Discrimination of Arabica and Robusta in instant coffee by Fourier transform infrared spectroscopy and chemometrics
Discrimination of Arabica and Robusta in instant coffee by Fourier transform infrared spectroscopy and chemometrics
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DOI：
10.1021/jf950305a
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发表时间：
1996-01-01
影响因子：
6.1
通讯作者：
Wilson, RH
中科院分区：
文献类型：
--
作者：
Briandet, R;Kemsley, EK;Wilson, RH
有两种咖啡豆在世界范围内具有重要的经济意义：它们是阿拉比卡咖啡和卡尼弗拉变种罗布斯塔咖啡。阿拉比卡咖啡豆在贸易中价值最高，因为它们被认为比罗布斯塔咖啡豆味道更好。在这项工作中，傅里叶变换红外光谱被探索作为湿化学方法的快速替代，用于咖啡产品的认证和定量。利用漫反射红外傅里叶变换（DRIFT）和衰减全反射（ATR）采样技术对速溶咖啡的光谱进行主成分分析（PCA），揭示了不同种类咖啡的聚类特征。主成分得分的线性判别分析对训练和测试样本都产生100%正确的分类。通过对主成分分析载荷的解释，探讨了鉴别的化学起源。偏最小二乘回归应用于阿拉比卡和罗布斯塔混合物的光谱，以确定每个物种的相对含量。内部交叉验证的相关系数为0.99，预测的标准误差为1.20% (w/w)，说明了该方法在工业脱机质量控制分析中的潜力。
Two species of coffee bean have acquired worldwide economic importance: these are, Coffea Arabica and Coffea Canephora variant Robusta. Arabica beans are valued most highly by the trade, as they are considered to have a finer flavor than Robusta. In this work, Fourier transform infrared spectroscopy is explored as a rapid alternative to wet chemical methods for authentication and quantification of coffee products. Principal component analysis (PCA) is applied to spectra of freeze-dried instant coffees, acquired by DRIFT (diffuse reflection infrared Fourier transform) and ATR (attenuated total reflection) sampling techniques, and reveals clustering according to coffee species. Linear discriminant analysis of the principal component scores yields 100% correct classifications for both training and test samples. The chemical origin of the discrimination is explored through interpretation of the PCA loadings. Partial least squares regression is applied to spectra of Arabica and Robusta blends to determine the relative content of each species. Internal cross-validation gives a correlation coefficient of 0.99 and a standard error of prediction of 1.20% (w/w), illustrating the potential of the method for industrial off-line quality control analysis.