Sigmoidal microbial survival curves are observed in high-pressure carbon dioxide (HPCD) pasteurization treatments. The objectives of this study were to use the Gompertz primary model to describe the inactivation in apple juice of the pathogen Escherichia coli CGMCC1.90 and to apply probabilistic engineering to select HPCD treatments meeting at least 5 log10 reductions (SV ≥ 5) at 95% confidence. This required secondary models for the temperature (T, °C) and pressure (P, MPa) dependence of the Gompertz model parameters. The expressions b(T, P) = 14.21 + 0.011P - 0.67T + 0.0085T2 and c(T, P) = - 0.10 + 0.0023P + 0.0037T selected using goodness-of-fit measures and assessments based on Akaike and Bayesian information criteria were consistent with proposed mechanistic models for HPCD bactericidal effects. Monte Carlo simulations accounting for the variability and uncertainty of the parameter b and c estimates were used to predict SV values for a given time, temperature and CO2 pressure combination and desired confidence boundary. A similar approach used to estimate process times meeting SV ≥ 5 at 95% confidence for a given temperature and CO2 pressure combination, showed that HPCD processes met this requirement only for relatively long processing times, i.e., 35–124 min in the experimental range of 32–42 °C and 10–30 MPa. Therefore, further HPCD research is required to reduce processing time.
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