Assessment of the uncertainty in thermal food processing decisions based on microbial safety objectives

Inactivation model and risk-analysis design for apple juice processing by high-pressure CO2

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 [Formula: see text] and [Formula: see text] 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.

Analysis of Vibrio vulnificus Infection Risk When Consuming Depurated Raw Oysters

A beta Poisson dose-response model for Vibrio vulnificus food poisoning cases leading to septicemia was used to evaluate the effect of depuration at 15 °C on the estimated health risk associated with raw oyster consumption. Statistical variability sources included V. vulnificus level at harvest, time and temperature during harvest and transportation to processing plants, decimal reductions (SV) observed during experimental circulation depuration treatments, refrigerated storage time before consumption, oyster size, and number of oysters per consumption event. Although reaching nondetectable V. vulnificus levels (<30 most probable number per gram) throughout the year and a 3.52 SV were estimated not possible at the 95% confidence level, depuration for 1, 2, 3, and 4 days would reduce the warm season (June through September) risk from 2,669 cases to 558, 93, 38, and 47 cases per 100 million consumption events, respectively. At the 95% confidence level, 47 and 16 h of depuration would reduce the warm and transition season (April through May and October through November) risk, respectively, to 100 cases per 100 million consumption events, which is assumed to be an acceptable risk; 1 case per 100 million events would be the risk when consuming untreated raw oysters in the cold season (December through March).

Use of simulation tools to illustrate the effect of data management practices for low and negative plate counts on the estimated parameters of microbial reduction models

In the last 20 years, the use of microbial reduction models has expanded significantly, including inactivation (linear and nonlinear), survival, and transfer models. However, a major constraint for model development is the impossibility to directly quantify the number of viable microorganisms below the limit of detection (LOD) for a given study. Different approaches have been used to manage this challenge, including ignoring negative plate counts, using statistical estimations, or applying data transformations. Our objective was to illustrate and quantify the effect of negative plate count data management approaches on parameter estimation for microbial reduction models. Because it is impossible to obtain accurate plate counts below the LOD, we performed simulated experiments to generate synthetic data for both log-linear and Weibull-type microbial reductions. We then applied five different, previously reported data management practices and fit log-linear and Weibull models to the resulting data. The results indicated a significant effect (α = 0.05) of the data management practices on the estimated model parameters and performance indicators. For example, when the negative plate counts were replaced by the LOD for log-linear data sets, the slope of the subsequent log-linear model was, on average, 22% smaller than for the original data, the resulting model underpredicted lethality by up to 2.0 log, and the Weibull model was erroneously selected as the most likely correct model for those data. The results demonstrate that it is important to explicitly report LODs and related data management protocols, which can significantly affect model results, interpretation, and utility. Ultimately, we recommend using only the positive plate counts to estimate model parameters for microbial reduction curves and avoiding any data value substitutions or transformations when managing negative plate counts to yield the most accurate model parameters.

Selection of process conditions by risk assessment for apple juice pasteurization by UV-heat treatments at moderate temperatures

The effect of bactericidal UV-C treatments (254 nm) on Escherichia coli O157:H7 suspended in apple juice increased synergistically with temperature up to a threshold value. The optimum UV-C treatment temperature was 55 °C, yielding a 58.9% synergistic lethal effect. Under these treatment conditions, the UV-heat (UV-H55 °C) lethal variability achieving 5-log reductions had a logistic distribution (α = 37.92, β = 1.10). Using this distribution, UV-H55 °C doses to achieve the required juice safety goal with 95, 99, and 99.9% confidence were 41.17, 42.97, and 46.00 J/ml, respectively, i.e., doses higher than the 37.58 J/ml estimated by a deterministic procedure. The public health impact of these results is that the larger UV-H55 °C dose required for achieving 5-log reductions with 95, 99, and 99.9% confidence would reduce the probability of hemolytic uremic syndrome in children by 76.3, 88.6, and 96.9%, respectively. This study illustrates the importance of including the effect of data variability when selecting operational parameters for novel and conventional preservation processes to achieve high food safety standards with the desired confidence level.