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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Machine Learning for Quality Control in the Food Industry: A Review.

Konstantinos G Liakos1, Vassilis Athanasiadis2, Eleni Bozinou2

  • 1Department of Electrical and Computer Engineering, University of Thessaly, Sekeri Street, 38334 Volos, Greece.

Foods (Basel, Switzerland)
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Summary

Machine learning (ML) enhances food quality control (QC) and safety. This review highlights ML advancements in six food industry domains, focusing on neural networks and emerging trends for adaptive QC systems.

Keywords:
Industry 4.0defect detectionexplainable AIfood quality controlfood traceabilityingredient optimizationmachine learningpredictive analyticssensor-based inspectionsmart packaging

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Area of Science:

  • Food Science and Technology
  • Artificial Intelligence
  • Industrial Engineering

Background:

  • Modern food production requires advanced quality control (QC) and safety monitoring.
  • Machine learning (ML) offers potential solutions for optimizing food production processes.

Purpose of the Study:

  • To systematically review recent advancements in ML for QC in the food industry.
  • To explore ML applications across six key domains of food production.
  • To identify emerging trends and challenges in implementing ML for food QC.

Main Methods:

  • A PRISMA-based systematic literature review was conducted.
  • Scopus database was searched for peer-reviewed publications (2005-2025).
  • 25 studies were selected based on inclusion criteria, rigor, and innovation.

Main Results:

  • Neural networks were the dominant ML approach, followed by ensemble learning.
  • Supervised learning methods were prevalent across various QC tasks.
  • Emerging trends include hyperspectral imaging, sensor fusion, explainable AI, and blockchain.

Conclusions:

  • ML, particularly neural networks, shows significant promise for improving food QC and safety.
  • Challenges remain in data scarcity, implementation, and integration with existing systems.
  • Further research into unsupervised/hybrid methods and Industry 4.0/5.0 integration is needed for robust QC systems.