for Arcobacter identification and speciation. Compared to the conventional biochemical assay and genotyping methods, Raman spectroscopy is rapid, easy to perform and provides a high discrimination power. Moreover, this study also demonstrates that Raman spectroscopy does not require any reagent, which is cost-effective compared to MALDI-TOF MS. Therefore, Raman spectroscopy coupled with chemometric models provides a rapid, accurate and reliable diagnostic tool that can be applied to epidemiological surveillance of Arcobacter in the food chain. This technique has the potential to be used as a standard for routine bacterial screening in the government agencies, food industries and clinical settings. Future directions This study demonstrates that Raman spectroscopy is capable of identifying and discriminating Arcobacter bacteria in pure culture and milk samples. There are still issues to be addressed in future when applying this technique to identify Arcobacter from various real environmental, clinical and food samples. Additional sample preparation and isolation procedures may be required and optimized to improve the identification performance. In addition, detection of bacteria at single-cell level using Raman spectroscopy without the need for enrichment has been reported (Kloß et al., 2013). Further works should be conducted to achieve single-cell typing to decrease the time of bacterial enrichment and improve the accuracy of Raman spectroscopic-based detection of Arcobacter. 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The peaks with heavy weight higher than 0.05 are identified: 780 cm-1, uracil-based ring breathing mode; 1136, C-C skeletal stretching; 1540 cm-1, amide II.
Thesis/Dissertation
eng
Food Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Detection and speciation of Arcobacter bacteria using Raman spectroscopy
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