Hu Yan, Wu Youli, Sun Jie, Geng Jinping, Fan Rongsheng, Kang Zhiliang
College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya'an 625000, China.
Foods. 2022 Aug 5;11(15):2344. doi: 10.3390/foods11152344.
Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS−DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS−DA. MSC_VIP_PLS−DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.
乌龙茶是一种深受人们喜爱的半发酵茶。本研究旨在建立一种基于荧光高光谱技术(FHSI)并结合化学计量学的乌龙茶分类方法。首先,获取了铁观音、本山、毛蟹和黄金桂的光谱数据。然后,采用标准正态变量变换(SNV)和多元散射校正(MSC)进行预处理。主成分分析(PCA)用于数据可视化,并根据霍特林绘制容忍椭圆以去除光谱中的异常值。在偏最小二乘判别分析(PLS−DA)中使用投影变量重要性(VIP)>1进行特征选择。最后,将处理后的光谱数据输入支持向量机(SVM)和PLS−DA。MSC_VIP_PLS−DA是乌龙茶分类的最佳模型。结果表明,使用FHSI能够准确区分这四种乌龙茶,并能够识别影响茶叶分类的关键波长,分别为650.11、660.29、665.39、675.6、701.17、706.31、742.34和747.5纳米。在这些波长下,不同种类的茶叶有显著差异(p<0.05)。本研究可为未来茶叶鉴定提供一种无损且快速的方法。