Origin identification of Polygonatum cyrtonema based on hyperspectral data.
10.19540/j.cnki.cjcmm.20230512.103
- Author:
Deng-Ting ZHANG
1
;
Jian YANG
2
;
Ming-En CHENG
3
;
Hui WANG
2
;
Dai-Yin PENG
3
;
Xiao-Bo ZHANG
2
Author Information
1. School of Pharmacy,Anhui University of Chinese Medicine Hefei 230012,China State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs,National Resource Center for Chinese Materia Medica,China Academy of Chinese Medical Sciences Beijing 100700,China.
2. State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs,National Resource Center for Chinese Materia Medica,China Academy of Chinese Medical Sciences Beijing 100700,China.
3. School of Pharmacy,Anhui University of Chinese Medicine Hefei 230012,China.
- Publication Type:Journal Article
- Keywords:
Polygonatum cyrtonema;
different spatial scales;
geographical origin identification;
hyperspectral;
identification model
- MeSH:
Spectroscopy, Near-Infrared;
Polygonatum;
Algorithms;
Random Forest;
Least-Squares Analysis
- From:
China Journal of Chinese Materia Medica
2023;48(16):4347-4361
- CountryChina
- Language:Chinese
-
Abstract:
In this study, visual-near infrared(VNIR), short-wave infrared(SWIR), and VNIR + SWIR fusion hyperspectral data of Polygonatum cyrtonema from different geographical origins were collected and preprocessed by first derivative(FD), second derivative(SD), Savitzky-Golay smoothing(S-G), standard normalized variate(SNV), multiplicative scatter correction(MSC), FD+S-G, and SD+S-G. Three algorithms, namely random forest(RF), linear support vector classification(LinearSVC), and partial least squares discriminant analysis(PLS-DA), were used to establish the identification models of P. cyrtonema origin from three spatial scales, i.e., province, county, and township, respectively. Successive projection algorithm(SPA) and competitive adaptive reweighted sampling(CARS) were used to screen the characteristic bands, and the P. cyrtonema origin identification models were established according to the selected characteristic bands. The results showed that(1)after FD preprocessing of VNIR+SWIR fusion hyperspectral data, the accuracy of recognition models established using LinearSVC was the highest, reaching 99.97% and 99.82% in the province origin identification model, 100.00% and 99.46% in the county origin identification model, and 99.62% and 98.39% in the township origin identification model. The accuracy of province, county, and township origin identification models reached more than 98.00%.(2)Among the 26 characteristic bands selected by CARS, after FD pretreatment, the accuracy of origin identification models of different spatial scales was the highest using LinearSVC, reaching 98.59% and 97.05% in the province origin identification model, 97.79% and 94.75% in the county origin identification model, and 90.13% and 87.95% in the township origin identification model. The accuracy of identification models of different spatial scales established by 26 characteristic bands reached more than 87.00%. The results show that hyperspectral imaging technology can realize accurate identification of P. cyrtonema origin from different spatial scales.
