A Theoretical Analysis for Assessing the Variability of Secondary Model Thermal Inactivation Kinetic Parameters

Abstract

Traditionally, for the determination of the kinetic parameters of thermal inactivation of a heat labile substance, an appropriate index is selected and its change is measured over time at a series of constant temperatures. The rate of this change is described through an appropriate primary model and a secondary model is applied to assess the impact of temperature. By this approach, the confidence intervals of the estimates of the rate constants are not taken into account. Consequently, the calculated variability of the secondary model parameters can be significantly lower than the actual variability. The aim of this study was to demonstrate the influence of the variability of the primary model parameters in establishing the confidence intervals of the secondary model parameters. Using a Monte Carlo technique and assuming normally distributed DT values (parameter associated with a primary inactivation model), the error propagating on the DTref and z-values (secondary model parameters) was assessed. When DT confidence intervals were broad, the secondary model’s parameter variability was appreciably high and could not be adequately estimated through the traditional deterministic approach that does not take into account the variation on the DT values. In such cases, the proposed methodology was essential for realistic estimations.

Survival of Escherichia coli O157:H7 during Moderate Temperature Dehydration of Plant-Based Foods

“…Dynamic processing simulations which do not account for the impact of fill density on microbial inactivation may overestimate predicted lethality. The complexity of developing accurate models for both isothermal [ 41 , 42 ] and dynamic processes [ 43 ] has been identified as relevant in validation, even as there has been increased recognition of the food safety challenges in low a w foods which cannot simply be controlled by preventing contamination [ 44 , 45 ]. The research presented here contributes to the ongoing effort to improve thermal inactivation stratagies for the growing sector of minimally processed, plant-based foods.…”

Quality and Shelf-Life Modeling of Frozen Fish at Constant and Variable Temperature Conditions

“…In order to evaluate the quality changes of frozen fish during storage, the “apparent kinetics” methodological approach was used. This methodology included two main successive calculations [ 25 , 26 ]: A primary kinetic model, where values obtained from the different measured quality parameters were plotted vs. time for all the tested storage temperatures. The apparent order of quality loss was estimated, based on the least square statistical fit.…”

Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions

“…applied through a FORTRAN algorithm. This iterative algorithm is applied based on the previous assumption that E a and k ref variability are effectively represented by a normal distribution [14,25,57]. At each iteration, a random number is generated through an appropriate FORTRAN routine function, and a value is assigned to E a and k ref (independently the one from the other).…”

“…This includes two main successive calculations. A primary kinetic model is developed via best statistical fit to describe the selected quality parameter change as a function of processing time or post-processing storage/distribution and a secondary model that reflects the effect of processing factors and/or environmental conditions on primary model's parameters [14]. Once validated, these mathematical formulae can be a practical tool to predict post-processing quality status at any stage of food storage.…”