Evidence map›Paper›PMID 41046557›Full record

ArticleJournal of food science2025

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.

Giulia Leni, Marco Armando De Gregorio, Elena Secomandi, Leilei Zhang, Terenzio Bertuzzi, Marco Trevisan, Giorgia Spigno, Luigi Lucini

Abstract read
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Article in Journal of food science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Giulia LeniDepartment for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy.ORCID https://orcid.org/0000-0001-5461-2967
Marco Armando De GregorioDepartment for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy.
Elena SecomandiDepartment for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy.
Leilei ZhangDepartment for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy.ORCID https://orcid.org/0000-0002-9278-0459
Terenzio BertuzziDepartment of Animal Science, Food and Nutrition, Università Cattolica del Sacro Cuore, Piacenza, Italy.
Marco TrevisanDepartment for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy.
Giorgia SpignoDepartment for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy.ORCID https://orcid.org/0000-0003-1636-6851
Luigi LuciniDepartment for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy.ORCID https://orcid.org/0000-0002-5133-9464

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

GarlicGas Chromatography-Mass SpectrometryMetabolomicsChromatography, High Pressure LiquidFlavonoidsItalyPolyphenolsSolid Phase MicroextractionVolatile Organic CompoundsFlavonoidsPolyphenolsVolatile Organic CompoundsAllium sativum | data fusion | garlic quality | multi‐omics

Identifiers

PMID41046557
PMCPMC12497448

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.