Machine learning-based meat freshness evaluation: principle, pipeline and application.

Area of Science:

• Food Science
• Computer Science
• Artificial Intelligence

Background:

• Traditional meat freshness evaluation methods are slow, costly, and destructive.
• Machine learning (ML) presents a viable alternative for real-time, non-destructive meat quality assessment.
• Accurate meat freshness monitoring is vital for food safety and consumer confidence.

Purpose of the Study:

• To review the principles and applications of ML in meat freshness evaluation.
• To detail the ML-based detection pipeline from data acquisition to model fine-tuning.
• To highlight advancements and challenges in ML for meat quality assessment.

Main Methods:

• Review of key ML algorithms: Principal Component Regression (PCR), Partial Least Squares (PLS), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and neural networks.
• Examination of ML pipeline stages: data acquisition, preprocessing, model selection, and fine-tuning.
• Focus on advanced ML techniques like Convolutional Neural Networks (CNN) and ensemble learning.

Main Results:

• ML algorithms demonstrate effectiveness in real-time, non-destructive meat freshness detection.
• Advanced models like CNNs and ensemble methods show promise in addressing spoilage and complex meat composition.
• The review outlines a comprehensive ML-based detection framework.

Conclusions:

• ML provides a powerful, efficient solution for monitoring meat freshness, enhancing food safety.
• Further research is needed to address challenges like dataset quality and model interpretability for wider adoption.
• Successful implementation of ML will lead to safer, higher-quality meat products for consumers.