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. 2007 May 10;116(2):275-82.
doi: 10.1016/j.ijfoodmicro.2007.01.008. Epub 2007 Feb 14.

A novel approach to predicting microbial inactivation kinetics during high pressure processing

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A novel approach to predicting microbial inactivation kinetics during high pressure processing

Shigenobu Koseki et al. Int J Food Microbiol. .

Abstract

The inactivation kinetics of Escherichia coli (ATCC 25922) during high pressure processing (HPP) was examined from 200 to 400 MPa in 50 MPa increments at 15 degrees C. Although the time course of HPP-induced E. coli inactivation in 0.1% peptone water successfully fitted the Weibull function, this procedure involved curve fitting, and not prediction. The objective of this study was to develop a novel HPP-induced microbial inactivation model to simulate the inactivation kinetics under various pressure conditions. The maximum inactivation rate during HPP was calculated from the inactivation curves at different pressure conditions on a semi-log plot. The relationship between the square root of the absolute value of the inactivation rate (k(max)) and treatment pressure was linear (R(2)=0.99). The linear relationship between k(max) and treatment pressure also successfully described independent data from other studies in the literature. Overall, the newly developed differential equation model, into which was substituted the square root function of the inactivation rate, was capable of simulating the inactivation kinetics during HPP at constant pressure. Additionally, the model could successfully describe the inactivation kinetics during HPP using other researchers' data. The accuracy of prediction of the new model was comparable to that derived from Weibull or modified Gompertz fitting to the observed data. Furthermore, the new model could successfully simulate the inactivation kinetics during dynamic pressure conditions, which included come-up time, changes in holding pressure during treatment, and pressure-release time. Moreover, the effect of pulsed pressure treatment was also simulated successfully using this model. Therefore, the modeling procedure presented in this study will contribute to the advancement of predictive modeling for HPP-induced microbial inactivation.

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