Proton transfer reaction-mass spectrometry (PTR-MS) is a spectrometric technique that allows direct injection and analysis of mixtures of volatile compounds. Its coupling with data mining techniques provides a reliable and fast method for the automatic characterization of agroindustrial products. We test the validity of this approach to identify samples of strawberry cultivars by measurements of single intact fruits. The samples used were collected over 3 years and harvested in different locations. Three data mining techniques (random forests, penalized discrimmant analysis and discriminant partial least squares) have been applied to the full PTR-NIS spectra without any preliminary projection or feature selection. We tested the classification models in three different ways (leave-one-out and leave-group-out intemal cross validation, and leaving a full year aside), thereby demonstrating that strawberry cultivars can be identified by rapid non-destructive measurements of single fruits. Performances of the different classification methods are compared. (c) 2006 Elsevier B.V. All rights reserved.
Rapid and non-destructive identification of strawberry cultivars by direct PTR-MS headspace analysis and data mining techniques / Granitto Pablo, M.; Biasioli, Franco; Aprea, E; Mott, Daniela; Furlanello, Cesare; Maerk Tilmann, D.; Gasperi, Flavia. - In: SENSORS AND ACTUATORS. B, CHEMICAL. - ISSN 0925-4005. - 121:2(2007), pp. 379-385. [10.1016/j.snb.2006.03.047]
Rapid and non-destructive identification of strawberry cultivars by direct PTR-MS headspace analysis and data mining techniques
Aprea E;Gasperi Flavia
2007-01-01
Abstract
Proton transfer reaction-mass spectrometry (PTR-MS) is a spectrometric technique that allows direct injection and analysis of mixtures of volatile compounds. Its coupling with data mining techniques provides a reliable and fast method for the automatic characterization of agroindustrial products. We test the validity of this approach to identify samples of strawberry cultivars by measurements of single intact fruits. The samples used were collected over 3 years and harvested in different locations. Three data mining techniques (random forests, penalized discrimmant analysis and discriminant partial least squares) have been applied to the full PTR-NIS spectra without any preliminary projection or feature selection. We tested the classification models in three different ways (leave-one-out and leave-group-out intemal cross validation, and leaving a full year aside), thereby demonstrating that strawberry cultivars can be identified by rapid non-destructive measurements of single fruits. Performances of the different classification methods are compared. (c) 2006 Elsevier B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione