AI driven approach and NIRS: A review on meat quality and safety

Abstract

Consumers' increasing demand for high-quality and safe meat products has led the food industry to explore advanced analytical techniques, such as artificial intelligence and nearinfrared reflectance spectroscopy to assess and monitor the quality and safety of meat. The quality and safety of meat products have become increasingly important considerations for consumers, driven by concerns over health implications and the need for transparency in the meat supply chain. The meat industry faces increasing demands from consumers for higher quality and safer products, driven by concerns over food-borne illnesses and the nutritional value of meat. Addressing these growing consumer expectations has become a priority, prompting researchers to actively investigate the potential of innovative nondestructive techniques, including near-infrared spectroscopy and others in combination with advanced data analysis methodologies to enable rapid, objective, and environmentally-friendly assessment of diverse meat quality and safety attributes. This review examines the current state of research on the application of these technologies in the meat industry. Additionally, it explores the integration of artificial intelligence algorithms with near-infrared spectroscopy data to enhance the accuracy and reliability of meat quality prediction and authentication.