Author ORCID Identifier:
Date of Graduation
12-2025
Document Type
Thesis
Degree Name
Master of Science in Food Science (MS)
Degree Level
Graduate
Department
Food Science
Advisor/Mentor
Subbiah, Jeyamkondan
Committee Member
Acuff, Jennifer C.
Second Committee Member
Fernandes, Samule B.
Keywords
food safety; lag time; Listeria; Predictive modeling; RTE; validation
Abstract
Listeria monocytogenes remains a significant food safety concern in ready-to-eat (RTE) meat and poultry products due to its persistence under refrigerated and stressed conditions. Predictive models play a vital role in risk assessment and food safety management; broth models are highly conservation & results in overprocessing. This thesis focuses on developing and validating meat-specific predictive models for Listeria monocytogenes growth and lag for RTE beef, pork, and poultry products. Matrix-specific secondary models were built to predict the maximum specific growth rate (μmax) of Listeria monocytogenes under varying environmental conditions, including temperature, pH, water activity, salt, organic acids, and nitrites. Growth rates were estimated using primary models, Baranyi model and the logistic-with-delay model and then corresponding secondary models were developed using the gamma concept. The logistic-with-delay gamma-based all meat generalized model consistently performed better the Baranyi gamma-based model, yielding lower root mean square errors (RMSE= 0.066–0.075 h⁻¹), minimal bias (Bf = 1.03), and improved predictive accuracy across all meat types. Over 86% of predicted growth rates were within acceptable error margins. This thesis also developed lag time prediction models using the Relative Lag Time (RLT) framework, correlating lag duration with μmax under various environmental stressors. Physiological approaches based on the h₀ parameter and K adaptation constant were compared. Lag time predictions showed that the RLT method using K performed better than the Baranyi ho method across all RTE meat matrices, yielding minimal bias (Bf =1.04, Af = 1.31), low RMSE (0.1.00 ln(h)), and higher correlation coefficient (R2 = 0.68) with lower mean prediction error of 109.7. The RLT method using K-concept approach provided the most reliable estimates, accurately capturing physiological adaptation effects under varying temperature, pH, water activity, and preservative conditions. While matrix specific models valuable insights, limited data under certain conditions reduced stability. The generalized all-meat model resulted in improved accuracy and more stable predictive performance, making it suitable for practical applications. The validated logistic-with-delay growth and lag time models offer reliable tools for microbial risk assessment of RTE meats. These models can be integrated into decision-support systems, guiding process design, hazard analysis, and regulatory strategies to strengthen food safety management.
Citation
SINGH, M. (2025). Predictive Modeling of Listeria monocytogenes Growth in Ready-to-Eat Meat Products. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/6054
