Yin Wen-Jun, Ru Chen-Lei, Zheng Jie, Zhang Lu, Yan Ji-Zhong, Zhang Hui
College of Pharmaceutical Sciences,Zhejiang University of Technology Hangzhou 310014,China.
School of Mechanical Engineering,Zhejiang University Hangzhou 310058,China.
Zhongguo Zhong Yao Za Zhi. 2021 Feb;46(4):923-930. doi: 10.19540/j.cnki.cjcmm.20201120.103.
To identify Glycyrrhizae Radix et Rhizoma from different geographical origins, spectrum and image features were extracted from visible and near-infrared(VNIR, 435-1 042 nm) and short-wave infrared(SWIR, 898-1 751 nm) ranges based on hyperspectral imaging technology. The spectral features of Glycyrrhizae Radix et Rhizoma samples were extracted from hyperspectral data and denoised by a variety of pre-processing methods. The classification models were established by using Partial Least Squares Discriminate Analysis(PLS-DA), Support Vector Classification(SVC) and Random Forest(RF). Meanwhile, Gray-Level Co-occurrence matrix(GLCM) was employed to extract textural variables. The spectrum and image data were implemented from three dimensions, including VNIR and SWIR fusion, spectrum and image fusion, and comprehensive data fusion. The results indicated that the spectrum in SWIR range performed better classification accuracy than VNIR range. Compared with other four pre-processing methods, the second derivative method based on Savitzky-Golay(SG) smoothing exhibited the best performance, and the classification accuracy of PLS-DA and SVC models were 93.40% and 94.11%, separately. In addition, the PLS-DA model was superior to SVC and RF models in terms of classification accuracy and model generalization capability, which were evaluated by confusion matrix and receiver operating characteristic curve(ROC). Comprehensive data fusion on SPA bands achieved a classification accuracy of 94.82% with only 28 bands. As a result, this approach not only greatly improved the classification efficiency but also maintained its accuracy. The hyperspectral imaging system, a non-invasively, intuitively and quickly identify technology, could effectively distinguish Glycyrrhizae Radix et Rhizoma samples from different origins.
为鉴别不同地理来源的甘草，基于高光谱成像技术，从可见及近红外（VNIR，435 - 1042 nm）和短波红外（SWIR，898 - 1751 nm）波段提取了甘草的光谱和图像特征。从高光谱数据中提取甘草样本的光谱特征，并通过多种预处理方法进行去噪。利用偏最小二乘判别分析（PLS - DA）、支持向量分类（SVC）和随机森林（RF）建立分类模型。同时，采用灰度共生矩阵（GLCM）提取纹理变量。光谱和图像数据从三个维度进行处理，包括VNIR与SWIR融合、光谱与图像融合以及综合数据融合。结果表明，SWIR波段的光谱分类准确率优于VNIR波段。与其他四种预处理方法相比，基于Savitzky - Golay（SG）平滑的二阶导数方法表现最佳，PLS - DA和SVC模型的分类准确率分别为93.40%和94.11%。此外，通过混淆矩阵和接收者操作特征曲线（ROC）评估，PLS - DA模型在分类准确率和模型泛化能力方面优于SVC和RF模型。在SPA波段进行综合数据融合，仅用28个波段就达到了94.82%的分类准确率。因此，该方法不仅大大提高了分类效率，还保持了准确率。高光谱成像系统作为一种非侵入性、直观且快速的鉴别技术，能够有效区分不同产地的甘草样本。
