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Abstract

With advances in artificial intelligence (AI) technologies, the development and implementation of digital food systems are becoming increasingly possible. There is tremendous interest in using different AI applications, such as machine learning models, natural language processing, and computer vision to improve food safety. Possible AI applications are broad and include, but are not limited to, () food safety risk prediction and monitoring as well as food safety optimization throughout the supply chain, () improved public health systems (e.g., by providing early warning of outbreaks and source attribution), and () detection, identification, and characterization of foodborne pathogens. However, AI technologies in food safety lag behind in commercial development because of obstacles such as limited data sharing and limited collaborative research and development efforts. Future actions should be directed toward applying data privacy protection methods, improving data standardization, and developing a collaborative ecosystem to drive innovations in AI applications to food safety.

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2023-03-27
2024-04-16
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Literature Cited

  1. Allen JP, Snitkin E, Pincus NB, Hauser AR. 2021. Forest and trees: exploring bacterial virulence with genome-wide association studies and machine learning. Trends Microbiol. 29:7621–33
    [Google Scholar]
  2. Arning N, Sheppard SK, Bayliss S, Clifton DA, Wilson DJ. 2021. Machine learning to predict the source of campylobacteriosis using whole genome data. PLOS Genet. 17:10e1009436
    [Google Scholar]
  3. Astill J, Dara RA, Fraser EDG, Roberts B, Sharif S. 2020. Smart poultry management: smart sensors, big data, and the internet of things. Comput. Electron. Agric. 170:105291
    [Google Scholar]
  4. Athamanolap P, Hsieh K, O'Keefe CM, Zhang Y, Yang S, Wang T-H 2019. Machine learning-assisted digital PCR and melt enables broad bacteria identification and pheno-molecular antimicrobial susceptibility test. bioRxiv 587543. https://doi.org/10.1101/587543
  5. Balkey M, Batz M, Gopinath G, Gosal G, Griffiths E, Tate H. 2021. Standardizing the isolation source metadata for the genomic epidemiology of foodborne pathogens using LexMapr Poster presented at FDA Science Forum May 26
  6. Barco L, Barrucci F, Olsen JE, Ricci A. 2013. Salmonella source attribution based on microbial subtyping. Int. J. Food Microbiol. 163:2–3193–203
    [Google Scholar]
  7. Barnett-Neefs C, Sullivan G, Zoellner C, Wiedmann M, Ivanek R. 2022. Using agent-based modeling to compare corrective actions for Listeria contamination in produce packinghouses. PLOS ONE 17:3e0265251
    [Google Scholar]
  8. Barreiro JR, Ferreira CR, Sanvido GB, Kostrzewa M, Maier T et al. 2010. Short communication: identification of subclinical cow mastitis pathogens in milk by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. J. Dairy Sci. 93:125661–67
    [Google Scholar]
  9. Belias A, Brassill N, Roof S, Rock C, Wiedmann M, Weller D. 2021. Cross-validation indicates predictive models may provide an alternative to indicator organism monitoring for evaluating pathogen presence in southwestern US agricultural water. Front. Water 3:693631
    [Google Scholar]
  10. Benjamens S, Dhunnoo P, Meskó B. 2020. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. npj Digit. Med. 3:118
    [Google Scholar]
  11. Böhme K, Fernández-No IC, Barros-Velázquez J, Gallardo JM, Calo-Mata P, Cañas B 2010. Species differentiation of seafood spoilage and pathogenic gram-negative bacteria by MALDI-TOF mass fingerprinting. J. Proteome Res. 9:63169–83
    [Google Scholar]
  12. Bouzembrak Y, Marvin HJP. 2016. Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling. Food Control 61:180–87
    [Google Scholar]
  13. Carvalho TP, Soares FAAMN, Vita R, da P Francisco R, Basto JP, Alcalá SGS. 2019. A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137:106024
    [Google Scholar]
  14. CDC (Cent. Dis. Control) 2021. FoodNet Fast. Centers for Disease Control. https://wwwn.cdc.gov/foodnetfast/
    [Google Scholar]
  15. Chaudhuri A, Dukovska-Popovska I, Subramanian N, Chan HK, Bai R. 2018. Decision-making in cold chain logistics using data analytics: a literature review. Int. J. Logist. Manag. 29:3839–61
    [Google Scholar]
  16. Chen JX. 2016. The evolution of computing: AlphaGo. Comput. Sci. Eng. 18:44–7
    [Google Scholar]
  17. Chen TC, Yu SY. 2022. The review of food safety inspection system based on artificial intelligence, image processing, and robotic. Food Sci. Technol. 42:e35421
    [Google Scholar]
  18. Copeland BJ. 2021. Artificial intelligence. Encyclopedia Britannica. https://www.britannica.com/technology/artificial-intelligence
    [Google Scholar]
  19. Defraeye T, Shrivastava C, Berry T, Verboven P, Onwude D et al. 2021. Digital twins are coming: Will we need them in supply chains of fresh horticultural produce?. Trends Food Sci. Technol. 109:245–58
    [Google Scholar]
  20. Deng X, Cao S, Horn AL. 2021. Emerging applications of machine learning in food safety. Annu. Rev. Food Sci. Technol. 12:513–38
    [Google Scholar]
  21. DiMucci D, Kon M, Segrè D. 2018. Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks. mSystems 3:5e00181-18
    [Google Scholar]
  22. Du Y, Guo Y. 2022. Machine learning techniques and research framework in foodborne disease surveillance system. Food Control 131:108448
    [Google Scholar]
  23. Duarte ASR, Röder T, Van Gompel L, Petersen TN, Hansen RB et al. 2021. Metagenomics-based approach to source-attribution of antimicrobial resistance determinants: identification of reservoir resistome signatures. Front. Microbiol. 11:3447
    [Google Scholar]
  24. Dyda A, Purcell M, Curtis S, Field E, Pillai P et al. 2021. Differential privacy for public health data: an innovative tool to optimize information sharing while protecting data confidentiality. Patterns 2:12100366
    [Google Scholar]
  25. Effland T, Lawson A, Balter S, Devinney K, Reddy V et al. 2018. Discovering foodborne illness in online restaurant reviews. J. Am. Med. Inform. Assoc. 25:121586–92
    [Google Scholar]
  26. Escrig J, Woolley E, Rangappa S, Simeone A, Watson NJ. 2019. Clean-in-place monitoring of different food fouling materials using ultrasonic measurements. Food Control 104:358–66
    [Google Scholar]
  27. Escrig J, Woolley E, Simeone A, Watson NJ. 2020a. Monitoring the cleaning of food fouling in pipes using ultrasonic measurements and machine learning. Food Control 116:107309
    [Google Scholar]
  28. Escrig JE, Simeone A, Woolley E, Rangappa S, Rady A, Watson NJ. 2020b. Ultrasonic measurements and machine learning for monitoring the removal of surface fouling during clean-in-place processes. Food Bioprod. Process. 123:1–13
    [Google Scholar]
  29. Eur. Comm 2018. Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions. Artificial intelligence for Europe. Eur-Lex. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2018%3A237%3AFIN%0A
    [Google Scholar]
  30. FDA (US Food Drug Adm.) 2022a. Import screening pilot unleashes the power of data and leverages artificial intelligence. FDA https://www.fda.gov/news-events/fda-voices/import-screening-pilot-unleashes-power-data-and-leverages-artificial-intelligence
    [Google Scholar]
  31. FDA (US Food Drug Adm.) 2022b. TechTalk podcast episode 3: artificial intelligence in the new era of smarter food safety. . FDA. https://www.fda.gov/food/new-era-smarter-food-safety-techtalk-podcast/techtalk-podcast-episode-3-artificial-intelligence-new-era-smarter-food-safety
    [Google Scholar]
  32. Feng YZ, Sun DW. 2012. Application of hyperspectral imaging in food safety inspection and control: a review. Crit. Rev. Food Sci. Nutr. 52:111039–58
    [Google Scholar]
  33. Fernández-Navarro F, Valero A, Hervás-Martínez C, Gutiérrez PA, García-Gimeno RM, Zurera-Cosano G. 2010. Development of a multi-classification neural network model to determine the microbial growth/no growth interface. Int. J. Food Microbiol. 141:3203–12
    [Google Scholar]
  34. Ferris MC, Christensen A, Wangen SR. 2020. Symposium review: Dairy Brain—informing decisions on dairy farms using data analytics. J. Dairy Sci. 103:43874–81
    [Google Scholar]
  35. Fisch D, Evans R, Clough B, Byrne SK, Channell WM et al. 2021. HRMAn 2.0: next-generation artificial intelligence-driven analysis for broad host-pathogen interactions. Cell. Microbiol. 23:7e13349
    [Google Scholar]
  36. Fisch D, Yakimovich A, Clough B, Wright J, Bunyan M et al. 2019. Defining host-pathogen interactions employing an artificial intelligence workflow. eLife 8:e40560
    [Google Scholar]
  37. Friedlander A, Zoellner C. 2020. Artificial intelligence opportunities to improve food safety at retail. Food Prot. Trends 40:4272–78
    [Google Scholar]
  38. Garver K. 2018. 6 examples of artificial intelligence in the food industry. Food Industry Executive https://foodindustryexecutive.com/2018/04/6-examples-of-artificial-intelligence-in-the-food-industry
    [Google Scholar]
  39. Ghannam RB, Techtmann SM. 2021. Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring. Comput. Struct. Biotechnol. J. 19:1092–107
    [Google Scholar]
  40. Golden CE, Rothrock MJ, Mishra A. 2019a. Comparison between random forest and gradient boosting machine methods for predicting Listeria spp. prevalence in the environment of pastured poultry farms. Food Res. Int. 122:47–55
    [Google Scholar]
  41. Golden CE, Rothrock MJ, Mishra A. 2019b. Using farm practice variables as predictors of Listeria spp. prevalence in pastured poultry farms. Front. Sustain. Food Syst. 3:15
    [Google Scholar]
  42. Goodswen SJ, Barratt JLN, Kennedy PJ, Kaufer A, Calarco L, Ellis JT. 2021. Machine learning and applications in microbiology. FEMS Microbiol. Rev. 45:5fuab015
    [Google Scholar]
  43. Gupta A, Anpalagan A, Guan L, Khwaja AS. 2021. Deep learning for object detection and scene perception in self-driving cars: survey, challenges, and open issues. Array 10:100057
    [Google Scholar]
  44. Haiminen N, Edlund S, Chambliss D, Kunitomi M, Weimer BC et al. 2019. Food authentication from shotgun sequencing reads with an application on high protein powders. npj Sci. Food. 3:24
    [Google Scholar]
  45. Harrand AS, Strawn LK, Illas-Ortiz PM, Wiedmann M, Weller DL. 2020. Listeria monocytogenes prevalence varies more within fields than between fields or over time on conventionally farmed new york produce fields. J. Food Prot. 83:111958–66
    [Google Scholar]
  46. Harris ZN, Dhungel E, Mosior M, Ahn TH. 2019. Massive metagenomic data analysis using abundance-based machine learning. Biol. Direct 14:12
    [Google Scholar]
  47. Harrison C, Jorder M, Stern H, Stavinsky F, Reddy V et al. 2014. Using online reviews by restaurant patrons to identify unreported cases of foodborne illness—New York City, 2012–2013. MMWR 63:20441–45
    [Google Scholar]
  48. Harrison L, Mukherjee S, Hsu CH, Young S, Strain E et al. 2021. Core genome MLST for source attribution of Campylobacter coli. Front. Microbiol. 12:703890
    [Google Scholar]
  49. Hazen TH, Martinez RJ, Chen Y, Lafon PC, Garrett NM et al. 2009. Rapid identification of Vibrio parahaemolyticus by whole-cell matrix-assisted laser desorption ionization-time of flight mass spectrometry. Appl. Environ. Microbiol. 75:216745–56
    [Google Scholar]
  50. Healthy People 2020. Healthy people 2020 objectives and topics: food safety. Healthy People. https://wayback.archive-it.org/5774/20220415004842/https://www.healthypeople.gov/2020/data-search/Search-the-Data?nid=4478
    [Google Scholar]
  51. Henrichs E, Noack T, Piedrahita AMP, Salem MA, Stolz J, Krupitzer C. 2022. Can a byte improve our bite? An analysis of digital twins in the food industry. Sensors 22:1115
    [Google Scholar]
  52. Hiura S, Koseki S, Koyama K. 2021. Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database. Sci. Rep. 11:10613
    [Google Scholar]
  53. Ho CS, Jean N, Hogan CA, Blackmon L, Jeffrey SS et al. 2019. Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nat. Commun. 10:4927
    [Google Scholar]
  54. Hyun JC, Kavvas ES, Monk JM, Palsson BO. 2020. Machine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens. PLOS Comput. Biol. 16:3e1007608
    [Google Scholar]
  55. Ivanek R, Gröhn YT, Wells MT, Lembo AJ, Sauders BD, Wiedmann M. 2009. Modeling of spatially referenced environmental and meteorological factors influencing the probability of Listeria species isolation from natural environments. Appl. Environ. Microbiol. 75:185893–909
    [Google Scholar]
  56. Jamal S, Khubaib M, Gangwar R, Grover S, Grover A, Hasnain SE. 2020. Artificial intelligence and machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis. Sci. Rep. 10:5487
    [Google Scholar]
  57. Kakani V, Nguyen VH, Kumar BP, Kim H, Pasupuleti VR. 2020. A critical review on computer vision and artificial intelligence in food industry. J. Agric. Food Res. 2:100033
    [Google Scholar]
  58. Kamble SS, Gunasekaran A, Gawankar SA. 2020. Achieving sustainable performance in a data-driven agriculture supply chain: a review for research and applications. Int. J. Prod. Econ. 219:179–94
    [Google Scholar]
  59. Kamilaris A, Prenafeta-Boldú FX. 2018. Deep learning in agriculture: a survey. Comput. Electron. Agric. 147:70–90
    [Google Scholar]
  60. Kang R, Park B, Ouyang Q, Ren N. 2021. Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms. Food Control 130:108379
    [Google Scholar]
  61. Kondakci T, Zhou W. 2016. Recent applications of advanced control techniques in food industry. Food Bioprocess Technol. 10:3522–42
    [Google Scholar]
  62. Kuehn BM. 2014. Agencies use social media to track foodborne illness. JAMA 312:2117–18
    [Google Scholar]
  63. Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D. 2018. Machine learning in agriculture: a review. Sensors 18:82674
    [Google Scholar]
  64. Loisel J, Duret S, Cornuéjols A, Cagnon D, Tardet M et al. 2021. Cold chain break detection and analysis: Can machine learning help?. Trends Food Sci. Technol. 112:391–99
    [Google Scholar]
  65. Lupolova N, Dallman TJ, Holden NJ, Gally DL. 2017. Patchy promiscuity: machine learning applied to predict the host specificity of Salmonella enterica and Escherichia coli. Microb. Genom. 3:10e000135
    [Google Scholar]
  66. Lupolova N, Lycett SJ, Gally DL. 2019. A guide to machine learning for bacterial host attribution using genome sequence data. Microb. Genom. 5:12e000317
    [Google Scholar]
  67. Maharana A, Cai K, Hellerstein J, Hswen Y, Munsell M et al. 2019. Detecting reports of unsafe foods in consumer product reviews. JAMIA Open 2:3330–38
    [Google Scholar]
  68. Martos V, Ahmad A, Cartujo P, Ordoñez J. 2021. Ensuring agricultural sustainability through remote sensing in the era of agriculture 5.0. Appl. Sci. 11:135911
    [Google Scholar]
  69. Mavani NR, Ali JM, Othman S, Hussain MA, Hashim H, Rahman NA 2021. Application of artificial intelligence in food industry—a guideline. Food Eng. Rev. 14:1134–75
    [Google Scholar]
  70. Mei X, Lee HC, Diao K-Y, Huang M, Lin B et al. 2020. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat. Med. 26:81224–28
    [Google Scholar]
  71. Mercier S, Villeneuve S, Mondor M, Uysal I. 2017. Time-temperature management along the food cold chain: a review of recent developments. Compr. Rev. Food Sci. Food Saf. 16:4647–67
    [Google Scholar]
  72. Mikkelä A, Ranta J, Tuominen P. 2019. A modular Bayesian Salmonella source attribution model for sparse data. Risk Anal. 39:81796–811
    [Google Scholar]
  73. Misra NN, Dixit Y, Al-Mallahi A, Bhullar MS, Upadhyay R, Martynenko A. 2020. IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet Things J. 9:96305–24
    [Google Scholar]
  74. Munck N, Njage PMK, Leekitcharoenphon P, Litrup E, Hald T. 2020. Application of whole-genome sequences and machine learning in source attribution of Salmonella Typhimurium. Risk Anal. 40:91693–705
    [Google Scholar]
  75. Murphy SI, Reichler SJ, Martin NH, Boor KJ, Wiedmann M. 2021. Machine learning and advanced statistical modeling can identify key quality management practices that affect postpasteurization contamination of fluid milk. J. Food Prot. 84:91496–511
    [Google Scholar]
  76. Nasirahmadi A, Hensel O. 2022. Toward the next generation of digitalization in agriculture based on digital twin paradigm. Sensors 22:2498
    [Google Scholar]
  77. Ndraha N, Hsiao HI, Hsieh YZ, Pradhan AK. 2021. Predictive models for the effect of environmental factors on the abundance of Vibrio parahaemolyticus in oyster farms in Taiwan using extreme gradient boosting. Food Control 130:108353
    [Google Scholar]
  78. Nguyen M, Wesley Long S, McDermott PF, Olsen RJ, Olson R et al. 2019. Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. J. Clin. Microbiol. 57:2e01260-18
    [Google Scholar]
  79. Nturambirwe JFI, Opara UL. 2020. Machine learning applications to non-destructive defect detection in horticultural products. Biosyst. Eng. 189:60–83
    [Google Scholar]
  80. Oldroyd RA, Morris MA, Birkin M. 2021. Predicting food safety compliance for informed food outlet inspections: a machine learning approach. Int. J. Environ. Res. Public Health 18:2312635
    [Google Scholar]
  81. Onoufriou G, Bickerton R, Pearson S, Leontidis G. 2019. Nemesyst: a hybrid parallelism deep learning-based framework applied for internet of things enabled food retailing refrigeration systems. Comput. Ind. 113:103133
    [Google Scholar]
  82. Park S, Navratil S, Gregory A, Bauer A, Srinath I et al. 2014. Farm management, environment, and weather factors jointly affect the probability of spinach contamination by generic Escherichia coli at the preharvest stage. Appl. Environ. Microbiol. 80:82504–15
    [Google Scholar]
  83. Pataki , Matamoros S, van der Putten BCL, Remondini D, Giampieri E et al. 2020. Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning. Sci. Rep. 10:15026
    [Google Scholar]
  84. Pettengill JB, Beal J, Balkey M, Allard M, Rand H, Timme R. 2021. Interpretative labor and the bane of nonstandardized metadata in public health surveillance and food safety. Clin. Infect. Dis. 73:81537–39
    [Google Scholar]
  85. Pires SM, Evers EG, Van Pelt W, Ayers T, Scallan E et al. 2009. Attributing the human disease burden of foodborne infections to specific sources. Foodborne Pathog. Dis. 6:4417–24
    [Google Scholar]
  86. Polat H, Topalcengiz Z, Danyluk MD. 2020. Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches. J. Food Saf. 40:1e12733
    [Google Scholar]
  87. Pouillot R, Klontz KC, Chen Y, Burall LS, Macarisin D et al. 2016. Infectious dose of Listeria monocytogenes in outbreak linked to ice cream, United States, 2015. Emerg. Infect. Dis. 22:122113–19
    [Google Scholar]
  88. Rajan K, Saffiotti A. 2017. Towards a science of integrated AI and robotics. Artif. Intell. 247:1–9
    [Google Scholar]
  89. Ranjan AA, Rai A, Haque S, Pradeep K, Kushwaha BPL. 2019. An approach for Netflix recommendation system using singular value decomposition. J. Comput. Math. Sci. 10:4774–79
    [Google Scholar]
  90. Ranta J, Matjushin D, Virtanen T, Kuusi M, Viljugrein H et al. 2011. Bayesian temporal source attribution of foodborne zoonoses: Campylobacter in Finland and Norway. Risk Anal. 31:71156–71
    [Google Scholar]
  91. Rieke N, Hancox J, Li W, Milletarì F, Roth HR et al. 2020. The future of digital health with federated learning. npj Digit. Med. 3:119
    [Google Scholar]
  92. Ropodi AI, Panagou EZ, Nychas GJE. 2016. Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines. Trends Food Sci. Technol. 50:11–25
    [Google Scholar]
  93. Sadilek A, Caty S, DiPrete L, Mansour R, Schenk T et al. 2018. Machine-learned epidemiology: real-time detection of foodborne illness at scale. npj Digit. Med. 1:36
    [Google Scholar]
  94. Sadilek A, Kautz H, Di Prete L, Labus B, Portman E et al. 2017. Deploying nemesis: preventing foodborne illness by data mining social media. AI Mag. 38:137–48
    [Google Scholar]
  95. Sen R, Nayak L, De RK. 2016. A review on host-pathogen interactions: classification and prediction. Eur. J. Clin. Microbiol. Infect. Dis. 35:101581–99
    [Google Scholar]
  96. Sharma R, Kamble SS, Gunasekaran A, Kumar V, Kumar A. 2020. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Oper. Res. 119:104926
    [Google Scholar]
  97. Sil S, Mukherjee R, Kumbhar D, Reghu D, Shrungar D et al. 2021. Raman spectroscopy and artificial intelligence open up accurate detection of pathogens from DNA-based sub-species level classification. J. Raman Spectrosc. 52:122648–59
    [Google Scholar]
  98. Simeone A, Woolley E, Escrig J, Watson NJ. 2020. Intelligent industrial cleaning: a multi-sensor approach utilising machine learning-based regression. Sensors 20:133642
    [Google Scholar]
  99. Singhal N, Kumar M, Kanaujia PK, Virdi JS. 2015. MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis. Front. Microbiol. 6:791
    [Google Scholar]
  100. Sinwar D, Dhaka VS, Sharma MK, Rani G 2020. AI-based yield prediction and smart irrigation. Internet of Things and Analytics for Agriculture, Vol. 2 PK Pattnaik, R Kumar, S Pal 155–80. Singapore: Springer
    [Google Scholar]
  101. Smith MJ. 2019. Getting value from artificial intelligence in agriculture. Anim. Prod. Sci. 60:146–54
    [Google Scholar]
  102. Song B, Yan W, Zhang T 2019. Cross-border e-commerce commodity risk assessment using text mining and fuzzy rule-based reasoning. Adv. Eng. Inform. 40:69–80
    [Google Scholar]
  103. Stocker MD, Pachepsky YA, Hill RL. 2022. Prediction of E. coli concentrations in agricultural pond waters: application and comparison of machine learning algorithms. Front. Artif. Intell. 4:768650
    [Google Scholar]
  104. Strawn LK, Fortes ED, Bihn EA, Nightingale KK, Gröhn YT et al. 2013. Landscape and meteorological factors affecting prevalence of three food-borne pathogens in fruit and vegetable farms. Appl. Environ. Microbiol. 79:2588–600
    [Google Scholar]
  105. Sullivan G, Zoellner C, Wiedmann M, Ivanek R. 2021. In silico models for design and optimization of science-based Listeria environmental monitoring programs in fresh-cut produce facilities. Appl. Environ. Microbiol. 87:21e0079921
    [Google Scholar]
  106. Tack DM, Ray L, Griffin PM, Cieslak PR, Dunn J et al. 2020. Preliminary incidence and trends of infections with pathogens transmitted commonly through food—foodborne diseases active surveillance network, 10 U.S. Sites, 2016–2019. MMWR 69:17509–14
    [Google Scholar]
  107. Teyhouee A, McPhee-Knowles S, Waldner C, Osgood N. 2017. Prospective detection of foodborne illness outbreaks using machine learning approaches. Lect. Notes Comput. Sci. 10354:302–8
    [Google Scholar]
  108. Thépault A, Méric G, Rivoal K, Pascoe B, Mageiros L et al. 2017. Genome-wide identification of host-segregating epidemiological markers for source attribution in Campylobacter jejuni. Appl. Environ. Microbiol. 83:7e03085-16
    [Google Scholar]
  109. Toro M, Weller D, Ramos R, Diaz L, Alvarez FP et al. 2022. Environmental and anthropogenic factors associated with the likelihood of detecting Salmonella in agricultural watersheds. Environ. Pollut. 306:119298
    [Google Scholar]
  110. Tyson GH, Tate HP, Abbott J, Tran TT, Kabera C et al. 2016. Molecular subtyping and source attribution of Campylobacter isolated from food animals. J. Food Prot. 79:111891–97
    [Google Scholar]
  111. Úbeda MA, Hussein WB, Hussein MA, Hinrichs J, Becker TM. 2016. Acoustic sensing and signal processing techniques for monitoring milk fouling cleaning operations. Eng. Life Sci. 16:167–77
    [Google Scholar]
  112. Vangay P, Steingrimsson J, Wiedmann M, Stasiewicz MJ. 2014. Classification of Listeria monocytogenes persistence in retail delicatessen environments using expert elicitation and machine learning. Risk Anal. 34:101830–45
    [Google Scholar]
  113. Wallhäußer E, Hussein WB, Hussein MA, Hinrichs J, Becker TM. 2011. On the usage of acoustic properties combined with an artificial neural network: a new approach of determining presence of dairy fouling. J. Food Eng. 103:4449–56
    [Google Scholar]
  114. Wallhäußer E, Hussein WB, Hussein MA, Hinrichs J, Becker T. 2013. Detection of dairy fouling: combining ultrasonic measurements and classification methods. Eng. Life Sci. 13:3292–301
    [Google Scholar]
  115. Wang H, Ceylan Koydemir H, Qiu Y, Bai B, Zhang Y et al. 2020. Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light Sci. Appl. 9:118
    [Google Scholar]
  116. Wang H, Cui W, Guo Y, Du Y, Zhou Y. 2021. Machine learning prediction of foodborne disease pathogens: algorithm development and validation study. JMIR Med. Inform. 9:1e24924
    [Google Scholar]
  117. Wang J, Yue H, Zhou Z. 2017. An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network. Food Control 79:363–70
    [Google Scholar]
  118. Wang L, Ting JSL, Ip WH. 2013. Design of Supply-chain Pedigree Interactive Dynamic Explore (SPIDER) for food safety and implementation of Hazard Analysis and Critical Control Points (HACCPs). Comput. Electron. Agric. 90:14–23
    [Google Scholar]
  119. Weis CV, Jutzeler CR, Borgwardt K. 2020. Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review. Clin. Microbiol. Infect. 26:101310–17
    [Google Scholar]
  120. Weller D, Brassill N, Rock C, Ivanek R, Mudrak E et al. 2020a. Complex interactions between weather, and microbial and physicochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water. Front. Microbiol. 11:134
    [Google Scholar]
  121. Weller DL, Love TMT, Belias A, Wiedmann M. 2020b. Predictive models may complement or provide an alternative to existing strategies for assessing the enteric pathogen contamination status of northeastern streams used to provide water for produce production. Front. Sustain. Food Syst. 4:561517
    [Google Scholar]
  122. Weller DL, Love TMT, Wiedmann M. 2021a. Comparison of resampling algorithms to address class imbalance when developing machine learning models to predict foodborne pathogen presence in agricultural water. Front. Environ. Sci. 9:701288
    [Google Scholar]
  123. Weller DL, Love TMT, Wiedmann M. 2021b. Interpretability versus accuracy: a comparison of machine learning models built using different algorithms, performance measures, and features to predict E. coli levels in agricultural water. Front. Artif. Intell. 4:628441
    [Google Scholar]
  124. WHO (World Health Organ.) 2015. WHO Estimates of the Global Burden of Foodborne Diseases. Geneva: WHO
  125. WHO (World Health Organ.) 2021. WHO steps up action to improve food safety and protect people from disease. WHO https://www.who.int/news/item/07-06-2021-who-steps-up-action-to-improve-food-safety-and-protect-people-from-disease
    [Google Scholar]
  126. Yang C-C, Jun W, Kim MS, Chao K, Kang S et al. 2010. Classification of fecal contamination on leafy greens by hyperspectral imaging. Sens. Agric. Food Qual. Saf. II. https://doi.org/10.1117/12.851069
    [Google Scholar]
  127. Yang J, Jensen BBB, Nordkvist M, Rasmussen P, Pedersen B et al. 2018. Anomaly analysis in cleaning-in-place operations of an industrial brewery fermenter. Ind. Eng. Chem. Res. 57:3812871–83
    [Google Scholar]
  128. Yang M, Liu X, Luo Y, Pearlstein AJ, Wang S et al. 2021. Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food. Nat. Food 2:2110–17
    [Google Scholar]
  129. Zhang P, Cui W, Wang H, Du Y, Zhou Y. 2021a. High-efficiency machine learning method for identifying foodborne disease outbreaks and confounding factors. Foodborne Pathog. Dis. 18:8590–98
    [Google Scholar]
  130. Zhang S, Li S, Gu W, Den Bakker H, Boxrud D et al. 2019. Zoonotic source attribution of Salmonella enterica serotype Typhimurium using genomic surveillance data, United States. Emerg. Infect. Dis. 25:182–91
    [Google Scholar]
  131. Zhang W, Valencia A, Chang N-B. 2021b. Synergistic integration between machine learning and agent-based modeling: a multidisciplinary review. IEEE Trans. Neural Netw. Learn. Syst. In press
    [Google Scholar]
  132. Zhou L, Zhang C, Liu F, Qiu Z, He Y. 2019. Application of deep learning in food: a review. Compr. Rev. Food Sci. Food Saf. 18:61793–811
    [Google Scholar]
  133. Zieliński B, Plichta A, Misztal K, Spurek P, Brzychczy-Włoch M, Ochońska D. 2017. Deep learning approach to bacterial colony classification. PLOS ONE 12:9e0184554
    [Google Scholar]
  134. Zoellner C, Jennings R, Wiedmann M, Ivanek R. 2019. EnABLe: an agent-based model to understand Listeria dynamics in food processing facilities. Sci. Rep. 9:495
    [Google Scholar]
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