A Machine Learning Approach to Predict Listeria monocytogenes Growth by Integrating FSSP and PMP Simulations: A Focus on t₁₀₀ as a Possible Endpoint Parameters for Predictive Models.
- 1Department of the Agriculture, Food, Natural Resources and Engineering, University of Foggia, Via Napoli 25, 71122 Foggia, Italy.
Abstract
Listeria monocytogenes is one of the most challenging foodborne pathogens for its psychrotrophic nature as well as the ability to grow under a wide range of environmental conditions; thus, food producers and regulatory agencies always search for new strategies to reduce the risk. In this context, predictive microbiology represents a valuable tool for supporting risk assessment and food safety management. This study investigated the time required for a 2-log increase in L. monocytogenes population (t₁₀₀) as an integrated time-to-threshold endpoint and developed a simulation-derived modeling framework combining outputs from the Food Spoilage and Safety Predictor (FSSP) and Pathogen Modeling Program (PMP). After a preliminary analysis based on DoE (Design of Experiments) theory, the dataset was expanded with 300 additional simulations and used to compare different modeling approaches (e.g., nonlinear models and Random Forest regression). The optimized Random Forest showed the best internal predictive performance; temperature and NaCl were the dominant predictors, followed by nitrite and pH, whereas simulation source and initial inoculum showed lower additional contributions. A separate FSSP-specific analysis showed that the inclusion of lactic acid, an input variable of this tool, did not improve the predictive performance of the model. Concerning the accuracy of the model, it is worth mentioning that error increases for high t₁₀₀ values and near the boundaries of the represented environmental space. Nevertheless, t₁₀₀ provides a useful integrated endpoint for combining heterogeneous simulated growth responses. However, the proposed model should be regarded as a simulation-derived metamodel whose performance has been assessed only by an internal cross-validation. Independent validation through challenge tests in representative food matrices is required before its applicability to practical food-safety decision making.
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BibTeX
@article{terlizzi2026machine,
title = {{A Machine Learning Approach to Predict Listeria monocytogenes Growth by Integrating FSSP and PMP Simulations: A Focus on t₁₀₀ as a Possible Endpoint Parameters for Predictive Models.}},
author = {Mario Terlizzi and Barbara Speranza and Angela Racioppo and Milena Sinigaglia and Maria Rosaria Corbo and Clelia Altieri and Antonio Bevilacqua},
journal = {Foods (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/foods15203640},
url = {https://europepmc.org/article/MED/42940277},
note = {CC BY},
}
RIS
TY - JOUR
TI - A Machine Learning Approach to Predict Listeria monocytogenes Growth by Integrating FSSP and PMP Simulations: A Focus on t₁₀₀ as a Possible Endpoint Parameters for Predictive Models.
AU - Mario Terlizzi
AU - Barbara Speranza
AU - Angela Racioppo
AU - Milena Sinigaglia
AU - Maria Rosaria Corbo
AU - Clelia Altieri
AU - Antonio Bevilacqua
PY - 2026
DA - 2026/10/14
JO - Foods (Basel, Switzerland)
DO - 10.3390/foods15203640
UR - https://europepmc.org/article/MED/42940277
AB - Listeria monocytogenes is one of the most challenging foodborne pathogens for its psychrotrophic nature as well as the ability to grow under a wide range of environmental conditions; thus, food producers and regulatory agencies always search for new strategies to reduce the risk. In this context, predictive microbiology represents a valuable tool for supporting risk assessment and food safety management. This study investigated the time required for a 2-log increase in L. monocytogenes population (t₁₀₀) as an integrated time-to-threshold endpoint and developed a simulation-derived modeling framework combining outputs from the Food Spoilage and Safety Predictor (FSSP) and Pathogen Modeling Program (PMP). After a preliminary analysis based on DoE (Design of Experiments) theory, the dataset was expanded with 300 additional simulations and used to compare different modeling approaches (e.g., nonlinear models and Random Forest regression). The optimized Random Forest showed the best internal predictive performance; temperature and NaCl were the dominant predictors, followed by nitrite and pH, whereas simulation source and initial inoculum showed lower additional contributions. A separate FSSP-specific analysis showed that the inclusion of lactic acid, an input variable of this tool, did not improve the predictive performance of the model. Concerning the accuracy of the model, it is worth mentioning that error increases for high t₁₀₀ values and near the boundaries of the represented environmental space. Nevertheless, t₁₀₀ provides a useful integrated endpoint for combining heterogeneous simulated growth responses. However, the proposed model should be regarded as a simulation-derived metamodel whose performance has been assessed only by an internal cross-validation. Independent validation through challenge tests in representative food matrices is required before its applicability to practical food-safety decision making.
SN - 2304-8158
ER -