Geographical origin authentication of Gongju at different spatial scales based on hyperspectral technology.
10.19540/j.cnki.cjcmm.20240814.101
- Author:
Xue GUO
1
;
Rui-Bin BAI
1
;
Hui WANG
1
;
Wei-Wen LI
2
;
Ling DONG
2
;
Jia-Hui SUN
1
;
Xiao-Bo ZHANG
1
;
Jian YANG
1
Author Information
1. National 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. Key Laboratory of Horticultural Crop Germplasm innovation and Utilization (Co-construction by Ministry and Province), Institute of Horticulture,Anhui Academy of Agricultural Sciences Hefei 230001, China.
- Publication Type:English Abstract
- Keywords:
Gongju;
chemometrics;
geographical origin authentication;
geographical scales;
hyperspectral technology
- MeSH:
Chrysanthemum/growth & development*;
China;
Support Vector Machine;
Geography;
Discriminant Analysis;
Spectroscopy, Near-Infrared/methods*;
Spectrum Analysis/methods*;
Drugs, Chinese Herbal/analysis*;
Least-Squares Analysis
- From:
China Journal of Chinese Materia Medica
2024;49(22):6073-6081
- CountryChina
- Language:Chinese
-
Abstract:
Gongju(Chrysanthemum morifolium) is one of the five major medicinal Chrysanthemum varieties included in the Chinese Pharmacopoeia. In recent years, its cultivation areas have changed significantly, resulting in mixed quality of the medicinal herbs. In this study, Gongju cultivated in Anhui, Yunnan, Chongqing, and other places were selected as research objects. Hyperspectral data were collected in the visible-near-infrared(VNIR) and short-wave infrared(SWIR) bands using different modes, such as corolla facing up(A) and flower base facing up(B). After pre-processing the hyperspectral data using five methods, including multiplicative scatter correction(MSC), Savitzky-Golay smoothing(SG), first derivative(D1), second derivative(D2), and standard normal variate(SNV), partial least squares discriminant analysis(PLSDA), random forest(RF), and support vector machine(SVM) were used to establish origin identification models of Gongju at the two geographical scales of the province and the city-county in Anhui province. The accuracy of the prediction results was used as an evaluation index to select the optimal models, and the classification performance of the models was evaluated by confusion matrix. The results showed that the flower base facing up(B) collection model combined with second derivative pretreatment and RF method was the best model for both geographical scale identification models. The modeling effect of the full-band(VNIR + SWIR) was slightly better than that of the single band, with the accuracy of the prediction set in the province and city-county regions reaching 99.69% and 99.40%, respectively. The competitive adaptive reweighted sampling algorithm(CARS), successive projections algorithm(SPA), and variable iterative space shrinkage approach(VISSA) were further used to screen the feature wavelength modeling. The number of feature wavelengths screened by CARS was fewer, and the prediction set accuracy of the two geographical scales models after optimization could reach 99.56% and 98.65%, which was basically comparable to the full-band model. However, the removal of redundant variables could greatly reduce the complexity of the model. The hyperspectral technology combined with the chemometrics model established in this study can achieve the origin identification of Gongju at different geographical scales, providing a theoretical basis and technical reference for the construction of a rapid detection system for Gongju origin and the development of exclusive miniaturized instrumentation and equipment systems.
