Examinando por Materia "Hyperspectral imaging"

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Implementing artificial intelligence to measure meat quality parameters in local market traceability processes
(John Wiley & Sons Inc., 2024-09-20) Alvarez Garcia, Wuesley Yusmein; Mendoza, Laura; Muñoz Vílchez, Yudith Yohany; Casanova Núñez-Melgar, David; Quilcate Pairazaman, Carlos
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.
Mango varietal discrimination using hyperspectral imaging and machine learning
(Springer Nature, 2024-07-29) Castro, Wilson; Tene, Baldemar; Castro, Jorge; Guivin, Alex; Ruesta Campoverde, Nelson Asdrubal; Avila George, Himer
Mango is a highly diverse tropical fruit with numerous varieties that differ in flavor, texture, and chemical composition. Consequently, identifying fraudulent substitutions of mango varieties poses a significant challenge using traditional techniques. Therefore, there is an increasing need for new methods to discriminate between mango varieties. Hyperspectral imaging coupled with machine learning techniques presents a promising approach for varietal discrimination. In this study, mango samples of eleven varieties were collected from a germplasm bank, with four slices obtained from each sample. Hyperspectral images were acquired in the Vis–NIR and NIR ranges for each slice, and spectral profiles were extracted and pretreated. Three discrimination models, linear discriminant analysis, K-nearest neighbor, and artificial neural networks, were implemented and validated using relevant wavelengths selected through a covering array feature selection algorithm. The performance of these models was evaluated using precision, accuracy, and F-score metrics. The average spectral profiles of the studied varieties exhibited a similar behavior with slight differences, which could be used for classification within the evaluated ranges. The optimal number of variables selected to refine the models was 17 for the UV–Vis–NIR range and 21 for the NIR range, with an accuracy ranging between 0.752 and 0.972. This study concludes that hyperspectral imaging combined with machine learning techniques can effectively discriminate between different varieties of mango.