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

Joshua Ombaka OwadeTeresa M BergholzJade Mitchell
Published in: Comprehensive reviews in food science and food safety (2024)
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.
Keyphrases
  • escherichia coli
  • risk assessment
  • microbial community
  • case control
  • climate change
  • cystic fibrosis
  • deep learning
  • pseudomonas aeruginosa
  • breast cancer risk