Table grapes classification is an important task in the global market because of the interest of consumers to quality of foodstuff. Objective: an expert and innovative tool, based on several robust classifiers, was designed and implemented to achieve unequivocal criteria and support decision for the discrimination of table grapes. Materials: data are acquired by powerful analytical techniques such as Nuclear Magnetic Resonance (NMR) and are related to 5 attributes: production year, vineyard location, variety, use of plant growth regulators (PGRs) and application of trunk girdling. In particular, datasets consisting of 813 samples regarded the former 3 attributes while datasets based on 596 samples regarded the latter 2 ones. Methods: in absence of an a-priori knowledge, we addressed the problem as an inferential task and then adopted supervised approaches like error back propagation neural networks, trees and random forest classifiers able to manage information from training sets. Experimental Results and Conclusion: our study has shown that the three classifiers, especially that based on a supervised neural network, when applied to NMR data, give from good to excellent performances, depending on the attribute. Such performances pave the way to development of innovative tools for classification of table grapes.

An Expert System for an Innovative Discrimination Tool of Commercial Table Grapes

Bevilacqua, Vitoantonio;Maurizio, Triggiani;Gallo, Vito;Cafagna, Isabella;Mastrorilli, Pietro;
2012

Abstract

Table grapes classification is an important task in the global market because of the interest of consumers to quality of foodstuff. Objective: an expert and innovative tool, based on several robust classifiers, was designed and implemented to achieve unequivocal criteria and support decision for the discrimination of table grapes. Materials: data are acquired by powerful analytical techniques such as Nuclear Magnetic Resonance (NMR) and are related to 5 attributes: production year, vineyard location, variety, use of plant growth regulators (PGRs) and application of trunk girdling. In particular, datasets consisting of 813 samples regarded the former 3 attributes while datasets based on 596 samples regarded the latter 2 ones. Methods: in absence of an a-priori knowledge, we addressed the problem as an inferential task and then adopted supervised approaches like error back propagation neural networks, trees and random forest classifiers able to manage information from training sets. Experimental Results and Conclusion: our study has shown that the three classifiers, especially that based on a supervised neural network, when applied to NMR data, give from good to excellent performances, depending on the attribute. Such performances pave the way to development of innovative tools for classification of table grapes.
Intelligent computing theories and applications, ICIC 2012
978-3-642-31575-6
Springer
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/12946
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