Master Program in Food Safety, Taipei Medical University, Taipei 11031, Taiwan.
Department of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan.
Sensors (Basel). 2020 Sep 23;20(19):5451. doi: 10.3390/s20195451.
Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400-2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents' specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models' identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.
部分发酵茶，如乌龙茶，是一种在全世界广受欢迎的饮品。为了保护生产者和消费者免受可能的经济损失，防止部分发酵茶的欺诈行为已变得势在必行。本研究采用可见/近红外（VIS/NIR）光谱结合逐步多元线性回归（SMLR）和支持向量机（SVM）方法，利用全可见近红外波长范围（400-2498nm）对来自越南、中国和台湾不同产地的部分发酵茶进行产地鉴别。SMLR 和 SVM 模型均取得了令人满意的结果。使用化学特征特定波长范围内的数据建立的模型与茶的光谱具有高度相关性，而 SMLR 分析在执行 SVM 分析时可提高对茶类和产地的鉴别能力。通过将几种成分的特定波长范围内的数据组合使用，可有效提高 SVM 模型对台湾不同产地的识别准确率。使用最佳 SVM 模型结合茶中成分的特定波长范围，对茶的类型、产地和产区的判别，校准集和预测集的准确率均达到 100%。NIR 可作为一种快速、无损、准确检测茶类、产地和产区的有效工具。
