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

• Published in: Food chemistry Vol. 118; no. 1; pp. 171 - 176
• Main Authors: Stanimirova, I., Üstün, B., Cajka, T., Riddelova, K., Hajslova, J., Buydens, L.M.C., Walczak, B.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 2010; [Amsterdam]: Elsevier Science; Elsevier
• Subjects: Biological and medical sciences; Classification; Comprehensive two-dimensional gas chromatography; Confectionery products and chocolate industries, honey; discriminant analysis; DPLS; Food analysis; food composition; food contamination; Food industries; Fundamental and applied biological sciences. Psychology; honey; Honey volatiles; LDA; pattern recognition; phytogeography; product authenticity; provenance; SIMCA; Solid phase microextraction; SVM; Time-of-flight mass spectrometry; volatile compounds; Corsica; Solid phase microextraction; Honey volatiles; DPLS; SIMCA; Classification; LDA; SVM; Food analysis; Comprehensive two-dimensional gas chromatography; Time-of-flight mass spectrometry; Volatile compound; Geographical origin; Pattern recognition; Chromatography; Profile; Honey; Comprehensive two-dimensional gas; Analysis; Mass spectrometry; Food
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2009.04.079

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.