Quantitative modeling of the survival of Listeria monocytogenes in soy sauce-based acidified food products

Date

2022-06-02

Author

Doğan, Önay Burak
Stratton, Jayne
Arciniega, Ana
Clarke, Jennifer
Tamplin, Mark L.
Bianchini, Andreia
Wang, Bing

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Primary and secondary models were developed for quantitatively characterizing the survival of Listeria monocytogenes in soy-sauce based acidified Asian style products that do not undergo a thermal treatment. The objective of this study was to quantify the effect of food matrix properties on L. monocytogenes' survival in soy sauce-based products. This quantification enables a product-specific estimation of 5-log reduction time to ensure a safe processing and management operation, to ultimately facilitate a science-based, safety-oriented product development process. A central composite design with four independent variables (pH, soy sauce, added NaCl and soluble solids) with five levels was used to plan the challenge studies on different formulations. To model microbial survival over time, different non-linear primary models were fit to the data obtained from challenge studies. The best-fit model was selected based on a series of statistical goodness-of-fit measures. Kinetic parameters estimated from the best-fit primary models were fit to response surface equations using second order polynomial regression. The best-fit primary model representative of the product formulations was a modified Weibull model. The natural logarithm of the scale parameter (δ, in h) was used as the response variable for the secondary model. This resulted in acceptable fitting compared to the observed values with R2 values of 0.95 and RMSE of 0.7 h. External validity of model predictions was conducted by comparing them to 5-log reduction times observed in independent challenge tests using different product formulations. Results indicated an acceptable validation with R2 = 0.81 and RMSE = 35 h. The present study provides quantitative tools specific for cold-fill-hold soy sauce-based products to enhance microbial safety management plans and product development.

Subject Keywords

Cold-fill-hold product, Condiment, Inactivation, Listeriosis, Predictive microbiological modeling

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85126863354&origin=inward
https://hdl.handle.net/11511/105687

Collections

Department of Food Engineering, Article