Traceability of honey origin based on volatiles pattern processing by artificial neural networks.
Head-space solid-phase microextraction (HS-SPME)-based procedure, coupled to comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GCxGC-TOF-MS), was employed for fast characterisation of honey volatiles. In total, 374 samples were collected over two production seasons in Corsica (n=219) and other European countries (n=155) with the emphasis to confirm the authenticity of the honeys labelled as "Corsica" (protected denomination of origin region). For the chemometric analysis, artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction (94.5%) and classification (96.5%) abilities of the ANN-MLP model were obtained when the data from two honey harvests were aggregated in order to improve the model performance compared to separate year harvests.
Cajka
Tomas
T
Institute of Chemical Technology, Prague, Faculty of Food and Biochemical Technology, Department of Food Chemistry and Analysis, Technicka 5, 16628 Prague 6, Czech Republic.
Hajslova
Jana
J
Pudil
Frantisek
F
Riddellova
Katerina
K
D005504:Food Analysis / Q000379:methods*; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods; D006722:Honey / Q000032:analysis*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis / Q000379:methods; D015203:Reproducibility of Results; D052617:Solid Phase Microextraction / Q000379:methods; D055549:Volatile Organic Compounds / Q000032:analysis
D055549:Volatile Organic Compounds