TY - JOUR

T1 - Prospective exploration of hazelnut's unsaponifiable fraction for geographical and varietal authentication

T2 - A comparative study of advanced fingerprinting and untargeted profiling techniques

AU - Torres-Cobos, B.

AU - Quintanilla-Casas, B.

AU - Rovira, M.

AU - Romero, A.

AU - Guardiola, F.

AU - Vichi, S.

AU - Tres, A.

N1 - Publisher Copyright: © 2024 The Author(s)

PY - 2024

Y1 - 2024

N2 - This study compares two data processing techniques (fingerprinting and untargeted profiling) to authenticate hazelnut cultivar and provenance based on its unsaponifiable fraction by GC–MS. PLS-DA classification models were developed on a selected sample set (n = 176). As test cases, cultivar models were developed for “Tonda di Giffoni” vs other cultivars, whereas provenance models were developed for three origins (Chile, Italy or Spain). Both fingerprinting and untargeted profiling successfully classified hazelnuts by cultivar or provenance, revealing the potential of the unsaponifiable fraction. External validation provided over 90 % correct classification, with fingerprinting slightly outperforming. Analysing PLS-DA models' regression coefficients and tentatively identifying compounds corresponding to highly relevant variables showed consistent agreement in key discriminant compounds across both approaches. However, fingerprinting in selected ion mode extracted slightly more information from chromatographic data, including minor discriminant species. Conversely, untargeted profiling acquired in full scan mode, provided pure spectra, facilitating chemical interpretability.

AB - This study compares two data processing techniques (fingerprinting and untargeted profiling) to authenticate hazelnut cultivar and provenance based on its unsaponifiable fraction by GC–MS. PLS-DA classification models were developed on a selected sample set (n = 176). As test cases, cultivar models were developed for “Tonda di Giffoni” vs other cultivars, whereas provenance models were developed for three origins (Chile, Italy or Spain). Both fingerprinting and untargeted profiling successfully classified hazelnuts by cultivar or provenance, revealing the potential of the unsaponifiable fraction. External validation provided over 90 % correct classification, with fingerprinting slightly outperforming. Analysing PLS-DA models' regression coefficients and tentatively identifying compounds corresponding to highly relevant variables showed consistent agreement in key discriminant compounds across both approaches. However, fingerprinting in selected ion mode extracted slightly more information from chromatographic data, including minor discriminant species. Conversely, untargeted profiling acquired in full scan mode, provided pure spectra, facilitating chemical interpretability.

KW - Fingerprinting

KW - Geographical and varietal authentication

KW - Hazelnut

KW - PLS-DA

KW - Unsaponifiable fraction

KW - Untargeted profiling

U2 - 10.1016/j.foodchem.2023.138294

DO - 10.1016/j.foodchem.2023.138294

M3 - Journal article

C2 - 38218156

AN - SCOPUS:85182273252

VL - 441

JO - Food Chemistry

JF - Food Chemistry

SN - 0308-8146

M1 - 138294

ER -