Rapid and non-destructive identification of strawberry cultivars by direct PTR-MS headspace analysis and data mining techniques

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 discriminant analysis and discriminant partial least squares) have been applied to the full PTR-MS spectra without any preliminary projection or feature selection. We tested the classification models in three different ways (leave-one-out and leave-group-out internal 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.