TY - JOUR
T1 - Fingerprinting of Green Arabica Coffee Volatile Organic Compounds (VOCs)
T2 - HS-GC-IMS Versus GC × GC-MS
AU - Bordiga, Matteo
AU - Disca, Vincenzo
AU - Manfredi, Marcello
AU - Barberis, Elettra
AU - Carrà, Francesca
AU - Navarini, Luciano
AU - Lonzarich, Valentina
AU - Arlorio, Marco
N1 - Publisher Copyright:
Copyright © 2025 Matteo Bordiga et al. International Journal of Food Science published by John Wiley & Sons Ltd.
PY - 2025
Y1 - 2025
N2 - This study compared two nontargeted analytical techniques—headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) and comprehensive two-dimensional gas chromatography–mass spectrometry (GC × GC-MS)—to fingerprint the volatile organic compounds (VOCs) of green Coffea arabica beans from Ethiopia, Brazil, Nicaragua, and Guatemala. HS-GC-IMS enabled rapid differentiation of samples, detecting VOC signal regions that effectively clustered samples by origin with minimal preparation. GC × GC-MS offered higher chemical resolution, identifying 98 compounds, including methoxypyrazines, aldehydes, and alcohols, which significantly contributed to interorigin variability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the capacity of both methods to distinguish geographical origins, with hierarchical clustering highlighting region-specific VOC patterns. HS-GC-IMS proved efficient for high-throughput screening, while GC × GC-MS provided molecular insights into potential aroma precursors. Together, these platforms offer a complementary approach to green coffee authentication and quality control.
AB - This study compared two nontargeted analytical techniques—headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) and comprehensive two-dimensional gas chromatography–mass spectrometry (GC × GC-MS)—to fingerprint the volatile organic compounds (VOCs) of green Coffea arabica beans from Ethiopia, Brazil, Nicaragua, and Guatemala. HS-GC-IMS enabled rapid differentiation of samples, detecting VOC signal regions that effectively clustered samples by origin with minimal preparation. GC × GC-MS offered higher chemical resolution, identifying 98 compounds, including methoxypyrazines, aldehydes, and alcohols, which significantly contributed to interorigin variability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the capacity of both methods to distinguish geographical origins, with hierarchical clustering highlighting region-specific VOC patterns. HS-GC-IMS proved efficient for high-throughput screening, while GC × GC-MS provided molecular insights into potential aroma precursors. Together, these platforms offer a complementary approach to green coffee authentication and quality control.
KW - Coffea arabica
KW - GC × GC-MS
KW - HS-GC-IMS
KW - VOCs
KW - chemometrics
KW - coffee authentication
UR - https://www.scopus.com/pages/publications/105013997368
U2 - 10.1155/ijfo/1302823
DO - 10.1155/ijfo/1302823
M3 - Article
SN - 2356-7015
VL - 2025
JO - International Journal of Food Science
JF - International Journal of Food Science
IS - 1
M1 - 1302823
ER -