光谱学与光谱分析, 2017, 37 (6): 1908, 网络出版: 2017-07-10  

激光诱导击穿光谱技术用于抹茶和绿茶粉的快速鉴别

Fast Identification of Matcha and Green Tea Powder with Laser-Induced Breakdown Spectroscopy
作者单位
浙江大学生物系统工程与食品科学学院, 浙江 杭州 310058
摘要
研究利用激光诱导击穿光谱技术结合化学计量学方法快速鉴别抹茶和绿茶粉的可行性。 抹茶与绿茶粉的主要区别在于茶树品种、 栽培管理、 生长时间和加工工艺。 通过采集不同厂家生产的抹茶和不同杀青方式制成的绿茶粉在230~880nm的激光诱导击穿光谱并进行归一化预处理后, 选用主成分分析(PCA), 依据X-variables loadings获取用于鉴别抹茶和绿茶粉的特征波长, 并基于特征波长建立线性判别式分析(LDA)模型。 结果表明: 基于特征波长建立的LDA模型能快速鉴别抹茶和绿茶粉, 4个特征波长分别属于C(Ⅰ) 24794 nm, Mg(Ⅱ) 27960 nm, Ca(Ⅱ) 39345 nm和Fe(Ⅱ) 76668 nm; 建模集和预测集的判别正确率均达到100%。 采用激光诱导击穿光谱技术可以准确鉴别不同厂家生产的抹茶和不同杀青方式制成的绿茶粉。
Abstract
The feasibility of using laser-induced breakdown spectroscopy (LIBS) combined with chemometrics methods for fast identification of matcha and green tea powder was investigated in this paper. The main differences between matcha and green tea powder are varieties of tea plants, cultivation management, growth time and processing technology. LIBS spectra between 230 to 880 nm of matcha produced by different manufacturers and green tea powder made with different fixation methods were selected and min-max normalization was the measure for preprocessing. Characteristic wavelengths were selected according to the X-variables loadings on the basis of principal component analysis (PCA), and then linear discriminant analysis (LDA) models were built based on characteristic wavelengths. The results showed that the LDA model based on characteristic wavelengths could identify matcha and green tea powder rapidly. Four characteristic wavelengths belong to C(Ⅰ) 24794 nm, Mg(Ⅱ) 27960 nm, Ca(Ⅱ) 39345 nm and Fe(Ⅱ) 76668 nm. The accuracy of the calibration and the prediction set all reached 100%. Laser-induced breakdown spectroscopy could accurately identify matcha produced by different manufacturers and green tea powder made with different fixation methods.

於筱岚, 彭继宇, 刘飞, 何勇. 激光诱导击穿光谱技术用于抹茶和绿茶粉的快速鉴别[J]. 光谱学与光谱分析, 2017, 37(6): 1908. YU Xiao-lan, PENG Ji-yu, LIU Fei, HE Yong. Fast Identification of Matcha and Green Tea Powder with Laser-Induced Breakdown Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2017, 37(6): 1908.

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