Meta-Analysis, Predictive Microbiology and New Technologies in “Intelligent” Food Risk Assessment
A special issue of Applied Microbiology (ISSN 2673-8007).
Deadline for manuscript submissions: 31 December 2026 | Viewed by 256
Editors
Interests: microbiolobical food safety; predictive microbiology; biopreservation; quantitative risk assessment; meta-analysis; LLM; AI; databases
Special Issues, Collections and Topics in MDPI journals
Interests: microbiolobical food safety; predictive microbiology; biopreservation; quantitative risk assessment; meta-analysis; LLM; AI; databases
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Risk assessment of foodborne pathogens is shifting towards a proactive, data-integrated ecosystem, supported by reuse of data and knowledge, AI and Machine Learning (ML), genetic precision, and real-time IoT-enabled monitoring. The integration of existing knowledge of pathogens’ occurrence and behaviour through meta-analysis and predictive microbiology is a powerful approach in the development of accurate quantitative microbial risk assessment (QMRA) models, which can be even further supported by new technologies such as WGS, AI, ML and IoT-enabled monitoring, making it possible to address strain-level tracking, analysis of high-dimensional data, real-time monitoring of cold chains, and early warning systems. The Special Issue, “Meta-Analysis, Predictive Microbiology and New Technologies in “Intelligent” Food Risk Assessment”, welcomes reviews and original research on novel QMRA models aided by (i) the sound integration of literature data and models through meta-analysis and predictive microbiology, (ii) the handling of multisectoral or high dimensional data through AI/ML, (iii) the use of ML algorithms that identify emerging risks in the global supply chain, (iv) the use of WGS and metagenomics data to “fingerprint” pathogens and link them to virulence genes and antimicrobial resistance, and/or (v) the integration of cold-chain data from IoT and Blockchain. The Special Issue also targets intelligent risk assessment models developed within the One Health approach, addressing multiple environmental factors or “stressors”, the transfer of antimicrobial resistance (AMR), and the challenges of breaking down “sectoral data silos” (food scientists, veterinarians, ecologists, and medical professionals).
Prof. Dr. Ursula Gonzales-Barron
Prof. Dr. Vasco Cadavez
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Microbiology 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 1200 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
- data integration
- real-time data
- computational microbiology
- microbial risk assessment
- One Health
- WGS
- metagenomics
- AMR
- AI
- ML
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