Mathematical modelling and validation of Salmonella enterica growth in sushi exposed to different time-temperature scenarios found in real sushi establishments

Propidium monoazide (PMA) qPCR assay compared to the plate count method for quantifying the growth of Salmonella enterica serotypes in vacuum-packaged turkey breast combined with a mathematical modeling approach

This study compares the plate count (PC) and the Propidium Monoazide-quantitative Polymerase Chain Reaction (PMA-qPCR) methods to assess the growth of a cocktail of three serotypes of Salmonella enterica (Heidelberg, Typhimurium, and Enteritidis) in cooked, sliced, and vacuum-packaged turkey breast (STB) under isothermal storage temperatures (8 °C-20 °C), using predictive models. Standard curves were developed for PMA-qPCR, demonstrating high efficiency (101%) and sensitivity, with quantification limits ranging from 1 to 2 log10 CFU/g for all temperatures studied. Comparative analysis revealed a significant correlation (R2 = 0.99; 95% CI) between the PC and PMA-qPCR methods; however, the agreement analysis indicated a mean difference (Bias) of -0.11 log10 CFU/g (p < 0.05), suggesting underestimation by the PC method. This indicates the presence of stressed or viable but nonculturable (VBNC) cells, detectable by PMA-qPCR but not by PC. The Baranyi and Roberts model showed a good ability to describe the behavior of S. enterica cocktail in STB for PC and PMA-qPCR data under all isothermal conditions. The exponential secondary model more accurately represented the temperature dependence of the maximum specific growth rate compared to the Ratkowsky square root model, with R2 values ≥ 0.984 and RMSE values ≤ 0.011 for both methods. These results suggest that combining PMA-qPCR with predictive modeling allows for a more accurate prediction of S. enterica growth, compared to PC method. In the event of cold chain disruptions of meat products, the use of PMA-qPCR method allow the quantification of VBNC cells, that can still pose a health risk to consumers, especially in ready-to-eat products.

Growth Effects as a Function of pH and Temperature of Salmonella enterica serovar Choleraesuis in In Vitro Tests and Chicken Meat: Prediction and Modeling

The present study aimed to predict and model the growth of Salmonella enterica serovar Choleraesuis as a function of pH and temperature in in vitro tests and raw chicken meat. To describe the growth, the data were adjusted by the Modified Gompertz primary model. After comparison, they presented R2 values > 0.9799 and an RMSE of 0.092 to 0.274. With the values of µmax obtained by the primary model, it was possible to evaluate the influence of temperature using the secondary Gamma model. Comparing the results obtained experimentally with those predicted by the ComBase tertiary model, it was possible to verify that the analyses carried out in the laboratory show that Salmonella did not multiply for a longer time. At pH 5, 6, and 7 at 12 °C, ComBase presented a λ of 43, 31, and 28 h, while the experimental λ was 109, 63, and 85 h, respectively. A correlation was observed between rising temperatures and pH, leading to an increase in μmax and a decrease in λ. However, no correlation was found concerning ymax.

Measurement Time Reduction by Means of Mathematical Modeling of Enzyme Mediated RedOx Reaction in Food Samples Biosensors

The possibility of measuring in real time the different types of analytes present in food is becoming a requirement in food industry. In this context, biosensors are presented as an alternative to traditional analytical methodologies due to their specificity, high sensitivity and ability to work in real time. It has been observed that the behavior of the analysis curves of the biosensors follow a trend that is reproducible among all the measurements and that is specific to the reaction that occurs in the electrochemical cell and the analyte being analyzed. Kinetic reaction modeling is a widely used method to model processes that occur within the sensors, and this leads to the idea that a mathematical approximation can mimic the electrochemical reaction that takes place while the analysis of the sample is ongoing. For this purpose, a novel mathematical model is proposed to approximate the enzymatic reaction within the biosensor in real time, so the output of the measurement can be estimated in advance. The proposed model is based on adjusting an exponential decay model to the response of the biosensors using a nonlinear least-square method to minimize the error. The obtained results show that our proposed approach is capable of reducing about 40% the required measurement time in the sample analysis phase, while keeping the error rate low enough to meet the accuracy standards of the food industry.