Comparison of Secondary Models for Modelling the Growth of Salmonella in Leafy Greens
DOI:
https://doi.org/10.24925/turjaf.v13is3.3927-3933.8140Keywords:
Food safety, Foodborne outbreaks, Pathogens, Predictive microbiology, LettuceAbstract
This study evaluated the performance of three mathematical models—the Ratkowsky square-root, Arrhenius-type, and Cardinal models—in describing the temperature-dependent growth of Salmonella in leafy greens. Therefore, data on the maximum growth rate (μmax) of Salmonella enterica in leafy greens under different temperature conditions were collected from peer-reviewed scientific publications available in databases. The Integrated Pathogen Modeling Program (IPMP) software was then used for modelling and analysis. The results indicated that the sub-optimal Ratkowsky model most accurately captured growth rates below the organism’s optimum temperature, with parameters a=0.02 and Tmin=7.3°C estimated with high precision. In contrast, the model’s supra-optimal parameters were weakly constrained due to limited high-temperature data. Despite achieving strong explanatory power (R2=0.90), the Arrhenius-type and Cardinal models had significant parameter uncertainty over the whole temperature range, which limited their usefulness. These findings support the use of simpler, empirically grounded models in predictive microbiology when thermal data are incomplete. Future research should prioritize gathering data at higher temperatures to better characterize growth decline in the supra-optimal range.
References
Aladhadh, M. (2023). A Review of Modern Methods for the Detection of Foodborne Pathogens. Microorganisms, 11(5), Article 5. https://doi.org/10.3390/microorganisms11051111
Baranyi, J., Buss da Silva, N., & Ellouze, M. (2017). Rethinking Tertiary Models: Relationships between Growth Parameters of Bacillus cereus Strains. Frontiers in Microbiology, 8. https://doi.org/10.3389/fmicb.2017.01890
Baranyi, J., Rockaya, M., & Ellouze, M. (2024). From data to models and predictions in food microbiology. Current Opinion in Food Science, 57, 101177. https://doi.org/10.1016/j.cofs.2024.101177
Cuggino, S. G., Possas, A., Posada-Izquierdo, G. D., Theumer, M. G., & Pérez-Rodríguez, F. (2023). Unveiling Fresh-Cut Lettuce Processing in Argentine Industries: Evaluating Salmonella Levels Using Predictive Microbiology Models. Foods, 12(21), Article 21. https://doi.org/10.3390/foods12213999
de Oliveira Elias, S., Noronha, T. B., & Tondo, E. C. (2018). Assessment of Salmonella spp. And Escherichia coli O157:H7 growth on lettuce exposed to isothermal and non-isothermal conditions. Food Microbiology, 72, 206–213. https://doi.org/10.1016/j.fm.2017.11.016
Hoffmann, S., White, A. E., McQueen, R. B., Ahn, J.-W., Gunn-Sandell, L. B., & Scallan Walter, E. J. (2024). Economic Burden of Foodborne Illnesses Acquired in the United States. Foodborne Pathogens and Disease. https://doi.org/10.1089/fpd.2023.0157
Huang, L. (2013). Optimization of a new mathematical model for bacterial growth. Food Control, 32(1), 283–288. https://doi.org/10.1016/j.foodcont.2012.11.019
Huang, L. (2014). IPMP 2013—A comprehensive data analysis tool for predictive microbiology. International Journal of Food Microbiology, 171, 100–107. https://doi.org/10.1016/j.ijfoodmicro.2013.11.019
Huang, L., Hwang, A., & Phillips, J. (2011a). Effect of Temperature on Microbial Growth Rate–Mathematical Analysis: The Arrhenius and Eyring–Polanyi Connections. Journal of Food Science, 76(8), E553–E560. https://doi.org/10.1111/j.1750-3841.2011.02377.x
Huang, L., Hwang, C.-A., & Phillips, J. (2011b). Evaluating the Effect of Temperature on Microbial Growth Rate—The Ratkowsky and a Bělehrádek-Type Models. Journal of Food Science, 76(8), M547–M557. https://doi.org/10.1111/j.1750-3841.2011.02345.x
Koseki, S., & Isobe, S. (2005). Prediction of pathogen growth on iceberg lettuce under real temperature history during distribution from farm to table. International Journal of Food Microbiology, 104(3), 239–248. https://doi.org/10.1016/j.ijfoodmicro.2005.02.012
Kumar, V., Ahire, J. J., & Taneja, N. K. (2024). Advancing microbial food safety and hazard analysis through predictive mathematical modeling. The Microbe, 2, 100049. https://doi.org/10.1016/j.microb.2024.100049
Li, H., Xie, G., & Edmondson, A. S. (2008). Review of Secondary Mathematical Models of Predictive Microbiology. Journal of Food Products Marketing, 14(2), 57–74. https://doi.org/10.1080/10454440801918283
McMeekin, T., Olley, J., Ratkowsky, D., Corkrey, R., & Ross, T. (2013). Predictive microbiology theory and application: Is it all about rates? Food Control, 29(2), 290–299. https://doi.org/10.1016/j.foodcont.2012.06.001
Mertens, L., Van Derlinden, E., & Van Impe, J. F. (2012). Comparing experimental design schemes in predictive food microbiology: Optimal parameter estimation of secondary models. Journal of Food Engineering, 112(3), 119–133. https://doi.org/10.1016/j.jfoodeng.2012.03.018
Mishra, A., Guo, M., Buchanan, R. L., Schaffner, D. W., & Pradhan, A. K. (2017). Prediction of Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes Growth in Leafy Greens without Temperature Control. Journal of Food Protection, 80(1), 68–73. https://doi.org/10.4315/0362-028X.JFP-16-153
Ndraha, N., Goh, A. P., Tran, G. D., Chen, C., & Hsiao, H.-I. (2022). Predictive models for the growth of Salmonella spp., Listeria spp., and Escherichia coli in lettuce harvested on Taiwanese farms. Journal of Food Science, 87(8), 3599–3610. https://doi.org/10.1111/1750-3841.16236
Park, S. Y., Zhang, C. Y., & Ha, S.-D. (2019). Predictive Modeling for the Growth of Salmonella Enterica Serovar Typhimurium on Lettuce Washed with Combined Chlorine and Ultrasound During Storage. Journal of Food Hygiene and Safety, 34(4), 374–379. https://doi.org/10.13103/JFHS.2019.34.4.374
Peleg, M., & Corradini, M. G. (2011). Microbial Growth Curves: What the Models Tell Us and What They Cannot. Critical Reviews in Food Science and Nutrition, 51(10), 917–945. https://doi.org/10.1080/10408398.2011.570463
Peleg, M., Normand, M. D., & Corradini, M. G. (2012). The Arrhenius Equation Revisited. Critical Reviews in Food Science and Nutrition, 52(9), 830–851. https://doi.org/10.1080/10408398.2012.667460
Puerta-Gomez, A. F., Moreira, R. G., Kim, J., & Castell-Perez, E. (2013). Modeling the growth rates of Escherichia coli spp. And Salmonella Typhimurium LT2 in baby spinach leaves under slow cooling. Food Control, 29(1), 11–17. https://doi.org/10.1016/j.foodcont.2012.05.070
Ratkowsky, D. A., Lowry, R. K., McMeekin, T. A., Stokes, A. N., & Chandler, R. E. (1983). Model for bacterial culture growth rate throughout the entire biokinetic temperature range. Journal of Bacteriology, 154(3), 1222–1226. https://doi.org/10.1128/jb.154.3.1222-1226.1983
Sant’Ana, A. S., Barbosa, M. S., Destro, M. T., Landgraf, M., & Franco, B. D. G. M. (2012). Growth potential of Salmonella spp. And Listeria monocytogenes in nine types of ready-to-eat vegetables stored at variable temperature conditions during shelf-life. International Journal of Food Microbiology, 157(1), 52–58. https://doi.org/10.1016/j.ijfoodmicro.2012.04.011
Son, S. B., Lee, H. K., Kim, S. J., & Yoon, K. S. (2024). Modeling Behavior of Salmonella spp. And Listeria monocytogenes in Raw and Processed Vegetables. Foods, 13(18), Article 18. https://doi.org/10.3390/foods13182972
Taiwo, O. R., Onyeaka, H., Oladipo, E. K., Oloke, J. K., & Chukwugozie, D. C. (2024). Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models. International Journal of Microbiology, 2024(1), 6612162. https://doi.org/10.1155/2024/6612162
Tarlak, F. (2023). The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products. Foods, 12(24), Article 24. https://doi.org/10.3390/foods12244461
Tarlak, F., Johannessen, G., Bascón Villegas, I., Bolívar, A., Posada-Izquierdo, G. D., & Pérez-Rodríguez, F. (2020). Modelling of the Behaviour of Salmonella enterica serovar Reading on Commercial Fresh-Cut Iceberg Lettuce Stored at Different Temperatures. Foods, 9(7), Article 7. https://doi.org/10.3390/foods9070946
Veys, O., Elias, S. de O., Sampers, I., & Tondo, E. C. (2016). Modelling the Growth of Salmonella spp. And Escherichia Coli O157 on Lettuce. Procedia Food Science, 7, 168–172. https://doi.org/10.1016/j.profoo.2016.10.003
Warmate, D., & Onarinde, B. A. (2023). Food safety incidents in the red meat industry: A review of foodborne disease outbreaks linked to the consumption of red meat and its products, 1991 to 2021. International Journal of Food Microbiology, 398, 110240. https://doi.org/10.1016/j.ijfoodmicro.2023.110240
Xiao, X., Tang, B., Liu, S., Suo, Y., Yang, H., & Wang, W. (2021). Evaluation of the Stress Tolerance of Salmonella with Different Antibiotic Resistance Profiles. BioMed Research International, 2021(1), 5604458. https://doi.org/10.1155/2021/5604458
Yoon, J.-H., Bae, Y.-M., Jung, S.-Y., Cha, M.-H., Ryu, K., Park, K.-H., & Lee, S.-Y. (2014). Predictive modeling for the growth of Listeria monocytogenes and Salmonella Typhimurium on fresh-cut cabbage at various temperatures. Journal of the Korean Society for Applied Biological Chemistry, 57(5), 631–638. https://doi.org/10.1007/s13765-014-4096-y
Yu, W., Ouyang, Z., Zhang, Y., Lu, Y., Wei, C., Tu, Y., & He, B. (2025). Research progress on the artificial intelligence applications in food safety and quality management. Trends in Food Science & Technology, 156, 104855. https://doi.org/10.1016/j.tifs.2024.104855
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


