{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T09:28:03Z","timestamp":1793525283418,"version":"4.5.0"},"reference-count":48,"publisher":"Elsevier BV","issue":"11","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:00:00Z","timestamp":1789862400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Food Protection"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.jfp.2026.100929","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T17:23:02Z","timestamp":1790097782000},"page":"100929","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Food Safety Problems Among Catering Service Operators Using Longitudinal Regulatory Data"],"prefix":"10.1016","volume":"89","author":[{"given":"Wen-Hui","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Qi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi-Wen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shao-Jie","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jfp.2026.100929_b0005","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2623\u20132631). Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3292500.3330701.","DOI":"10.1145\/3292500.3330701"},{"issue":"2","key":"10.1016\/j.jfp.2026.100929_b0010","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1177\/1094428104263672","article-title":"Using generalized estimating equations for longitudinal data analysis","volume":"7","author":"Ballinger","year":"2004","journal-title":"Organizational Research Methods"},{"issue":"1","key":"10.1016\/j.jfp.2026.100929_b0015","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"Journal of the Royal Statistical Society: Series B"},{"key":"10.1016\/j.jfp.2026.100929_b0020","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","volume":"24","author":"Bergstra","year":"2011","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.1016\/j.jfp.2026.100929_b0025","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"issue":"11","key":"10.1016\/j.jfp.2026.100929_b0030","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.3390\/foods15111864","article-title":"Temporal screening of high-risk food service inspections in New York State, 2023-2025: A case study using multimodal evidential learning","volume":"15","author":"Cai","year":"2026","journal-title":"Foods"},{"key":"10.1016\/j.jfp.2026.100929_b0035","doi-asserted-by":"crossref","unstructured":"Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785\u2013794). Association for Computing Machinery. https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"issue":"16","key":"10.1016\/j.jfp.2026.100929_b0040","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.3390\/foods12163113","article-title":"Prediction and visual analysis of food safety risk based on TabNet-GRA","volume":"12","author":"Chen","year":"2023","journal-title":"Foods"},{"key":"10.1016\/j.jfp.2026.100929_b0045","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000). Ensemble methods in machine learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (Lecture Notes in Computer Science, Vol. 1857, pp. 1\u201315). Springer. https:\/\/doi.org\/10.1007\/3-540-45014-9_1.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"10.1016\/j.jfp.2026.100929_b0050","doi-asserted-by":"crossref","first-page":"21650","DOI":"10.1038\/s41598-022-25817-8","article-title":"Leveraging machine learning to assess market-level food safety and zoonotic disease risks in China","volume":"12","author":"Gao","year":"2022","journal-title":"Scientific Reports"},{"issue":"5","key":"10.1016\/j.jfp.2026.100929_b0055","doi-asserted-by":"crossref","first-page":"437","DOI":"10.3233\/IDA-2007-11502","article-title":"New probabilistic interest measures for association rules","volume":"11","author":"Hahsler","year":"2007","journal-title":"Intelligent Data Analysis"},{"issue":"32","key":"10.1016\/j.jfp.2026.100929_b0060","first-page":"681","article-title":"Health department use of social media to identify foodborne illness - Chicago, Illinois, 2013-2014","volume":"63","author":"Harris","year":"2014","journal-title":"Morbidity and Mortality Weekly Report"},{"issue":"20","key":"10.1016\/j.jfp.2026.100929_b0065","first-page":"441","article-title":"Using online reviews by restaurant patrons to identify unreported cases of foodborne illness - New York City, 2012\u20132013","volume":"63","author":"Harrison","year":"2014","journal-title":"Morbidity and Mortality Weekly Report"},{"issue":"12","key":"10.1016\/j.jfp.2026.100929_b0070","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1001923","article-title":"World Health Organization global estimates and regional comparisons of the burden of foodborne disease in 2010","volume":"12","author":"Havelaar","year":"2015","journal-title":"PLOS Medicine"},{"issue":"3","key":"10.1016\/j.jfp.2026.100929_b0075","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1002\/bimj.201700067","article-title":"Variable selection - a review and recommendations for the practicing statistician","volume":"60","author":"Heinze","year":"2018","journal-title":"Biometrical Journal"},{"issue":"1","key":"10.1016\/j.jfp.2026.100929_b0080","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1086\/697682","article-title":"A tale of repetition: Lessons from Florida restaurant inspections","volume":"61","author":"Jin","year":"2018","journal-title":"Journal of Law and Economics"},{"key":"10.1016\/j.jfp.2026.100929_b0085","doi-asserted-by":"crossref","unstructured":"Kang, J.S., Kuznetsova, P., Luca, M., & Choi, Y. (2013). Where not to eat? Improving public policy by predicting hygiene inspections using online reviews. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1443\u20131448). Association for Computational Linguistics. https:\/\/aclanthology.org\/D13-1150\/.","DOI":"10.18653\/v1\/D13-1150"},{"key":"10.1016\/j.jfp.2026.100929_b0090","unstructured":"Kannan, V., Shapiro, M.A., & Bilgic, M. (2019). Hindsight analysis of the Chicago food inspection forecasting model. In Proceedings of AAAI FSS-19: Artificial Intelligence in Government and Public Sector. https:\/\/arxiv.org\/abs\/1910.04906."},{"key":"10.1016\/j.jfp.2026.100929_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodcont.2020.107775","article-title":"Efficacy of disclosed food safety inspections in restaurants","volume":"123","author":"Kaskela","year":"2021","journal-title":"Food Control"},{"key":"10.1016\/j.jfp.2026.100929_b0100","first-page":"3146","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"2","key":"10.1016\/j.jfp.2026.100929_b0105","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1089\/fpd.2023.0086","article-title":"Foodborne illness complaint systems detect, and restaurant inspection programs prevent restaurant-associated foodborne illness outbreaks","volume":"21","author":"Kim","year":"2024","journal-title":"Foodborne Pathogens and Disease"},{"issue":"648","key":"10.1016\/j.jfp.2026.100929_b0110","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.abk0855","article-title":"Risk assessment with gut microbiome and metabolite markers in NAFLD development","volume":"14","author":"Leung","year":"2022","journal-title":"Science Translational Medicine"},{"key":"10.1016\/j.jfp.2026.100929_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodcont.2020.107359","article-title":"Surveillance of foodborne disease outbreaks in China, 2003-2017","volume":"118","author":"Li","year":"2020","journal-title":"Food Control"},{"issue":"1","key":"10.1016\/j.jfp.2026.100929_b0120","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1093\/biomet\/73.1.13","article-title":"Longitudinal data analysis using generalized linear models","volume":"73","author":"Liang","year":"1986","journal-title":"Biometrika"},{"issue":"8","key":"10.1016\/j.jfp.2026.100929_b0125","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1089\/fpd.2020.2919","article-title":"Microrisk Lab: An online freeware for predictive microbiology","volume":"18","author":"Liu","year":"2021","journal-title":"Foodborne Pathogens and Disease"},{"issue":"1","key":"10.1016\/j.jfp.2026.100929_b0130","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nature Machine Intelligence"},{"key":"10.1016\/j.jfp.2026.100929_b0135","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"4","key":"10.1016\/j.jfp.2026.100929_b0140","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1287\/isre.2019.0866","article-title":"A for effort? Using the crowd to identify moral hazard in New York City restaurant hygiene inspections","volume":"30","author":"Mejia","year":"2019","journal-title":"Information Systems Research"},{"issue":"11","key":"10.1016\/j.jfp.2026.100929_b0145","doi-asserted-by":"crossref","first-page":"4780","DOI":"10.1111\/1750-3841.16334","article-title":"Food safety risk-assessment systems utilized by China, Australia\/New Zealand, Canada, and the United States","volume":"87","author":"Ng","year":"2022","journal-title":"Journal of Food Science"},{"issue":"1","key":"10.1016\/j.jfp.2026.100929_b0150","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40168-024-01990-y","article-title":"Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease","volume":"13","author":"Nychas","year":"2025","journal-title":"Microbiome"},{"key":"10.1016\/j.jfp.2026.100929_b0155","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1146\/annurev-biodatasci-020221-123602","article-title":"Data science in the food industry","volume":"4","author":"Nychas","year":"2021","journal-title":"Annual Review of Biomedical Data Science"},{"issue":"23","key":"10.1016\/j.jfp.2026.100929_b0160","doi-asserted-by":"crossref","first-page":"12635","DOI":"10.3390\/ijerph182312635","article-title":"Predicting food safety compliance for informed food outlet inspections: A machine learning approach","volume":"18","author":"Oldroyd","year":"2021","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"24","key":"10.1016\/j.jfp.2026.100929_b0165","doi-asserted-by":"crossref","first-page":"4078","DOI":"10.3390\/foods13244078","article-title":"A comprehensive review of food safety culture in the food industry: Leadership, organizational commitment, and multicultural dynamics","volume":"13","author":"Pai","year":"2024","journal-title":"Foods"},{"issue":"10","key":"10.1016\/j.jfp.2026.100929_b0170","first-page":"24","article-title":"Recurrent critical violations of the food code in retail food service establishments","volume":"68","author":"Phillips","year":"2006","journal-title":"Journal of Environmental Health"},{"issue":"6","key":"10.1016\/j.jfp.2026.100929_b0175","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.foodcont.2010.12.009","article-title":"Enhancing food safety culture to reduce rates of foodborne illness","volume":"22","author":"Powell","year":"2011","journal-title":"Food Control"},{"key":"10.1016\/j.jfp.2026.100929_b0180","first-page":"6638","article-title":"CatBoost: Unbiased boosting with categorical features","volume":"31","author":"Prokhorenkova","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.jfp.2026.100929_b0185","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","year":"2020","journal-title":"BMJ"},{"issue":"7","key":"10.1016\/j.jfp.2026.100929_b0190","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1002\/sim.7992","article-title":"Minimum sample size for developing a multivariable prediction model: Part II - binary and time-to-event outcomes","volume":"38","author":"Riley","year":"2019","journal-title":"Statistics in Medicine"},{"issue":"8","key":"10.1016\/j.jfp.2026.100929_b0195","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"issue":"2","key":"10.1016\/j.jfp.2026.100929_b0200","doi-asserted-by":"crossref","first-page":"223","DOI":"10.3390\/foods15020223","article-title":"Explainable transformer-based modelling for pathogen-oriented food safety inspection grade prediction using New York State open data","volume":"15","author":"Sari","year":"2026","journal-title":"Foods"},{"issue":"3","key":"10.1016\/j.jfp.2026.100929_b0205","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0152117","article-title":"Supplementing public health inspection via social media","volume":"11","author":"Schomberg","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.jfp.2026.100929_b0210","doi-asserted-by":"crossref","unstructured":"Singh, S., Shah, B., Kanich, C., & Kash, I.A. (2022). Fair decision-making for food inspections. In Equity and access in algorithms, mechanisms, and optimization, EAAMO 2022 (pp. 5:1\u20135:11). ACM. https:\/\/doi.org\/10.1145\/3551624.3555289.","DOI":"10.1145\/3551624.3555289"},{"key":"10.1016\/j.jfp.2026.100929_b0215","unstructured":"State Administration for Market Regulation of China (2021). Measures for the Administration of Supervision and Inspection of Food Production and Operation (SAMR Order No. 49). https:\/\/www.samr.gov.cn\/zw\/zfxxgk\/fdzdgknr\/fgs\/art\/2023\/art_2c192a66cf4f46218d3356e63522e4d4.html."},{"issue":"12","key":"10.1016\/j.jfp.2026.100929_b0220","doi-asserted-by":"crossref","first-page":"5078","DOI":"10.1111\/1750-3841.15978","article-title":"Methods to perform risk-based inspections of food companies","volume":"86","author":"van Asselt","year":"2021","journal-title":"Journal of Food Science"},{"key":"10.1016\/j.jfp.2026.100929_b0225","unstructured":"World Health Organization Regional Office for the Western Pacific (2024). Risk-based food inspection system: practical guidance for national authorities. WHO Regional Office for the Western Pacific. https:\/\/iris.who.int\/handle\/10665\/375910."},{"key":"10.1016\/j.jfp.2026.100929_b0230","doi-asserted-by":"crossref","unstructured":"Zadrozny, B., & Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 694\u2013699). Association for Computing Machinery. https:\/\/doi.org\/10.1145\/775047.775151.","DOI":"10.1145\/775047.775151"},{"issue":"5","key":"10.1016\/j.jfp.2026.100929_b0235","doi-asserted-by":"crossref","DOI":"10.1111\/1541-4337.13403","article-title":"Risk factors affecting the food safety risk in food business operations for risk-based inspection: A systematic review","volume":"23","author":"Ze","year":"2024","journal-title":"Comprehensive Reviews in Food Science and Food Safety"},{"issue":"23","key":"10.1016\/j.jfp.2026.100929_b0240","doi-asserted-by":"crossref","first-page":"4005","DOI":"10.3390\/foods14234005","article-title":"Application of machine learning in food safety risk assessment","volume":"14","author":"Zhang","year":"2025","journal-title":"Foods"}],"container-title":["Journal of Food Protection"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0362028X26002346?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0362028X26002346?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T08:32:50Z","timestamp":1793521970000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0362028X26002346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":48,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["S0362028X26002346"],"URL":"https:\/\/doi.org\/10.1016\/j.jfp.2026.100929","relation":{},"ISSN":["0362-028X"],"issn-type":[{"value":"0362-028X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting Food Safety Problems Among Catering Service Operators Using Longitudinal Regulatory Data","name":"articletitle","label":"Article Title"},{"value":"Journal of Food Protection","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jfp.2026.100929","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Inc. on behalf of International Association for Food Protection.","name":"copyright","label":"Copyright"}],"article-number":"100929"}}