Predictive Microbiology: New Trends of Food Safety and Quality in Food Processing Systems

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Microbiology".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2023

Special Issue Editors


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Guest Editor Assistant
Human Environmental Sciences Department, University of Central Oklahoma, Edmond, OK 73034, USA
Interests: food safety; food microbiology; predictive modeling; foodborne pathogens; environmental monitoring; traceability; supply chain; risk analysis

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Guest Editor
Department of Food Science & Technology, University of Georgia, Athens, GA 30602, USA
Interests: food microbiology; modeling microbial responses in food; quantitative microbial risk assessment (QMRA); food safety

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Guest Editor
Microbial and Chemical Food Safety, USDA-ARS, Washington, DC, USA
Interests: food safety microbiology; predictive modeling; minimally processed foods

Special Issue Information

Dear Colleagues,

This edition aims to showcase the recent developments in predictive modelling for the enhancement of food processing systems' safety and quality. The predictive microbial models can be used to develop precise interventions for the control of pathogenic and spoilage microorganisms in food products. This edition will focus on predictive models, including kinetic, probability, empirical, and mechanistic models. By leveraging predictive modelling, food processors can drastically reduce the time required for challenge studies, enabling rapid identification of potential foodborne contamination and solutions.

Therefore, we are pleased to invite researchers to contribute to a Special Issue on Predictive Microbiology: New Trends of Food Safety and Quality in Food Processing Systems. We are calling for papers on the following research topics with a focus on predictive modelling:

  1. Evaluating the impact of biotic and abiotic factors such as temperature, pH, moisture, and nutrient availability on the survival, growth, and inactivation of pathogenic and spoilage microorganisms;
  2. Quantitative risk assessment techniques;
  3. Investigation of thermal and non-thermal intervention technologies to reduce the risks associated with the microbial contamination in foods;
  4. Development and validation of primary, secondary, and tertiary predictive models;
  5. Exploring the potential of machine learning techniques in predictive microbiology;
  6. Case studies on practical applications of predictive models to estimate food safety and quality.

Dr. Priyanka Gupta
Guest Editor Assistant

Dr. Abhinav Mishra
Dr. Vijay K. Juneja
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • predictive modelling
  • antimicrobial interventions
  • risk assessment
  • combination models
  • pathogen control

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Published Papers (2 papers)

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16 pages, 2148 KiB  
Article
Modeling Behavior of Salmonella spp. and Listeria monocytogenes in Raw and Processed Vegetables
by Su Bin Son, Ha Kyoung Lee, So Jeong Kim and Ki Sun Yoon
Foods 2024, 13(18), 2972; https://doi.org/10.3390/foods13182972 - 19 Sep 2024
Viewed by 730
Abstract
Given the persistent occurrence of foodborne illnesses linked to both raw and processed vegetables, understanding microbial behavior in these foods under distribution conditions is crucial. This study aimed to develop predictive growth models for Salmonella spp. and Listeria monocytogenes in raw (mung bean [...] Read more.
Given the persistent occurrence of foodborne illnesses linked to both raw and processed vegetables, understanding microbial behavior in these foods under distribution conditions is crucial. This study aimed to develop predictive growth models for Salmonella spp. and Listeria monocytogenes in raw (mung bean sprouts, onion, and cabbage) and processed vegetables (shredded cabbage salad, cabbage and onion juices) at various temperatures, ranging from 4 to 36 °C. Growth models were constructed and validated using isolated strains of Salmonella spp. (S. Bareilly, S. Enteritidis, S. Typhimurium) and L. monocytogenes (serotypes 1/2a and 1/2b) from diverse food sources. The minimum growth temperatures for Salmonella varied among different vegetable matrices: 8 °C for mung bean sprouts, 9 °C for both onion and cabbage, and 10 °C for ready-to-eat (RTE) shredded cabbage salad. Both pathogens grew in cabbage juice at temperatures above 17 °C, while neither demonstrated growth in onion juice, even at 36 °C. Notably, Salmonella spp. exhibited faster growth than L. monocytogenes in all tested samples. At 8 °C, the lag time (LT) and specific growth rate (SGR) for Salmonella spp. in mung bean sprouts were approximately tenfold longer and threefold slower, respectively, compared to those at 10 °C. A decrease in refrigerator storage temperature by 1 or 2 degrees significantly prevented the growth of Salmonella in raw vegetables. These findings offer valuable insights into assessing the risk of foodborne illness associated with the consumption of raw and processed vegetables and inform management strategies in mitigating these risks. Full article
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9 pages, 909 KiB  
Technical Note
Open Access Bacillus cereus Cocktail Secondary Growth Model for the Food Industry
by Xiaoyang Tang, Dingwu Zhang and Pradeep K. Malakar
Foods 2024, 13(21), 3382; https://doi.org/10.3390/foods13213382 - 24 Oct 2024
Viewed by 554
Abstract
A cost-effective algorithm is presented, using a virtual dataset of growth rates from a cocktail of Bacillus cereus strains, for developing an open access, extended-range secondary growth model. Extended-range growth models can span the range of processing conditions typically used in food manufacturing [...] Read more.
A cost-effective algorithm is presented, using a virtual dataset of growth rates from a cocktail of Bacillus cereus strains, for developing an open access, extended-range secondary growth model. Extended-range growth models can span the range of processing conditions typically used in food manufacturing and are therefore more relevant for industry. The open access extended-range secondary growth model for a cocktail of B. cereus strains was created using publicly available data, and the methodology can be adapted for modelling growth of other pathogens. An extended-range model can help manage B. cereus hazards in novel food categories with non-traditional formulations as estimations of B. cereus risks in these foods become more precise. This open access model, however, needs to be validated using data from B. cereus strain cocktails isolated from production facilities. Once validated, these independent factor models are valuable tools, in a pathogen decision support platform, which are tuned to local production environments. Such a platform can address the needs of current and future food product portfolios, effectively mitigating risks associated with B. cereus and other relevant pathogens. Full article
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