2026-04-14T08:12:53Zhttps://recercat.cat/oai/request

oai:recercat.cat:20.500.12327/27812025-10-22T11:01:13Zcom_2072_4428com_2072_4427col_2072_487898

Prospective exploration of hazelnut’s unsaponifiable fraction for geographical and varietal authentication: A comparative study of advanced fingerprinting and untargeted profiling techniques Torres-Cobos, B. Quintanilla-Casas, B. Rovira, M. Romero, A. Guardiola, F. Vichi, S. Tres, A. Producció Vegetal Fructicultura 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. 2025-10-22T11:01:13Z 2025-10-22T11:01:13Z 2025-10-22T11:01:13Z 2024-01-04 info:eu-repo/semantics/article Torres-Cobos, Berta, Beatriz Quintanilla‐Casas, M. Rovira, Agustí Romero, Francesc Guardiola, Stefania Vichi, and Alba Tres. 2024.“Prospective Exploration of Hazelnut’s Unsaponifiable Fraction for Geographical and Varietal Authentication: A Comparative Study of Advanced Fingerprinting and Untargeted Profiling Techniques.” Food Chemistry 441: 138294. doi:10.1016/j.foodchem.2023.138294. 0308-8146 http://hdl.handle.net/20.500.12327/2781 https://doi.org/10.1016/j.foodchem.2023.138294 eng Food Chemistry MICIU/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I y Programa Estatal de I+D+I orientada a los retos de la sociedad/PID2020-117701RB-I00/ES/Desarrollo de herramientas de detección de fraudes en frutos secos españoles con alto riesgo de falsificación/TRACENUTS MINECO/Programa Estatal de promoción del talento y su empleabilidad en I+D+I/RYC-2017-23601/ES/ / MICINN/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I/CEX2021-001234-M/ES/ / FEDER/ / /EU/ / http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 International Elsevier