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Machine Learning for Quality Control in the Food Industry: A Review

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Publicado en:Foods vol. 14, no. 19 (2025), p. 3424-3459
Autor principal: Liakos, Konstantinos G
Otros Autores: Athanasiadis Vassilis, Bozinou Eleni, Lalas, Stavros I
Publicado:
MDPI AG
Materias:
Food & Drug Administration > FDA
United States > US
Nutrition assessment
Quality control
Defects
Supervised learning
Optimization
Industry 4.0
Food quality
Food production
ISO standards
Machine learning
Explainable artificial intelligence
Learning algorithms
Food industry
Good Manufacturing Practice
Adaptive systems
Embedded systems
Neural networks
Artificial intelligence
Genetic algorithms
Sensors
Classification
Industrial applications
Supply chains
Ensemble learning
Multisensor fusion
Hyperspectral imaging
Acceso en línea:Citation/Abstract
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Resumen:The increasing complexity of modern food production demands advanced solutions for quality control (QC), safety monitoring, and process optimization. This review systematically explores recent advancements in machine learning (ML) for QC across six domains: Food Quality Applications; Defect Detection and Visual Inspection Systems; Ingredient Optimization and Nutritional Assessment; Packaging—Sensors and Predictive QC; Supply Chain—Traceability and Transparency and Food Industry Efficiency; and Industry 4.0 Models. Following a PRISMA-based methodology, a structured search of the Scopus database using thematic Boolean keywords identified 124 peer-reviewed publications (2005–2025), from which 25 studies were selected based on predefined inclusion and exclusion criteria, methodological rigor, and innovation. Neural networks dominated the reviewed approaches, with ensemble learning as a secondary method, and supervised learning prevailing across tasks. Emerging trends include hyperspectral imaging, sensor fusion, explainable AI, and blockchain-enabled traceability. Limitations in current research include domain coverage biases, data scarcity, and underexplored unsupervised and hybrid methods. Real-world implementation challenges involve integration with legacy systems, regulatory compliance, scalability, and cost–benefit trade-offs. The novelty of this review lies in combining a transparent PRISMA approach, a six-domain thematic framework, and Industry 4.0/5.0 integration, providing cross-domain insights and a roadmap for robust, transparent, and adaptive QC systems in the food industry.
ISSN:2304-8158
DOI:10.3390/foods14193424
Fuente:Agriculture Science Database