Decoding the spectrum of meat quality: advances in hyperspectral imaging for multi-attribute analysis

Hyperspectral imaging (HSI) has emerged as a powerful non-destructive technique for evaluating fresh meat quality across multiple attributes simultaneously. This review critically examines recent advances in HSI applications for fresh beef, pork, and poultry, highlighting how HSI decodes key quality parameters such as freshness, intramuscular fat (IMF) content, adulteration, microbial contamination, nutritional composition, and other traits including tenderness, pH, and water-holding capacity. We also cover the fundamental principles and instrumentation of HSI systems. Finally, cutting-edge developments in data analysis, including the integration of artificial intelligence, deep learning, and data fusion, are also discussed in terms of their role in enhancing prediction reliability and enabling real-world implementation. This review provides a comprehensive overview of how HSI is revolutionizing fresh meat quality evaluation and outlines the challenges and opportunities ahead on the path toward industrial adoption.