Advances in machine learning framework for near-infrared spectroscopy: a taxonomic review on food quality assessment

Key Points

• Machine learning frameworks enhance food quality assessment through near-infrared spectroscopy, improving accuracy and efficiency.
• The review identifies key algorithms that significantly optimize near-infrared data analysis with success metrics highlighted.
• Assessment examines various methodologies in near-infrared spectroscopy for food quality, emphasizing practical applications.
• Implications of incorporating advanced machine learning could transform food safety and quality monitoring processes.