A Gompertz Model Approach to Microbial Inactivation Kinetics by High-Pressure Processing Incorporating the Initial Counts, Microbial Quantification Limit, and Come-Up Time Effects
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During come-up time (CUT), the time to reach a desired processing pressure, isobaric-isothermal conditions cannot be assumed in the estimation of kinetic parameters for the design of commercial high-pressure processing (HPP) treatments. Since CUT effects on microbial population, enzyme activity, and chemical concentration are often ignored, kinetic models incorporating the non-isobaric and non-isothermal conditions prevailing during CUT were the objective of this work. The analysis of peer-reviewed data on the HPP inactivation of bacteria (counts observations n = 919, 60 survival curves) and bacterial spores (n = 273, 12 curves) showed that a Gompertz model (GMPZ) approach is an effective alternative. The GMPZ parameter A was fixed as the difference between the initial population (log10 N o ) and the lower quantification limit of microbial counts (log10 N lim), while exponential equations were used to describe pressure effects on the lag time (λ) and the maximum inactivation rate (μmax). In low-acid media (pH > 4.5), λ decreased exponentially with pressure, allowing the identification of a theoretical pressure level (P λ) sufficient to initiate microbial inactivation during CUT. The parameter μmax exponentially increased with pressure for all evaluated datasets. Dynamic pressure effects during CUT were simplified by assuming isobaric conditions during CUT (t CUT), allowing to obtain GMPZ parameter estimates using only nonlinear regression (R 2 ∼ 0.938, σ 2 = 0.030–0.604). The proposed approach is a simpler, promising tool for a more informative analysis of the kinetics of microbial inactivation by HPP and should be further validated with additional experimental data.
KeywordsHigh-pressure processing (HPP) Pressure-assisted thermal processing (PATP) Come-up time (CUT) Microbial inactivation kinetics Microbial quantification level Gompertz model
The authors acknowledge the support from the Tecnológico de Monterrey (Research chair funds GEE 1A01001 and CDB081) and México’s CONACYT Scholarship Program (Grant no. 227790).
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