On the use of counterpropagation artificial neural networks to characterize Italian rice varieties

• Published in: Analytica chimica acta Vol. 510; no. 2; pp. 231 - 240
• Main Authors: Marini, Federico, Zupan, Jure, Magrı̀, Antonio L.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-05-2004; Elsevier
• Subjects: Analytical chemistry; Chemistry; Chemometrics; Counterpropagation artificial neural networks (CP-ANN); Exact sciences and technology; General, instrumentation; Mapping; Neural networks; Oryza sativa; Pattern recognition; Principal components analysis; Q1; Q2; Rice varieties; Italy; Europe; Pattern recognition; Rice varieties; Counterpropagation artificial neural networks (CP-ANN); Chemometrics; Classification; Neural network; Variety; Rice
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2004.01.009

A counterpropagation artificial neural network (CP-ANN) approach was used to classify 1779 Italian rice samples according to their variety, using physical measurements which are routinely determined for the commercial classification of the product. If compared to the classical Principal Component Analysis, the mapping based on the Kohonen network showed a significantly better representational ability, being able to separate classes which appeared quite undistinguished in the PC space. From the classification and prediction viewpoint, the optimal CP-ANN was able to correctly predict more than 90% of the test set samples.