Aguilar-Toala JE, Deering AJ and Liceaga AM 2020 New insights into the antimicrobial properties of hydrolysates and peptide fractions derived from chia seed (Salvia hispanica L.). Probiotics Antimicrob. Proteins 12 1571–1581
Allende A, Bover-Cid S and Fernández PS 2022 Challenges and opportunities related to the use of innovative modelling approaches and tools for microbiological food safety management. Curr. Opin. Food Sci. 45 100839
Alshannaq A and Yu JH 2017 Occurrence, toxicity, and analysis of major mycotoxins in food. Int. J. Environ. Res. Public Health 14 632
PubMed PubMed Central Google Scholar
Banerji R, Karkee A, Kanojiya P, et al. 2021 Pore-forming toxins of foodborne pathogens. Compr. Rev. Food Sci. Food Saf. 20 2265–2285
Baranyi J 1998 Comparison of stochastic and deterministic concepts of bacterial lag. J. Theor. Biol. 192 403–408
Baranyi J and Roberts TA 1994 A dynamic approach to predicting bacterial growth in food. Int. J. Food Microbiol. 23 277–294
Baranyi J, Roberts T and Mcclure P 1993 A non-autonomous differential equation to model bacterial growth. Food Microbiol. 10 43–59
Bassolé IHN and Juliani HR 2012 Essential oils in combination and their antimicrobial properties. Molecules 17 3989–4006
PubMed PubMed Central Google Scholar
Bennett JW and Klich M 2003 Mycotoxins. Clin. Microbiol. Rev. 16 497–516
PubMed PubMed Central Google Scholar
Borchers AT, Chang C and Gershwin ME 2017 Mold and human health: a reality check. Clin. Rev. Allergy Immunol. 52 305–322
Bosch A, Sánchez G, Abbaszadegan M, et al. 2011 Analytical methods for virus detection in water and food. Food Anal. Methods 4 4–12
Brumini D, Criscione A, Bordonaro S, et al. 2016 Whey proteins and their antimicrobial properties in donkey milk: a brief review. Dairy Sci. Technol. 96 1–14
Bullerman LB 1979 Significance of mycotoxins to food safety and human health. J. Food Prot. 42 65–86
Centers for Disease Control and Prevention 2011 Burden of foodborne illness: Overview (https://archive.cdc.gov/www_cdc_gov/foodborneburden/estimates-overview.html)
Chakraborty S, Chatterjee R and Chakravortty D 2022 Evolving and assembling to pierce through: evolutionary and structural aspects of antimicrobial peptides. Comput. Struct. Biotechnol. J. 20 2247–2258
PubMed PubMed Central Google Scholar
Chapman MD 2006 Challenges associated with indoor moulds: health effects, immune response and exposure assessment. Med. Mycol. 44 S29–S32
Costello KM, Gutierrez-Merino J, Bussemaker M, et al. 2018 Modelling the microbial dynamics and antimicrobial resistance development of Listeria in viscoelastic food model systems of various structural complexities. Int. J. Food Microbiol. 286 15–30
Crim SM, Iwamoto M, Huang JY, et al. 2014 Incidence and trends of infection with pathogens transmitted commonly through food—Foodborne Diseases Active Surveillance Network, 10 US sites, 2006–2013. MMWR Morb. Mortal. Wkly. Rep. 63 328–332
PubMed PubMed Central Google Scholar
Cuggino SG, Possas A, Posada-Izquierdo GD, et al. 2023 Unveiling fresh-cut lettuce processing in Argentine industries: evaluating Salmonella levels using predictive microbiology models. Foods 12 3999
PubMed PubMed Central Google Scholar
Dagnas S and Membre JM 2013 Predicting and preventing mold spoilage of food products. J. Food Prot. 76 538–551
Devlieghere F, Vermeulen A and Debevere J 2004 Chitosan: antimicrobial activity, interactions with food components and applicability as a coating on fruit and vegetables. Food Microbiol. 21 703–714
Dong M, Holle MJ, Miller MJ, et al. 2024 Fates of attached E. coli o157:h7 on intact leaf surfaces revealed leafy green susceptibility. Food Microbiol. 119 104432
Feng CH 2022 Quality evaluation and mathematical modelling approach to estimate the growth parameters of total viable count in sausages with different casings. Foods 11 634
PubMed PubMed Central Google Scholar
Fu T, Gifford DR, Knight CG, et al. 2023 Eco-evolutionary dynamics of experimental Pseudomonas aeruginosa populations under oxidative stress. Microbiology 169 001396
Fujikawa H 2010 Development of a new logistic model for microbial growth in foods. Biocontrol Sci. 15 75–80
Gibson AM, Bratchell N and Roberts TA 1988 Predicting microbial growth: growth responses of salmonellae in a laboratory medium as affected by pH, sodium chloride and storage temperature. Int. J. Food Microbiol. 6 155–178
Godfray HC, Beddington JR, Crute IR, et al. 2010 Food security: the challenge of feeding 9 billion people. Science 327 812–818
Heinrich R, Rapoport S and Rapoport T 1978 Metabolic regulation and mathematical models. Prog. Biophys. Mol. Biol. 1–82
Hepburn K, Cordts KP, Danae D, et al. 2023 The challenges of conducting research in rural populations: a feasibility study. Online J. Rural Nurs. Health Care 23 21–38
Hiura S, Koseki S and 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
PubMed PubMed Central Google Scholar
Horowitz J, Normand MD, Corradini MG, et al. 2010 Probabilistic model of microbial cell growth, division, and mortality. Appl. Environ. Microbiol. 76 230–242
Huang L 2013 Optimization of a new mathematical model for bacterial growth. Food Control 32 283–288
Joerger RD 2007 Antimicrobial films for food applications: a quantitative analysis of their effectiveness. Packag. Technol. Sci. 20 231–273
Jubayer F, Soeb JA, Mojumder AN, et al. 2021 Detection of mold on the food surface using YOLOv5. Curr. Res. Food Sci. 4 724–728
PubMed PubMed Central Google Scholar
Juneja VK, Golden CE, Mishra A, et al. 2019 Predictive model for growth of Bacillus cereus at temperatures applicable to cooling of cooked pasta. J. Food Sci. 84 590–598
Kim S-J, Cho AR and Han J 2013 Antioxidant and antimicrobial activities of leafy green vegetable extracts and their applications to meat product preservation. Food Control 29 112–120
Koutsoumanis KP, Lianou A and Gougouli M 2016 Latest developments in foodborne pathogens modeling. Curr. Opin. Food Sci. 8 89–98
Larsen DS, Tang J, Ferguson L, et al. 2016 Textural complexity is a food property–shown using model foods. Int. J. Food Prop. 19 1544–1555
Law JW, Ab Mutalib NS, Chan KG, et al. 2014 Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations. Front. Microbiol. 5 770
Levins R 1966 The strategy of model building in population biology. Am. Sci. 54 421–431
Lobacz A, Kowalik J and Tarczynska A 2013 Modeling the growth of Listeria monocytogenes in mold-ripened cheeses. J. Dairy Sci. 96 3449–3460
Lobete MM, Fernandez EN and Van Impe JF 2015 Recent trends in non-invasive in situ techniques to monitor bacterial colonies in solid (model) food. Front. Microbiol. 6 148
PubMed PubMed Central Google Scholar
Longhi DA, Dalcanton F, Aragão GMFD, et al. 2017 Microbial growth models: a general mathematical approach to obtain μ max and λ parameters from sigmoidal empirical primary models. Braz. J. Chem. Eng. 34 369–375
Martin NH, Murphy SC, Ralyea RD, et al. 2011 When cheese gets the blues: Pseudomonas fluorescens as the causative agent of cheese spoilage. J. Dairy Sci. 94 3176–3183
Marvin HJ, Janssen EM, Bouzembrak Y, et al. 2017 Big data in food safety: an overview. Crit. Rev. Food Sci. Nutr. 57 2286–2295
McDonald K and Sun DW 1999 Predictive food microbiology for the meat industry: a review. Int. J. Food Microbiol. 52 1–27
Michaelis L, Menten ML, Johnson KA, et al. 2011 The original Michaelis constant: translation of the 1913 Michaelis-Menten paper. Biochemistry 50 8264–8269
Mitchell T 1997 Machine learning (McGraw Hill)
Motulsky H and Christopoulos A 2004 Fitting models to biological data using linear and nonlinear regression: a practical guide to curve fitting (Oxford University Press)
Neter J, Kutner MH, Nachtsheim CJ, et al. 1996 Applied linear statistical models (WCB McGraw-Hill)
Paul A, Ghosh N and Bhattacharya S 2022 Estimation of the present status of the species based on the theoretical bounds of environmental noise intensity: an illustration through a big abundance data and simulation. Theor. Ecol. 15 245–266
Peleg M and Corradini MG 2011 Microbial growth curves: what the models tell us and what they cannot. Crit. Rev. Food Sci. Nutr. 51 917–945
Pirt SJ 1975 Principles of microbe and cell cultivation (Halsted Press, Division of John Wiley and Sons)
Purk L, Kitsiou M, Ioannou C, et al. 2023 Unravelling the impact of fat content on the microbial dynamics and spatial distribution of foodborne bacteria in tri-phasic viscoelastic 3D models. Sci. Rep. 13 21811
Raposo A, Pérez E, De Faria CT, et al. 2016 Food spoilage by Pseudomonas spp.—an overview; in Foodborne pathogens and antibiotic resistance (Ed.) Singh OV (Wiley) pp 41–71
Skiadas CH 2010 Exact solutions of stochastic differential equations: Gompertz, generalized logistic and revised exponential. Methodol. Comput. Appl. Probab. 12 261–270
Skinner GE, Larkin JW and Rhodehamel EJ 1994 Mathematical modeling of microbial growth: a review. J. Food Saf. 14 175–217
Skjerdal T, Gangsei LE, Alvseike O, et al. 2021 Development and validation of a regression model for Listeria monocytogenes growth in roast beefs. Food Microbiol. 98 103770
Smet C, Noriega E, Rosier F, et al. 2017 Impact of food model (micro) structure on the microbial inactivation efficacy of cold atmospheric plasma. Int. J. Food Microbiol. 240 47–56
Soni A, Dixit Y, Reis MM, et al. 2022 Hyperspectral imaging and machine learning in food microbiology: developments and challenges in detection of bacterial, fungal, and viral contaminants. Compr. Rev. Food Sci. Food Saf. 21 3717–3745
Tedeschi LO 2006 Assessment of the adequacy of mathematical models. Agric. Syst. 89 225–247
Tomita M, Bellamy W, Takase M, et al. 1991 Potent antibacterial peptides generated by pepsin digestion of bovine lactoferrin. J. Dairy Sci. 74 4137–4142
Comments (0)