Bordiga Matteo, Disca Vincenzo, Manfredi Marcello, Barberis Elettra, Carrà Francesca, Navarini Luciano, Lonzarich Valentina, Arlorio Marco
Department of Pharmaceutical Sciences, University of Piemonte Orientale, Novara, Italy.
Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
Int J Food Sci. 2025 Aug 22;2025:1302823. doi: 10.1155/ijfo/1302823. eCollection 2025.
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 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.
本研究比较了两种非靶向分析技术——顶空气相色谱-离子迁移谱(HS-GC-IMS)和全二维气相色谱-质谱联用(GC×GC-MS)——用于对来自埃塞俄比亚、巴西、尼加拉瓜和危地马拉的青豆中的挥发性有机化合物(VOCs)进行指纹图谱分析。HS-GC-IMS能够快速区分样品,检测到的VOC信号区域能以最少的样品制备有效地按产地对样品进行聚类。GC×GC-MS提供了更高的化学分辨率,鉴定出98种化合物,包括甲氧基吡嗪、醛类和醇类,这些化合物对不同产地间的差异有显著贡献。主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)证实了这两种方法区分地理来源的能力,层次聚类突出了特定区域的VOC模式。HS-GC-IMS被证明对高通量筛选有效,而GC×GC-MS则为潜在的香气前体提供了分子层面的见解。总之,这些平台为生咖啡豆的认证和质量控制提供了一种互补的方法。