Journal
TALANTA
Volume 116, Issue -, Pages 50-55Publisher
ELSEVIER
DOI: 10.1016/j.talanta.2013.04.043
Keywords
Cheese; Classification; Fatty acid; NIR spectroscopy; Artificial neuronal networks; Ripening time
Categories
Funding
- CONACyT (Mexican Council of Sciences and Technology)
- Spanish MICINN [JCI-2011-09201]
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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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