Tracing the geographical origin of honeys based on volatile compounds profiles assessment using pattern recognition techniques

Abstract

Abstract The goal of this study was to examine the possibility of verifying the geographical origin of honeys based on the profiles of volatile compounds. A head-space solid phase microextraction (SPME) combined with comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOFMS) was used to analyze the volatiles in honeys with various geographical and floral origins. Once the analytical data were collected, supervised pattern recognition techniques were applied to construct classification/discrimination rules to predict the origin of samples on the basis of their profiles of volatile compounds. Specifically, linear discriminant analysis (LDA), soft independent modeling of class analogies (SIMCA), discriminant partial least squares (DPLS) and support vector machines (SVM) with the recently proposed Pearson VII universal kernel (PUK) were used in our study to discriminate between Corsican and non-Corsican honeys. Although DPLS and LDA provided models with high sensitivities and specificities, the best performance was achieved by the SVM using PUK. The results of this study demonstrated that GC × GC–TOFMS combined with methods like LDA, DPLS and SVM can be successfully applied to detect mislabeling of Corsican honeys.