为了快速识别茶叶品种,提出了激光诱导击穿光谱全光学诊断方法。采集7种茶叶样品在200~480 nm波长范围的激光诱导击穿光谱的全谱数据,分别运用九点平滑和九点平滑/一阶导数方法对光谱进行降噪、消除干扰预处理,再结合主成分分析对预处理后的光谱进行降维。选择判别分析(DA)、径向基函数网络(RBF)和B-P反向传播网络(又称MLP)三种模型对7种茶叶进行品种识别。结果显示:综合九点平滑和一阶导数预处理后,再结合主成分分析降维,可使三种模型对茶叶品种的识别准确率均有一定程度的提高,MLP的识别准确率高于DA和RBF,其训练集识别准确率为99.6%,测试集识别准确率为99.1%。选择合适的激光诱导击穿光谱预处理及模型构建方法,对快速准确识别茶叶品种具有可行性。

Laser induced breakdown spectroscopy (LIBS) is proposed to identify tea variety rapidly. LIBS spectra of seven kinds of teas are collected at 200-480 nm wavelength. Two approaches in preprocessing spectra are applied to decrease noise and eliminate disturbance. One is nine-point smoothing (NPS), the other is NPS combined with first derivative (FD). Principal component analysis (PCA) is adopted to reduce the dimensions of processed spectra. Three models like discriminant analysis (DA), radical basic function (RBF) and BP-ANN (multi layer perception, MLP) are selected to discriminate the tea variety. The results demonstrate that the recognition accuracy of tea variety is improved while NPS, FD and PCA are utilized according to priority. And the recognition accuracy of MLP is higher than that of DA and RBF. The recognition accuracy of MLP is 99.6% in training set and 99.1% in test set. It is feasible to select suitable LIBS spectra preprocessing and model construction method to identify tea variety.