基于高光谱成像技术的祁门红茶等级无损检测

为实现快速无损的茶叶产品等级评估，应用近红外（900~1700 nm）高光谱成像技术对6个等级的祁门红茶进行分类。首先利用线性和非线性降维方法对高光谱数据进行可视化处理，可视化算法包括线性方法的主成分分析（Principal Component Analysis，PCA）、多维尺度变换（Multi-Dimensional Scaling，MDS），和非线性方法的t分布随机邻域嵌入（t-Distributed Stochastic Neighbour Embedding，t-SNE）、Sammon非线性映射。其次利用支持向量机（Support Vector Machine，SVM）和极限学习机（Extreme Learning Machine，ELM）建立分类模型来鉴定祁门红茶的不同等级。最后利用SVM和ELM分类模型对高光谱图像每个像素点进行识别，得到预测图。结果表明，t-SNE可以将6个等级的祁门红茶分在六个不同的簇，SVM和ELM的预测集准确率分别为100%和96.35%。t-SNE可视化效果最佳，SVM的检测模型能够有效地对祁门红茶六个等级进行识别。本文为茶叶产品等级的快速、无损检测提供了一种有效的方法，对茶叶产品的质量控制、真伪检测和掺假检测具有重要意义。

Nondestructive Detection of Keemun Black Tea Grade Based on Hyperspectral Imaging Technique

In order to evaluate the grade of tea products quickly and nondestructive, the near infrared (900~1700 nm) hyperspectral imaging technology was applied to classify six grades of black tea. Firstly, the hyperspectral data were visualized by linear and nonlinear dimension reduction methods. The visualization algorithms included the linear method of principal component analysis (PCA), multi-dimensional scaling (MDS), the nonlinear method of T-distributed stochastic embedding (T-SNE) and Sammon nonlinear mapping. Secondly, the classification model was established by using support vector machine (SVM) and extreme learning machine (ELM) to identify the different grades of Keemun black tea. Finally, SVM and ELM classification model were used to identify each pixel of hyperspectral image, and the prediction map was obtained. The results showed that T-SNE could divide the six grades of Keemun black tea into six different clusters, and the accuracy of SVM and ELM prediction set was 100% and 96.35%, respectively. The T-SNE visualization effect was the best, and the detection model of SVM could effectively identify the six grades of Keemun black tea. This paper provides an effective method for rapid and nondestructive testing of tea product grade, which had great significance for quality control, authenticity and adulteration detection of tea products.