Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning

Abdollahzadeh, 2017, Predictive modeling of survival/death of Listeria monocytogenes in liquid media: bacterial responses to cinnamon essential oil, ZnO nanoparticles, and strain, Food Control, 73, 954, 10.1016/j.foodcont.2016.10.014 Amina, 2010, Wavelet neural networks for modelling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk, Chemom. Intell. Lab. Syst., 103, 170, 10.1016/j.chemolab.2010.07.004 Amina, 2012, Identification of the Listeria monocytogenes survival curves in UHT whole milk utilising local linear wavelet neural networks, Expert Syst. Appl., 39, 1435, 10.1016/j.eswa.2011.08.028 Ashino, 2019, Predicting the decision making chemicals used for bacterial growth, Sci. Rep., 9, 1, 10.1038/s41598-019-43587-8 Aspridou, 2015, Individual cell heterogeneity as variability source in population dynamics of microbial inactivation, Food Microbiol., 45, 216, 10.1016/j.fm.2014.04.008 Baranyi, 1994, A dynamic approach to predicting bacterial growth in food, Int. J. Food Microbiol., 23, 277, 10.1016/0168-1605(94)90157-0 Basheer, 2000, Artificial neural networks: fundamentals, computing, design, and application, J. Microbiol. Methods, 43, 3, 10.1016/S0167-7012(00)00201-3 Breiman, 2001, Random forests, Mach. Learn., 5–32 Buchanan, 1993, Predictive food microbiology, Trends Food Sci. Technol., 4, 6, 10.1016/S0924-2244(05)80004-4 Buchanan, 1997, When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves, Food Microbiol., 14, 313, 10.1006/fmic.1997.0125 Chitra, 2020, Application of deep neural techniques in predictive modelling for the estimation of Escherichia coli growth rate, J. Appl. Microbiol., 1–11 Coleman, 1999, Qualitative and quantitative risk assessment, Food Control, 10, 289, 10.1016/S0956-7135(99)00052-3 Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018 Deringer, 2021, Gaussian process regression for materials and molecules, Chem. Rev., 121, 10073, 10.1021/acs.chemrev.1c00022 European Commission, 2002 Fakruddin, 2012, Predictive microbiology: Modeling microbial responses in food, Ceylon J. Sci. (Biol. Sci.), 40, 121, 10.4038/cjsbs.v40i2.3928 Fernández-Navarro, 2010, Development of a multi-classification neural network model to determine the microbial growth/no growth interface, Int. J. Food Microbiol., 141, 203, 10.1016/j.ijfoodmicro.2010.05.013 Geurts, 2006, Extremely randomized trees, Mach. Learn., 63, 3, 10.1007/s10994-006-6226-1 Gosukonda, 2015, Application of artificial neural network to predict Escherichia coli O157: H7 inactivation on beef surfaces, Food Control, 47, 606, 10.1016/j.foodcont.2014.08.002 Gu, 2015, Use of random forest to estimate population attributable fractions from a case-control study of Salmonella enterica serotype Enteritidis infections, Epidemiol. Infect., 143, 2786, 10.1017/S095026881500014X Hajmeer, 1997, Computational neural networks for predictive microbiology II. Application to microbial growth, Int. J. Food Microbiol., 34, 51, 10.1016/S0168-1605(96)01169-5 Hassani, 2019, A support vector machine based approach for predicting the risk of freshwater disease emergence in England, Stats, 2, 89, 10.3390/stats2010007 Hiura, 2021, Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database, Sci. Rep., 11, 10613, 10.1038/s41598-021-90164-z Hochachka, 2007, Data-mining discovery of pattern and process in ecological systems, J. Wildl. Manag., 71, 2427, 10.2193/2006-503 Jason, 1983, A deterministic model for monophasic growth of batch cultures of bacteria, Antonie Van Leeuwenhoek, 49, 513, 10.1007/BF00399845 Jeyamkondan, 2001, Microbial growth modelling with artificial neural networks, Int. J. Food Microbiol., 64, 343, 10.1016/S0168-1605(00)00483-9 Keeratipibul, 2011, Prediction of coliforms and Escherichia coli on tomato fruits and lettuce leaves after sanitizing by using artificial neural networks, LWT Food Sci. Technol., 44, 130, 10.1016/j.lwt.2010.05.015 Koutsoumanis, 2013, Stochasticity in colonial growth dynamics of individual bacterial cells, Appl. Environ. Microbiol., 79, 2294, 10.1128/AEM.03629-12 Koyama, 2016, Do bacterial cell numbers follow a theoretical Poisson distribution? Comparison of experimentally obtained numbers of single cells with random number generation via computer simulation, Food Microbiol., 60, 49, 10.1016/j.fm.2016.05.019 Koyama, 2019, Calculating stochastic inactivation of individual cells in a bacterial population using variability in individual cell inactivation time and initial cell number, J. Theor. Biol., 469, 172, 10.1016/j.jtbi.2019.01.042 Koyama, 2021, Application of growth rate from kinetic model to calculate stochastic growth of a bacteria population at low contamination level, J. Theor. Biol., 525, 10.1016/j.jtbi.2021.110758 Kuroda, 2019, Modeling growth limits of Bacillus spp. spores by using deep-learning algorithm, Food Microbiol., 78, 38, 10.1016/j.fm.2018.09.013 Liakos, 2018, Machine learning in agriculture: a review, Sensors (Switzerland)., 10.3390/s18082674 McKellar, 2003 McMeekin, 2002, Predictive microbiology: providing a knowledge-based framework for change management, Int. J. Food Microbiol., 78, 133, 10.1016/S0168-1605(02)00231-3 Nauta, 2000, Separation of uncertainty and variability in quantitative microbial risk assessment models, Int. J. Food Microbiol., 57, 9, 10.1016/S0168-1605(00)00225-7 Nauta, 2001 Oscar, 2009, General regression neural network and Monte Carlo simulation model for survival and growth of Salmonella on raw chicken skin as a function of serotype, temperature, and time for use in risk assessment, J. Food Prot., 72, 2078, 10.4315/0362-028X-72.10.2078 Oscar, 2011, Development and validation of a predictive microbiology model for survival and growth of Salmonella on chicken stored at 4 to 12 °C, J. Food Prot., 74, 279, 10.4315/0362-028X.JFP-10-314 Oscar, 2014, General regression neural network model for behavior of Salmonella on chicken meat during cold storage, J. Food Sci., 79, M978, 10.1111/1750-3841.12435 Oscar, 2015, Neural network model for survival and growth of Salmonella enterica serotype 8,20:-:z6 in ground chicken thigh meat during cold storage: extrapolation to other serotypes, J. Food Prot., 78, 1819, 10.4315/0362-028X.JFP-15-093 Oscar, 2017, Neural network models for growth of Salmonella serotypes in ground chicken subjected to temperature abuse during cold storage for application in HACCP and risk assessment, Int. J. Food Sci. Technol., 52, 214, 10.1111/ijfs.13242 Ozturk, 2012, Application of non-linear models to predict inhibition effects of various plant hydrosols on Listeria monocytogenes inoculated on fresh-cut apples, Foodborne Pathog. Dis., 9, 607, 10.1089/fpd.2012.1138 Pedregosa, 2012, 12, 2825 Peleg, 2006 Quiñonero-Candela, 2005, A unifying view of sparse approximate Gaussian process regression, J. Mach. Learn., 6, 1939 Ratkowsky, 1982, Relationship between temperature and growth rate of bacterial cultures, J. Bacteriol., 149, 1, 10.1128/jb.149.1.1-5.1982 Renshaw, 1991 Robinson, 2001, The effect of inoculum size on the lag phase of Listeria monocytogenes, Int. J. Food Microbiol., 70, 163, 10.1016/S0168-1605(01)00541-4 Ross, 1994, Predictive microbiology, Int. J. Food Microbiol., 23, 241, 10.1016/0168-1605(94)90155-4 Ru, 2017, Machine learning techniques applied in risk assessment related to food safety, EFSA Support. Publ., 14 Tonner, 2017, Detecting differential growth of microbial populations with Gaussian process regression, Genome Res., 27, 320, 10.1101/gr.210286.116 Tsuruma, 2021, How many repetitions per condition are required for developing a stable growth/no growth boundary model for Bacillus simplex spores?, Food Control, 122, 10.1016/j.foodcont.2020.107756 Van Gerwen, 1998, Growth and inactivation models to be used in quantitative risk assessments, J. Food Prot., 61, 1541, 10.4315/0362-028X-61.11.1541 Wijtzes, 1995, Modelling bacterial growth of Lactobacillus curvatus as a function of acidity and temperature, Appl. Environ. Microbiol., 61, 2533, 10.1128/aem.61.7.2533-2539.1995