JOURNAL ARTICLE

Kinetic modeling the survival of Escherichia coli in pickled radish fermentation with different salt concentrations.

  • Published In: Journal of Food Process Engineering, 2023, v. 46, n. 2. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Wu, Jiale; Yang, Li; Wu, Zhengyun; Zhang, Wenxue 3 of 3

Abstract

The survival of Escherichia coli in pickled radish fermentation with salt concentrations from 2% to 10% was studied kinetically. The results showed that mostly E. coli first underwent 1–2 days of growth, followed by a decline and finally died off after 4–5 days of fermentation. A kinetic model was developed to reveal how the growth and survival of E. coli were affected by acid content, pH value and salt concentration during the pickle fermentation. Model‐based simulations suggested that moderate salt concentration (4%–9%) resulted in shorter survival period of E. coli. Sensitivity analysis showed that the lowest pH value for E. coli growth played the most important role in determining the peak amount and the survival period of E. coli. This study provides a reference for risk assessment and safe production of pickles and other similar fermented vegetables. Practical Applications: This study provides an appropriate salt concentration range for safer production of pickles. The model developed can be a reference for the kinetic analyses and risk assessments of pickle making and other similar fermentation processes. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Food Process Engineering. 2023/02, Vol. 46, Issue 2, p1
  • Document Type:Article
  • Subject Area:Agriculture and Agribusiness
  • Publication Date:2023
  • ISSN:0145-8876
  • DOI:10.1111/jfpe.14241
  • Accession Number:161619106
  • Copyright Statement:Copyright of Journal of Food Process Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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