Fast identification of origins and cultivation patterns of Astragali Radix by dimension reduction algorithms of hyperspectral data.
10.19540/j.cnki.cjcmm.20240827.101
- Author:
Fei-Xiang ZHOU
1
;
Hong JIANG
2
;
Bao-Lin GUO
3
;
Jiao-Yang LUO
3
;
Cheng PAN
3
;
Mei-Hua YANG
3
;
Ye-Lin LIU
4
Author Information
1. School of Investigation, People's Public Security University of China Beijing 100038, China.
2. Judicial Appraisal Center of Wanzijian Testing Technology Co., Ltd. Beijing 100141, China.
3. Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100193, China.
4. Jiangsu Dualix Spectral Imaging Co., Ltd. Wuxi 214000, China.
- Publication Type:Journal Article
- Keywords:
Astragali Radix;
competitive adaptive reweighted sampling;
convolutional neural network;
hyperspectrum;
partial least squares-discriminant analysis
- MeSH:
Drugs, Chinese Herbal/chemistry*;
Neural Networks, Computer;
Algorithms;
Support Vector Machine;
Principal Component Analysis;
Discriminant Analysis;
Hyperspectral Imaging/methods*;
Least-Squares Analysis;
Astragalus Plant/growth & development*;
Astragalus propinquus/growth & development*
- From:
China Journal of Chinese Materia Medica
2024;49(24):6660-6666
- CountryChina
- Language:Chinese
-
Abstract:
This study aims to establish a rapid and non-destructive method for recognizing the origins and cultivation patterns of Astragali Radix. A hyperspectral imaging system(spectral ranges: 400-1 000 nm, 900-1 700 nm; detection time: 15 s) was used to examine the samples of Astragali Radix with different origins and cultivation patterns. The collected hyperspectral datasets were highly correlated and numerous, which required the establishment of stable and reliable dimension reduction and classification models. Firstly, the original spectra were preprocessed by normalization, Gaussian smoothing, and masking. Then, principal component analysis(PCA), partial least squares-discriminant analysis(PLS-DA), and competitive adaptive reweighted sampling(CARS) were performed to reduce the dimension of the hyperspectral data. Finally, support vector machine(SVM), feedforward neural network(FFNN), and convolutional neural network(CNN) were used for data training of the spectral images and spectral curves with dimension reduction. The results showed that applying CARS as a variable selection method before PLS-DA on the hyperspectral data of Astragali Radix achieved the accuracy, precision, and recall of 100% on the CNN test dataset. The F_1-score and area under the curve of ROC(AUC) reached 1. This method is convenient, quick, sample-saving, and non-destructive, providing technical support for rapid identification of the origins and cultivation patterns of Astragali Radix.
