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Combined SPME—GC × GC–MS Volatilomics and UHPLC–HRMS Metabolomics Data Fusion Provides a Multidimensional Fingerprinting and Discrimination of White Garlic From the Piacenza Region

  • University of Pavia

Research output: Contribution to journalArticle

Abstract

Garlic quality is attributed to a diverse array of volatile and non-volatile compounds, and several varieties are protected under geographical indication schemes, whereas many typical local cultivars remain chemically uncharacterized. Among them, white garlic from Piacenza (Italy) is a traditional product with recognized sensory attributes. In this study, a comprehensive characterization of the volatile and non-volatile profiles of this variety was performed, followed by its discrimination against other European garlic cultivars. To achieve this, an integrated foodomics approach, based on both ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) and solid-phase microextraction-bi-dimensional gas chromatography coupled with mass spectrometry (SPME-GC × GC-MS) analyses, was applied, along with chemometric modeling and data fusion with a DIABLO framework. A total of 89 volatile and over 2300 non-volatile compounds were identified. Piacenza garlic exhibited a significantly higher accumulation of aldehydes and revealed a diversity of organosulfur compounds, polyphenols, dipeptides, and flavonoids. Chemometrics highlighted the presence of chemical clusters that distinguished the Piacenza variety from the others. These findings were also confirmed by the data fusion approach, which identified specific aldehydes, sulfur, and phenolic compounds as key discriminant molecules. This comprehensive chemical fingerprinting supports the valorization of Piacenza white garlic. In addition, the proposed approach provides a valuable tool for the characterization and authentication of agri-food products.
Original languageEnglish
Pages (from-to)N/A-N/A
JournalJournal of Food Science
Volume90
Issue number10
DOIs
Publication statusPublished - 2025

All Science Journal Classification (ASJC) codes

  • Food Science

Keywords

  • Allium sativum
  • data fusion
  • garlic quality
  • multi‐omics

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