Implementing artificial intelligence to measure meat quality parameters in local market traceability processes

• Autores: Alvarez García, Wuesley Yusmein, Mendoza, Laura, Muñoz Vílchez, Yudith Yohany, Casanova Núñez-Melgar, David, Quilcate Pairazaman, Carlos
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:null:20.500.12955/2589
• Enlace del recurso: https://hdl.handle.net/20.500.12955/2589; https://doi.org/10.1111/ijfs.17546
• Nivel de acceso: acceso abierto
• Materia: Artificial Intelligence; Computer vision; Hyperspectral imaging; Meat quality; Ohmic; Ultrasound; https://purl.org/pe-repo/ocde/ford#4.02.01; Inteligencia artificial; Multispectral imagery; Imagen multiespectral; Calidad de la carne; Ultrasonido

The application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri-food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products’ production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as the automation of inspection processes, accurate product classification, traceability, and food safety. While the potential of artificial intelligence associated with sensor development in the meat industry is promising, it is crucial to recognize that this is an evolving field. This technology offers innovative solutions that enable efficient, cost effective, and consumer-oriented production. However, it also underlines the urgent need for further research and development of new techniques and tools such as artificial intelligence algorithms, the development of more sensitive and accurate multispectral sensors, advances in computer vision for 3D image analysis and automated detection, and the integration of advanced ultrasonography with other technologies. Also crucial is the development of autonomous robotic systems for the automation of inspection processes, the implementation of real-time monitoring systems for traceability and food safety, and the creation of intuitive interfaces for human-machine interaction. In addition, the automation of sensory analysis and the optimisation of sustainability and energy efficiency are key areas that require immediate attention to address the current challenges in this agri-food and agri-industrial sector, highlighting and emphasising the importance of ongoing innovation in the field.