Fusion of spectrum and image features to identify Glycyrrhizae Radix et Rhizoma from different origins based on hyperspectral imaging technology.
10.19540/j.cnki.cjcmm.20201120.103
- Author:
Wen-Jun YIN
1
;
Chen-Lei RU
2
;
Jie ZHENG
1
;
Lu ZHANG
1
;
Ji-Zhong YAN
1
;
Hui ZHANG
1
Author Information
1. College of Pharmaceutical Sciences,Zhejiang University of Technology Hangzhou 310014,China.
2. School of Mechanical Engineering,Zhejiang University Hangzhou 310058,China.
- Publication Type:Journal Article
- Keywords:
Glycyrrhizae Radix et Rhizoma;
data fusion;
hyperspectral imaging;
image features;
origin identification;
spectral features
- MeSH:
Drugs, Chinese Herbal;
Hyperspectral Imaging;
Technology
- From:
China Journal of Chinese Materia Medica
2021;46(4):923-930
- CountryChina
- Language:Chinese
-
Abstract:
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.
