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Updated: Apr 24, 2026

Fruit Volatile Analysis Using an Electronic Nose
Published on: March 30, 2012
Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with
C Cevoli1, L Cerretani2, A Gori3
1Agricultural Economics and Engineering Department, University of Bologna, Piazza G. Goidanich, 60 - 47521 Cesena (FC), Italy.
Abstract:
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.
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