Examinando por Autor "Mendoza, Laura"

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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.
Morphological differentiation, yield, and cutting time of Lolium multiflorum L. under acid soil conditions in Highlands
(MDPI, 2024-08-21) Carrasco Chilón, William; Cervantes Peralta, Marieta; Mendoza, Laura; Muñoz Vílchez, Yudith; Quilcate, Carlos; Casanova Nuñez-Melgar, David; Vásquez, Héctor; Alvarez García, Wuesley Yusmein
Livestock production in the basins of the northern macro-region of Peru has as its primary source pastures of Lolium multiflorum L. ‘Cajamarquino ecotype’ (ryegrass CE) in monoculture, or in association with white clover Ladino variety, for feeding. The objective of this research work was the morphological characterisation, yield evaluation, and cutting time evaluation of two local genotypes (LM-58 and LM-43) of Lolium multiflorum L. in six locations. An ANOVA was performed to compare fixed effects and interaction. It was determined that the LM-58 genotype is intermediate, growing semi-erect, with a dark green colouring and 0.8 cm broadleaf, and can reach an average stem length of 46 cm, up to 1.6 cm. day−1, achieving fourth-leaf growth at 28 days under appropriate management conditions. Despite the differentiated characteristics, according to BLASTn evaluation, the ITS1 sequences showed a greater than 99.9% similar identification to Lolium multiflorum L., characterising it as such. It was determined that the LM-58 genotype outperforms LM-43, achieving a forage yield of 4.49 Mg. ha−1, a seed production of 259.23 kg. ha−1, and an average of 13.48% crude protein (CP). The best biomass yield (49.10 Mg. ha−1.yr−1) is reached at 60 days; however, at 30 days, there is a high level of CP (14.84%) and there are no differences in the annual protein production at the cutting age of 60 and 45 days. With the results of the present study, LM-58 from a selection and crossbreeding of 680 ryegrass EC accessions emerges as an elite genotype adapted to the conditions of the northern high Andean zone of Peru.