Special Issue "Evaluation of Risks of Microbiological Origin Associated with Food Consumption"

A special issue of Microorganisms (ISSN 2076-2607). This special issue belongs to the section "Food Microbiology".

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editor

Prof. Dr. Elena González-Fandos
Website
Guest Editor
Food Technology Department, Universidad de La Rioja, Logroño, Rioja, Spain
Interests: campylobacter; poultry; Listeria; foodborne pathogens
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In order to evaluate microbiological risk, knowledge of food consumption patterns is essential. Food consumption data is often taken from food consumption surveys designed to obtain epidemiological data on risk factors for chronic diseases or nutritional intake. In the last several years, changes in food consumption patterns have been observed (a preference for raw foods, RTE foods…). It will be of great interest to obtain data on consumption of ready to eat foods (RTE) and the consumption of raw milk or raw fish, among others, especially in high risk populations. On other hand, storage time-temperature, cooking preferences, handling and preparation of foods play an important role in food safety.

The aim of this Special Issue is to present research on the effect of food consumption patterns and food handling and preparation at consumer level on microbiological food safety. Original research articles, as well as review articles, are invited.

Prof. Dr. Elena González-Fandos
Guest Editor

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 papers will be 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. Microorganisms is an international peer-reviewed open access monthly 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 2000 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

  • Food safety
  • Food consumption
  • Food preparation
  • Food handling
  • Microbiological risk assessment
  • Predictive microbiology

Published Papers (2 papers)

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Research

Open AccessArticle
Predictive Modeling for the Growth of Salmonella spp. in Liquid Egg White and Application of Scenario-Based Risk Estimation
Microorganisms 2021, 9(3), 486; https://doi.org/10.3390/microorganisms9030486 - 25 Feb 2021
Viewed by 188
Abstract
The objective of the study was to develop a predictive model of Salmonella spp. growth in pasteurized liquid egg white (LEW) and to estimate the salmonellosis risk using the baseline model and scenario analysis. Samples were inoculated with six strains of Salmonella, [...] Read more.
The objective of the study was to develop a predictive model of Salmonella spp. growth in pasteurized liquid egg white (LEW) and to estimate the salmonellosis risk using the baseline model and scenario analysis. Samples were inoculated with six strains of Salmonella, and bacterial growth was observed during storage at 10–37 °C. The primary models were developed using the Baranyi model for LEW. For the secondary models, the obtained specific growth rate (μmax) and lag phase duration were fitted to a square root model and Davey model, respectively, as functions of temperature (R2 ≥ 0.98). For μmax, the values were satisfied within an acceptable range (Af, Bf: 0.70–1.15). The probability of infection (Pinf) due to the consumption of LEW was zero in the baseline model. However, scenario analysis suggested possible salmonellosis for the consumption of LEW. Because Salmonella spp. proliferated much faster in LEW than in egg white (EW) during storage at 20 and 30 °C (p < 0.01), greater Pinf may be obtained for LEW when these products are stored at the same conditions. The developed predictive model can be applied to the risk management of Salmonella spp. along the food chain, including during product storage and distribution. Full article
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Open AccessArticle
Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes
Microorganisms 2020, 8(11), 1772; https://doi.org/10.3390/microorganisms8111772 - 11 Nov 2020
Viewed by 464
Abstract
The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial [...] Read more.
The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes. Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA. Full article
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