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Predictive Modelling in Food: Food Safety Validation and Risk Assessment
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Predictive Modelling in Food: Food Safety Validation and Risk Assessment

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3968

Special Issue Editors

Prof. Dr. Ursula Andrea Gonzales-Barron

E-Mail Website
Guest Editor
CIMO Mountain Research Center, School of Agriculture, Polytechnic Institute of Bragança, Santa Apolónia Campus, 5300-253 Bragança, Portugal
Interests: predictive microbiology; biopreservation; quantitative risk assessment; meta-analysis; statistical quality control; Bayesian applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qingli Dong

E-Mail Website
Guest Editor
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: risk assessment; predictive microbiology; risk modelling; software development; foodborne pathogen; food bacterial safety; risk control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Predictive modelling of microbial behaviour has been long understood as a body of tools that combines data and mathematics to forecast the level of safety of food products and their processing environments and conditions. This Special Issue, “Predictive Modelling in Food: Food Safety Validation and Risk Assessment”, will showcase the pivotal role that predictive modelling exerts (1) on food safety validation, by providing estimates of the expected performance of a new/intervened treatment, control measure, or system and (2) on risk assessment, by facilitating quantitative risk assessments and addressing emerging risks.

This Special Issue intends to be a collection of current modern applications of predictive modelling, such as the (early) identification of potential food safety risks, quantitative risk assessment in the context of One Health and molecular epidemiology, efficient allocation of resources by targeting high-risk areas, data-driven decision-making tools for food business operators, food safety process validation, and optimisation of food safety interventions. The Editors also welcome communications that propose strategies for increasing accessibility to data and models; for increasing the wide applicability of models through data science; and for facilitating the visualisation of complex models and results to stakeholders and regulators.

Prof. Dr. Ursula Gonzales-Barron
Prof. Dr. Qingli Dong
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 microbiology
  • process validation
  • process deviation
  • product design
  • food safety interventions
  • decision-making tool
  • dashboards
  • data science
  • ML
  • AI

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Published Papers (3 papers)

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12 pages, 670 KiB  
Article
Growth Rate Determination of Listeria monocytogenes in Ready-To-Eat Fish Products Under Different Storage Conditions for Possible Shelf-Life Extension
by Paolo Cipriani, Elena Dalzini, Elena Cosciani-Cunico, Muhammad-Ehtesham Abdul, Paola Monastero, Daniela Merigo, Stefania Ducoli, Alessandro Norton, Marina-Nadia Losio and Enrico Pavoni
Foods 2025, 14(5), 777; https://doi.org/10.3390/foods14050777 - 25 Feb 2025
Cited by 1 | Viewed by 1466
Abstract
An increasing trend among food business operators (FBOs) to extend the shelf life of Ready-To-Eat (RTE) fish products over 5 days, the duration usually assigned to this kind of product, has been observed recently. In this study, three independent challenge tests (food artificial [...] Read more.
An increasing trend among food business operators (FBOs) to extend the shelf life of Ready-To-Eat (RTE) fish products over 5 days, the duration usually assigned to this kind of product, has been observed recently. In this study, three independent challenge tests (food artificial contamination) were performed on tuna fillet, marinated salmon tartare, and cubed salmon, with the aim of calculating the maximum growth rate (Vmax) of Listeria monocytogenes and estimating the time required to reach the legal limit of 2 log CFU/g, as established by European Regulation 2073/2005. The pathogen counts were fitted by the model of Baranyi and Roberts to calculate the Vmax, which were 0.041, 0.020, and 0.039 log CFU/g·h−1, respectively, for the tuna fillet, marinated salmon tartare, and cubed salmon at 10 °C. These results can help FBOs in assigning the correct shelf life based on hygienic practices during the process, product characteristics, and storage conditions. The time to reach the legal limit greatly depends on the starting concentration of the pathogen and on the storage temperature. The challenges for FBOs and the health authorities include reducing the contamination of L. monocytogenes, controlling the retail temperatures, and implementing the analytical tests for quick responses. Full article
(This article belongs to the Special Issue Predictive Modelling in Food: Food Safety Validation and Risk Assessment)
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14 pages, 935 KiB  
Systematic Review
The Global Prevalence of Bacillus spp. in Milk and Dairy Products: A Systematic Review and Meta-Analysis
by Tianmei Sun, Ran Wang, Yanan Sun, Xiaoxu Zhang, Chongtao Ge and Yixuan Li
Foods 2025, 14(15), 2599; https://doi.org/10.3390/foods14152599 - 24 Jul 2025
Viewed by 268
Abstract
The spoilage of dairy products and foodborne diseases caused by Bacillus spp. are important public concerns. The objective of this study was to estimate the global prevalence of Bacillus spp. in a range of milk and dairy products by using a meta-analysis of [...] Read more.
The spoilage of dairy products and foodborne diseases caused by Bacillus spp. are important public concerns. The objective of this study was to estimate the global prevalence of Bacillus spp. in a range of milk and dairy products by using a meta-analysis of literature data published between 2001 and 2023. A total of 3624 publications were collected from Web of Science and PubMed databases. Following the principles of systematic review, 417 sets of prevalence data were extracted from 142 eligible publications. Estimated by the random-effects model, the overall prevalence of Bacillus spp. in milk and dairy products was 11.8% (95% CI: 10.1–13.7%), with highly severe heterogeneity (94.8%). Subgroup analyses revealed substantial heterogeneity in Bacillus spp. prevalence according to geographical continents, sources of sampling, types of dairy products, microbial species, and detection methods. The prevalence of Bacillus spp. was highest in Asia (15.4%, 95% CI: 12.3–19.1%), lowest in Oceania (3.5%, 95% CI: 3.3–3.7%) and generally higher in developing versus developed countries. The prevalence of Bacillus spp. isolated from retail markets (16.1%, 95% CI: 13.0–19.7%) was higher than from farms (10.3%, 95% CI: 6.9–15.0%) or dairy plants (9.2%, 95% CI: 7.1–12.0%). This finding is likely attributable to its inherent characteristic of the resistant endospores and ubiquitous presence in the environment—Bacillus spp. can potentially cyclically contaminate farms, dairy products and human markets. Regarding the species distribution, Bacillus cereus presented a cosmopolitan distribution across all continents. The epidemic patterns of different Bacillus species vary depending on the sample sources. In addition, the detection method utilized also affected the reported prevalence of Bacillus spp. It is recommended to use molecular-based rapid detection methods to obtain a more accurate prevalence of Bacillus contamination. Therefore, a better understanding of variations in Bacillus spp. prevalence across different factors will enable competent authorities, industries, and other relevant stakeholders to tailor their interventions for effectively controlling Bacillus spp. in milk and dairy products. Full article
(This article belongs to the Special Issue Predictive Modelling in Food: Food Safety Validation and Risk Assessment)
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24 pages, 2745 KiB  
Systematic Review
A Meta-Analysis on the In Vitro Antagonistic Effects of Lactic Acid Bacteria from Dairy Products on Foodborne Pathogens
by Yara Loforte, Nathália Fernandes, André Martinho de Almeida, Vasco Cadavez and Ursula Gonzales-Barron
Foods 2025, 14(6), 907; https://doi.org/10.3390/foods14060907 - 7 Mar 2025
Viewed by 1342
Abstract
Raw milk and traditional fermented foods such as artisanal cheese represent a natural source of lactic acid bacteria (LAB). They can produce antimicrobial compounds, such as bacteriocins and lactic acid, which may be exploited in dairy biopreservation. This study aimed to conduct a [...] Read more.
Raw milk and traditional fermented foods such as artisanal cheese represent a natural source of lactic acid bacteria (LAB). They can produce antimicrobial compounds, such as bacteriocins and lactic acid, which may be exploited in dairy biopreservation. This study aimed to conduct a systematic review and meta-analysis to synthesize the inhibition diameter (ID) of LAB against L. monocytogenes, S. aureus, and Salmonella spp. Literature electronic searches were performed on PubMed, Scopus, and Web of Science, to identify articles that reported data on in-vitro antimicrobial activity by LAB isolated from dairy foods. A total of 1665 papers were retrieved, and 20 primary studies were selected according to the selection criteria, of which 397 observations were extracted. Random-effects meta-regression models were employed to describe the effects of LAB genus, pathogen concentration, susceptibility method, incubation time, inoculation volume, agar type and pH on the IDs for L. monocytogens, S. aureus, and Salmonella spp. L. monocytogens was the most susceptible pathogen (p < 0.05) to the LAB effects, followed by S. aureus and Salmonella spp. As a whole, LAB from the Lacticaseibacillus genus were the most effective (p < 0.05) in inhibiting L. monocytogens (21.49 ± 2.654 mm), followed by S. aureus (21.06 ± 2.056 mm). Salmonella spp. presented higher (p < 0.05) susceptibility to Lactobacillus genus (19.93 ± 2.456 mm). From the results, a general trend could be observed for the well-diffusion method to produce higher (p < 0.05) ID estimates than the spot and disk methods (30.73 ± 2.530 mm vs. 21.98 ± 1.309 mm vs. 13.39 ± 1.403 mm for L. monocytogenes; 22.37 ± 1.073 mm vs. 14.91 ± 2.312 mm vs. 20.30 ± 2.319 mm for Salmonella spp.), respectively. Among the tested moderators, the pathogen’s inoculum concentration, the in vitro susceptibility assay itself, incubation time and inoculation volume on agar are determinant parameters to be looked at when designing a robust and reproducible experimental plan. The in vitro results reinforced that LAB can be useful in controlling the development of pathogenic bacteria frequently found in the dairy industry. Full article
(This article belongs to the Special Issue Predictive Modelling in Food: Food Safety Validation and Risk Assessment)
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