aceMachine Learning for Quality Control in the Food Industry: A Review
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 Model…
Machine learning, led by neural networks, provides advanced quality control, safety monitoring, and process optimization across food industry domains including defect detection and predictive QC.
Deployment of ML for food quality control is constrained by data scarcity, domain coverage biases, and challenges integrating with legacy systems while meeting regulatory compliance and cost-benefit requirements.
Current research limited by domain coverage biases, data scarcity, and underexplored unsupervised and hybrid methods, with implementation hindered by legacy integration and compliance costs.
Evidence
- Peer-reviewedFoods2025-10-04
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Truvace Impact Record TRV-2026-0481, v1: “Machine Learning for Quality Control in the Food Industry: A Review.” Truvace, 2026-07-22. /record/TRV-2026-0481 (accessed at citation time). sha256 528e18e11ebb2259…
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