Machine Learning for Quality Control in the Food Industry: A Review
Sparad:
| I publikationen: | Foods vol. 14, no. 19 (2025), p. 3424-3459 |
|---|---|
| Huvudupphov: | |
| Övriga upphov: | , , |
| Utgiven: |
MDPI AG
|
| Ämnen: | |
| Länkar: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Taggar: |
Inga taggar, Lägg till första taggen!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3261075778 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2304-8158 | ||
| 024 | 7 | |a 10.3390/foods14193424 |2 doi | |
| 035 | |a 3261075778 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231462 |2 nlm | ||
| 100 | 1 | |a Liakos, Konstantinos G |u Department of Electrical and Computer Engineering, University of Thessaly, Sekeri Street, 38334 Volos, Greece; kliakos@uth.gr | |
| 245 | 1 | |a Machine Learning for Quality Control in the Food Industry: A Review | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 610 | 4 | |a Food & Drug Administration--FDA | |
| 651 | 4 | |a United States--US | |
| 653 | |a Nutrition assessment | ||
| 653 | |a Quality control | ||
| 653 | |a Defects | ||
| 653 | |a Supervised learning | ||
| 653 | |a Optimization | ||
| 653 | |a Industry 4.0 | ||
| 653 | |a Food quality | ||
| 653 | |a Food production | ||
| 653 | |a ISO standards | ||
| 653 | |a Machine learning | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Food industry | ||
| 653 | |a Good Manufacturing Practice | ||
| 653 | |a Adaptive systems | ||
| 653 | |a Embedded systems | ||
| 653 | |a Neural networks | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Sensors | ||
| 653 | |a Classification | ||
| 653 | |a Industrial applications | ||
| 653 | |a Supply chains | ||
| 653 | |a Ensemble learning | ||
| 653 | |a Multisensor fusion | ||
| 653 | |a Hyperspectral imaging | ||
| 700 | 1 | |a Athanasiadis Vassilis |u Department of Food Science and Nutrition, University of Thessaly, Terma N. Temponera Street, 43100 Karditsa, Greece; vaathanasiadis@uth.gr (V.A.); empozinou@uth.gr (E.B.) | |
| 700 | 1 | |a Bozinou Eleni |u Department of Food Science and Nutrition, University of Thessaly, Terma N. Temponera Street, 43100 Karditsa, Greece; vaathanasiadis@uth.gr (V.A.); empozinou@uth.gr (E.B.) | |
| 700 | 1 | |a Lalas, Stavros I |u Department of Food Science and Nutrition, University of Thessaly, Terma N. Temponera Street, 43100 Karditsa, Greece; vaathanasiadis@uth.gr (V.A.); empozinou@uth.gr (E.B.) | |
| 773 | 0 | |t Foods |g vol. 14, no. 19 (2025), p. 3424-3459 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3261075778/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3261075778/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3261075778/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |