Prediction of the type of milk and degree of ripening in cheeses by means of artificial neural networks with data concerning fatty acids and near infrared spectroscopy

Carlos Soto-Barajas, Milton; Inmaculada Gonzalez-Martin, Ma; Salvador-Esteban, Javier; Miguel Hernandez-Hierro, Jose; Moreno-Rodilla, Vidal; Ma Vivar-Quintana, Ana; Revilla, Isabel; Lobos Ortega, Iris; Moron-Sancho, Raul; Curto-Diego, Belen


The present study addresses the prediction of the time of ripening and type of mixtures of milk (cow's, ewe's and goat's) in cheeses of varying composition using artificial neural networks (ANN). To accomplish this aim, neural networks were designed using as input data the content of 19 fatty acids obtained with GC-FID of the cheese fat and scores obtained from principal component analysis (PCA) of NIR spectra. The best model of neuronal networks for the identification of the type of mixtures of milk was obtained using the information concerning the fatty acid concentration (80% of correct results in the training phase and 75% in the validation phase). Regarding the information of the near-infrared (NIR) spectra a neural network was designed. The aforesaid neural network predicted the ripening of cheeses with 100% accuracy in both training and in validation. (C) 2013 Elsevier B.V. All rights reserved.

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Título según WOS: ID WOS:000328176000009 Not found in local WOS DB
Título de la Revista: TALANTA
Volumen: 116
Editorial: Elsevier
Fecha de publicación: 2013
Página de inicio: 50
Página final: 55


Notas: ISI