Revolutionizing Fresh Food Quality Control in Supply Chains With Machine Learning: Current Advances and Future...

Area of Science:

• Food Science
• Artificial Intelligence
• Supply Chain Management

Background:

• Global fresh food waste is substantial, impacting supply chains.
• Machine learning (ML) presents opportunities to improve postharvest logistics and food preservation.

Purpose of the Study:

• To review advanced ML applications in the food supply chain for quality control.
• To explore ML's potential in integrating multi-omics data for understanding molecular changes.
• To evaluate practical ML applications in grading, sensor technology, and intelligent packaging.

Main Methods:

• Review of current ML applications in postharvest food quality control.
• Analysis of ML models like Support Vector Machine (SVM) and Convolutional Neural Network (CNN).
• Evaluation of ML for multi-omics data integration and sensor development.

Main Results:

• ML enhances food grading, quality prediction, and sensor design through analysis of attributes and composition.
• ML-driven sensors and intelligent packaging improve real-time monitoring and food preservation.
• ML models show transformative potential for reducing spoilage and improving quality control.

Conclusions:

• ML integration in postharvest systems is promising but faces challenges in generalization, transparency, and data availability.
• Addressing ML adoption challenges is crucial for sustainable and effective food quality management.
• Further research is needed to ensure model robustness and widespread application of ML in the food supply chain.