Truace
Subscribe

Review of 25 studies maps how machine learning is used for food quality control

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

On 2025-10-04, a peer-reviewed review in Foods synthesized 25 studies selected from 124 Scopus records from 2005-2025 to map machine learning use for quality control in food production. It organized findings into six domains covering quality applications, defect detection and visual inspection, ingredient optimization, packaging sensors and predictive QC, supply chain traceability, and Industry 4.0 models.

Read the study abstract

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…

By the Truvace desk1 min read1 sourceEvidence: Moderate · 70/100 — Systematic review or meta-analysis · n = 25 · 1 independent sourcePeer-reviewedTRV-2026-0481· cite & verify
Machine Learning for Quality Control in the Food Industry: A Review
"Homemade strawberry Nilla Wafer ice cream. Yes, I had a small bowl for quality control purposes only." by amberdegrace is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.
Trace impact readingContested
P 63The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 62The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

The synthesis matters because it shows where AI is already applied to food safety and efficiency and where adoption stalls. While neural networks and sensor fusion show promise, the review itself flags persistent gaps in data availability, uneven domain coverage, and practical barriers like legacy system integration and regulatory compliance that leave scalability uncertain.

Main points

  1. Review used PRISMA-based methodology and structured search of Scopus database identifying 124 peer-reviewed publications from 2005-2025, selecting 25 studies.
  2. Six domains analyzed: 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.
  3. Emerging trends identified include hyperspectral imaging, sensor fusion, explainable AI, and blockchain-enabled traceability.

The gain

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.

The problem

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.

The rundown

The review followed a PRISMA-based methodology with thematic Boolean keywords in Scopus, screening 124 publications from 2005-2025 down to 25 studies based on rigor and innovation. Supervised learning prevailed across tasks, with neural networks as the dominant approach and ensemble learning secondary.

Authors frame novelty as combining transparent PRISMA approach, six-domain thematic framework, and Industry 4.0/5.0 integration to provide cross-domain insights and a roadmap for robust, transparent, and adaptive QC systems.

What this doesn’t fix

Current research limited by domain coverage biases, data scarcity, and underexplored unsupervised and hybrid methods, with implementation hindered by legacy integration and compliance costs.

Sources

  1. Peer-reviewedFoods2025-10-04

The debate