ReviewFoods (Basel, Switzerland)2025
Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods.
Review in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hybrid Intelligence for Fouling Prediction and Adaptive Clean-in-Place Optimization in Food Processing Heat Exchangers: A Systematic Review.Journal of food science · 2026Pooled it
- From Food Additives and Contaminants to the Human Exposome: A Precision Toxicology Framework for Next-Generation Food Safety.Foods (Basel, Switzerland) · 2026Article
- Artificial intelligence driven diet-intestinal microbiota-host health integration: A four-dimensional paradigm for advancing host wellness research.iMetaOmics · 2026Review
- Fungal Growth Risk Prediction and Optimal Regulation Method for Food Storage Based on the Forward Reachable Set.Foods (Basel, Switzerland) · 2026Article
- A Review of Machine Learning and AI Applications in Enhancing HACCP Systems for Ice Cream Manufacturing.Foods (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Postharvest Food Safety Control of Animal-Source Foods: Evidence Thresholds, Validation, and Regulatory Applicability.Veterinary sciences · 2026Review
- Mechanistic Insights Into Color and Texture Stability of Frozen Tropical Vegetables: Processing, Modeling, and Preservation Strategies.Food science & nutrition · 2026Review
- Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction.Food chemistry: X · 2026Review
- Machine learning prioritization of antibiotic residues in aquatic foods reveals exposure-driven genotoxic risk mediated by BCL2.Food chemistry: X · 2026Article
- Intelligent Discrimination of Grain Aging Using Volatile Organic Compound Fingerprints and Machine Learning: A Comprehensive Review.Foods (Basel, Switzerland) · 2026Review
- Next-Generation Strategies for Controlling Foodborne Pathogens: Precision Antimicrobials, Biofilm Disruption, and Emerging Molecular Interventions.Foods (Basel, Switzerland) · 2026Review
- Digital technologies for sustainable food supply chains: a scoping review of impacts on food safety, loss reduction, and implications for nutritional security.Frontiers in nutrition · 2026Review
- Application of Machine Learning in Food Safety Risk Assessment.Foods (Basel, Switzerland) · 2025Review
- Green carbon dots in the era of AI: sustainable synthesis, intelligent drug delivery, advanced diagnostics, and bioimaging.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating advanced computing techniques into food safety management has attracted significant attention recently. Machine learning (ML) algorithms offer innovative solutions for Hazard Analysis Critical Control Point (HACCP) monitoring by providing advanced data analysis capabilities and have proven to be powerful tools for assessing the safety of Animal-Source Foods (ASFs). Studies that link ML with HACCP monitoring in ASFs are limited. The present review provides an overview of ML, feature extraction, and selection algorithms employed for food safety. Several non-destructive techniques are presented, including spectroscopic methods, smartphone-based sensors, paper chromogenic arrays, machine vision, and hyperspectral imaging combined with ML algorithms. Prospects include enhancing predictive models for food safety with the development of hybrid Artificial Intelligence (AI) models and the automation of quality control processes using AI-driven computer vision, which could revolutionize food safety inspections. However, handling conceivable inclinations in AI models is vital to guaranteeing reasonable and exact hazard assessments in an assortment of nourishment generation settings. Moreover, moving forward, the interpretability of ML models will make them more straightforward and dependable. Conclusively, applying ML algorithms allows real-time monitoring and predictive analytics and can significantly reduce the risks associated with ASF consumption.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.