Evaluation of models describing the growth of nalidixic acid-resistant E. coli O157:H7 in blanched spinach and Iceberg lettuce as a function of temperature

A review of conditions influencing fate of Shiga toxin-producing Escherichia coli O157:H7 in leafy greens

The accuracy of predictive microbial models used in quantitative microbial risk assessment (QMRA) relies on the relevancy of conditions influencing growth or inactivation. The continued use of log-linear models in studies remains widespread, despite evidence that they fail to accurately account for biphasic kinetics or include parameters to account for the effect of environmental conditions within the model equation. Although many experimental studies detail conditions of interest, studies that do not do so lead to uncertainty in QMRA modeling because the applicability of the predictive microbial models to the conditions in the risk scenarios is questionable or must be extrapolated. The current study systematically reviewed 65 articles that provided quantitative data and documented the conditions influencing the inactivation or growth of Shiga toxin-producing Escherichia coli (STEC) O157:H7 in leafy greens. The conditions were identified and categorized as environmental, biological, chemical, and/or processing. Our study found that temperature (n = 37 studies) and sanitizing and washing procedures (n = 12 studies) were the most studied conditions in the farm-to-table continuum of leafy greens. In addition, relative humidity was also established to affect growth and inactivation in more than one stage in the continuum. This study proposes the evaluation of the interactive effects of multiple conditions in processing and storage stages from controlled experiments as they relate to the fate of STEC O157:H7 in leafy greens for future quantitative analysis.

A meta-analysis of factors influencing the inactivation of Shiga toxin-producing Escherichia coli O157:H7 in leafy greens

Recent advancements in modeling suggest that microbial inactivation in leafy greens follows a nonlinear pattern, rather than the simple first-order kinetics. In this study, we evaluated 17 inactivation models commonly used to describe microbial decline and established the conditions that govern microbial survival on leafy greens. Through a systematic review of 65 articles, we extracted 530 datasets to model the fate of Shiga toxin-producing Escherichia coli O157:H7 on leafy greens. Various factor analysis methods were employed to evaluate the impact of identified conditions on survival metrics. A two-parameter model (jm2) provided the best fit to most of both natural and antimicrobial-induced persistence datasets, whereas the one-parameter exponential model provided the best fit to less than 20% of the datasets. The jm2 model (adjusted R2 = .89) also outperformed the exponential model (adjusted R2 = .58) in fitting the pooled microbial survival data. In the context of survival metrics, the model averaging approach generated higher values than the exponential model for >4 log reduction times (LRTs), suggesting that the exponential model may be overpredicting inactivation at later time points. The random forest technique revealed that temperature and inoculum size were common factors determining inactivation in both natural and antimicrobial-induced die-offs.. The findings show the limitations of relying on the first-order survival metric of 1 LRT and considering nonlinear inactivation in produce safety decision-making.

Quantitative assessment of human exposure to extended spectrum and AmpC β-lactamases bearing E. coli in lettuce attributable to irrigation water and subsequent horizontal gene transfer

The contribution of the fresh produce production environment to human exposure with bacteria bearing extended spectrum β-lactamases and AmpC β-lactamases (ESBL/AmpC) has not been reported. High prevalence of ESBLs/AmpC bearing E. coli as well as a high gene transfer efficiency of lettuce and irrigation water E. coli isolates was previously reported. This stochastic modeling was aimed at quantitatively assessing human exposure to ESBL/AmpC bearing E. coli through lettuce attributable to irrigation water and subsequent horizontal gene transfer. Modular process risk approach was used for the quantitative exposure assessment and models were constructed in Ms. Excel spreadsheet with farm to consumption chain accounted for by primary production, processing, retail and consumer storage. Probability distributions were utilised to take into account the variability of the exposure estimates. Exposure resulting from ESBL/AmpC positive E. coli and gene transfer was taken into account. Monte Carlo simulation was carried out using @Risk software followed by sensitivity and scenario analysis to assess most effective single or combinations of mitigation strategies for the ESBL/AmpC positive E. coli events from farm to fork. Three percent of South African lettuce consumers are exposed to lettuce contaminated with about 106.4±106.7 (95% CI: 105.1-107) cfu of ESBL/AmpC positive E. coli per serving. The contribution of originally positive isolates and conjugative genetic transfer was 106±106.7 (95% CI: 105-107) and 105.2±105.6 (95% CI: 103.9-105.8) cfu per serving respectively. Proportion of ESBL/AmpC positive E. coli (Spearman's correlation coefficient (ρ)=0.85), conjugative gene transfer (ρ=0.05-0.14), washing in chlorine water (ρ=0.18), further rinsing (ρ=0.15), and prevalence of E. coli in irrigation water (ρ=0.16) had highest influence on consumer exposure. The most effective single methods in reducing consumer exposure were reduction in irrigation water microbial quality variation (87.4% reduction), storage period (49.9-87.4% reduction) and growth rate reduction by 75% (90% reduction). Reduction in growth rate together with storage time (92.1-99.4%) and reduction in storage time combined with E. coli concentration in irrigation water (95-96% reduction) were most effective combinations of mitigation measures. The high variation in exposure reflected the high irrigation water quality variation. The exposure levels may impose higher consumer risk than acceptable for irrigation water risk. E. coli contamination and growth related measures, as well as measures to reduce contamination with antimicrobial resistant E. coli from lettuce production environment are recommended. This exposure model could form a basis for the development of similar models assessing the impact of contaminated irrigation water and gene transfer in other microbial hazards, antimicrobial resistance types and fresh produce types.