期刊
FOOD CHEMISTRY
卷 129, 期 3, 页码 1315-1319出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2011.05.126
关键词
Electronic nose; Artificial neural network; Classification; Volatile compounds; Pecorino cheese
An electronic nose based on an array of 6 metal oxide semiconductor sensors was used, jointly with artificial neural network (ANN) method, to classify Pecorino cheeses according to their ripening time and manufacturing techniques. For this purpose different pre-treatments of electronic nose signals have been tested. In particular, four different features extraction algorithms were compared with a principal component analysis (PCA) using to reduce the dimensionality of data set (data consisted of 900 data points per sensor). All the ANN models (with different pre-treatment data) have different capability to predict the Pecorino cheeses categories. In particular, PCA show better results (classification performance: 100%; RMSE: 0.024) in comparison with other pre-treatment systems. (C) 2011 Elsevier Ltd. All rights reserved.
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