In this study (open access) the authors used the fingerprint profile of fats and their metabolites to classify the cultivar (as either Tonda di Giffoni, TG, or as non-TG) and origin (either Chile CHL, Spain ESP, Italy ITA or Georgia GEO) of hazelnuts. They propose a systematic workflow (see graphical abstract, below) first examining the Triacylglycerol profiles (TAG) as a screen and then testing the Unsapnifable Fraction (UF), if needed, for further classification.
The reference database was constructed from 309 traceable hazelnut samples collected from 2019 to 2022 directly from producers. Analyses was by gas chromatography–mass spectrometry.
PLS-DA classification models were developed to discriminate hazelnuts by cultivar and origin. The authors report that external validation results demonstrated the suitability of the UF fingerprint as a hazelnut authentication tool. Both tested models showing a high efficiency (>94 %). The correct classification rate of the TAG fingerprinting method was lower (>80 %), but due to its faster analysis time, it is recommended as a complementary screening tool to UF fingerprinting.
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