University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Department of Agricultural Engineering, Cape Coast, Ghana.
Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development, Ghana Department of Hospitality and Tourism Education, Kumasi, Ghana.
Anal Methods. 2022 Dec 1;14(46):4756-4766. doi: 10.1039/d2ay01480g.
Coffee is the most consumed beverage and the second most valuable traded commodity in the world. In this current study, a pocket-sized spectrometer and multivariate analysis were used for rapid authentication of coffee varieties (Arabica and Robusta) in three states to check mislabelling (food fraud). Two main coffee varieties were collected from different locations in Africa. The samples were scanned in the 740-1070 nm wavelength and the spectral data were pre-treated with several methods: mean centering (MC), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD) and standard normal variate (SNV) independently while partial least squares discriminate analysis (PLS-DA), K-nearest neighbour (KNN) and support vector machine (SVM) were used to comparatively build the prediction models for coffee beans (raw, roasted and powdered). The performances of the models were evaluated by using accuracy and efficiency. Among the classification methods developed, the best results were obtained for the following: raw coffee bean SD-SVM had an accuracy of 0.92 and efficiency of 0.82. For roasted coffee beans, SD-KNN had an accuracy of 0.92 and efficiency of 0.87, while for roasted powdered coffee, FD-KNN showed an accuracy of 0.97 and efficiency of 0.97. These finding reveals that for a more accurate differentiation of coffee beans, the roasted powder offers the best results. The obtained results showed that a pocket-sized spectrometer coupled with chemometrics could be employed to provide accurate and rapid authentication of different categories of coffee bean varieties.
咖啡是全球消费最多的饮料和第二大交易商品。在本研究中，使用袖珍光谱仪和多元分析对三种状态下的咖啡品种（阿拉比卡和罗布斯塔）进行快速鉴定，以检查标签错误（食品欺诈）。两种主要的咖啡品种从非洲的不同地区收集。对样品在 740-1070nm 波长下进行扫描，光谱数据分别采用均值中心化（MC）、乘法散射校正（MSC）、一阶导数（FD）、二阶导数（SD）和标准正态变量（SNV）进行预处理，同时采用偏最小二乘判别分析（PLS-DA）、K 最近邻（KNN）和支持向量机（SVM）建立咖啡豆（生豆、烘焙豆和粉豆）的预测模型。通过准确率和效率评估模型的性能。在所开发的分类方法中，以下方法的结果最佳：生豆 SD-SVM 的准确率为 0.92，效率为 0.82。烘焙豆 SD-KNN 的准确率为 0.92，效率为 0.87，而烘焙粉豆 FD-KNN 的准确率为 0.97，效率为 0.97。这些发现表明，为了更准确地区分咖啡豆，烘焙粉提供了最佳结果。结果表明，袖珍光谱仪结合化学计量学可用于提供不同类别咖啡豆品种的准确快速鉴定。
