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

Ensuring the freshness of meat is crucial for food safety and consumer trust. Traditional methods for evaluating meat freshness, such as sensory analysis and chemical assays, are time-consuming, labor-intensive, and destructive. Machine learning (ML) offers a promising alternative by providing real-time, nondestructive solutions for monitoring meat quality, rationalizing the food industry. This review examines the principles and applications of ML in meat freshness evaluation, focusing on key algorithms like Principal Component Regression (PCR), Partial Least Squares (PLS), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and neural networks. It also details the ML-based detection pipeline, covering data acquisition, preprocessing, model selection, and fine-tuning. The paper highlights recent advancements in ML approaches tailored for meat freshness assessment, such as Convolutional Neural Networks (CNN) and ensemble learning models, which have proven effective in tackling spoilage rate, safety concerns, and the complex chemical composition of meat. However, challenges remain, including the need for high-quality datasets and model interpretability. Addressing these challenges will be crucial for the widespread adoption of ML-based solutions in meat freshness detection, ultimately leading to safer and higher-quality food products. SVM and CNN are effective in handling complex data and making reliable predictions.ML shows strong potential for improving meat freshness evaluation accuracy.Future ML should enhance data quality, model accuracy, and interpretability. SVM and CNN are effective in handling complex data and making reliable predictions. ML shows strong potential for improving meat freshness evaluation accuracy. Future ML should enhance data quality, model accuracy, and interpretability.