Elefante Arianna, Giglio Marilena, Mongelli Lavinia, Bux Adriana, Zifarelli Andrea, Menduni Giansergio, Patimisco Pietro, Caratti Andrea, Cagliero Cecilia, Liberto Erica, Cordero Chiara, Navarini Luciano, Spagnolo Vincenzo, Sampaolo Angelo
Consiglio Nazionale delle Ricerche (CNR), Istituto di Fotonica e Nanotecnologie, 70126 Bari, Italy.
PolySense Lab, Dipartimento Interateneo di Fisica, University and Politecnico of Bari, Via Amendola 173, 70126 Bari, Italy.
Molecules. 2025 Aug 25;30(17):3487. doi: 10.3390/molecules30173487.
This study aimed at defining the infrared spectral signatures of volatile organic compounds (VOCs) of relevant interest for coffee bean authentication and quality control. Fourier Transform Infrared Spectroscopy was employed to acquire the mid-infrared absorption spectra of some representative coffee markers, namely Pyridine, 2-Methylpyrazine, 2,5-Dimethylpyrazine, Furfural, 5-Methylfurfural and Furfuryl Alcohol, with high resolution of 0.1 cm. Mixtures of these VOCs simulating their amount in coffee seeds were analyzed using multilinear regression. The achieved results demonstrate the potentiality of coffee fingerprinting by VOC's signature in the absorption spectra for discriminating coffee origin.
本研究旨在确定对咖啡豆认证和质量控制具有相关意义的挥发性有机化合物(VOCs)的红外光谱特征。采用傅里叶变换红外光谱法获取了一些代表性咖啡标志物(即吡啶、2-甲基吡嗪、2,5-二甲基吡嗪、糠醛、5-甲基糠醛和糠醇)的中红外吸收光谱,分辨率高达0.1 cm。使用多元线性回归分析了模拟其在咖啡种子中含量的这些VOCs混合物。所取得的结果证明了利用吸收光谱中VOCs特征进行咖啡指纹识别以区分咖啡产地的潜力。