2026-09-15T16:57:27Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1599902024-10-08T00:00:53Zcom_10366_137028com_10366_4512com_10366_3823col_10366_137029

Hernández-Jiménez, Miriam Hernández-Ramos, Pedro Martínez-Martín, Iván Vivar Quintana, Ana María González Martín, María Inmaculada Revilla Martín, Isabel 2024-10-07T08:59:07Z 2024-10-07T08:59:07Z 2020 Hernández-Jiménez, M., Hernández-Ramos, P., Martínez-Martín, I., Vivar-Quintana, A. M., González-Martín, I., & Revilla, I. (2020). Comparison of artificial neural networks and multiple regression tools applied to near infrared spectroscopy for predicting sensory properties of products from quality labels. Microchemical Journal, 159, 105459-. https://doi.org/10.1016/j.microc.2020.105459 0026-265X http://hdl.handle.net/10366/159990 10.1016/j.microc.2020.105459 [EN] In products from quality labels a sensory analysis is obligatory although this is a slow and expensive process. This study examines the prediction of the sensory parameters of chorizo dry-cured sausage by using NIRS technology and the application of chemometric methods such as MPLS (Modified Partial Least Square regression) and ANN (Artificial Neural Networks). The results show that by applying ANN it is possible to predict the 20 sensory parameters analyzed with RSQ values of from 0.61 to 0.92; these values are always higher than those obtained by prediction using MPLS. Moreover, the combination of NIRS and RMS-X residual discrimination allowed the correct classification of 94.4% of the samples according to whether or not they belonged to a certain Quality Label. eng info:eu-repo/semantics/openAccess MPLS regression ANN Dry sausage Sensory analysis Discrimination analysis Análisis sensorial Análisis de discriminación Comparison of artificial neural networks and multiple regression tools applied to near infrared spectroscopy for predicting sensory properties of products from quality labels info:eu-repo/semantics/article