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Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants from Curcuma L. in Rhizomes and Tuberous Roots

文献类型：期刊论文

作者Wen, Qiuyi2,3; Wei, Wenlong2; Li, Yun2; Chen, Dan1; Zhang, Jianqing2; Li, Zhenwei2; Guo, De-an2,3
刊名SENSORS
出版日期2025
卷号25期号:1页码:14
关键词Curcumae Radix classification algorithms ATR-FTIR species identification
DOI10.3390/s25010050
通讯作者Guo, De-an(daguo@simm.ac.cn)
英文摘要Curcumae Longae Rhizoma (CLRh), Curcumae Radix (CRa), and Curcumae Rhizoma (CRh), derived from the different medicinal parts of the Curcuma species, are blood-activating analgesics commonly used for promoting blood circulation and relieving pain. Due to their certain similarities in chemical composition and pharmacological effects, these three herbs exhibit a high risk associated with mixing and indiscriminate use. The diverse methods used for distinguishing the medicinal origins are complex, time-consuming, and limited to intraspecific differentiation, which are not suitable for rapid and systematic identification. We developed a rapid analysis method for identification of affinis and different medicinal materials using attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR) combined with machine learning algorithms. The original spectroscopic data were pretreated using derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and smoothing (S) methods. Among them, 1D + MSC + 13S emerged as the best pretreatment method. Then, t-distributed stochastic neighbor embedding (t-SNE) was applied to visualize the results, and seven kinds of classification models were constructed. The results showed that support vector machine (SVM) modeling was superior to other models and the accuracy of validation and prediction was preferable, with a modeling time of 127.76 s. The established method could be employed to rapidly and effectively distinguish the different origins and parts of Curcuma species and thus provides a technique for rapid quality evaluation of affinis species.
WOS关键词QUALITY EVALUATION ; IDENTIFICATION ; QUANTIFICATION ; CHEMOMETRICS ; SPECTROSCOPY ; PREDICTION
资助项目National Natural Science Foundation of China[82104385] ; National Natural Science Foundation of China ; Qi-Huang Chief Scientist Project of National Administration of Traditional Chinese Medicine[SZZYSM202106004] ; Sanming Project of Medicine in Shenzhen[2020B1111110007] ; Key-Area Research and Development Program of Guangdong Province
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:001393905100001
出版者MDPI
源URL[http://119.78.100.183/handle/2S10ELR8/315633]  
专题中国科学院上海药物研究所
通讯作者Guo, De-an
作者单位1.Nanjing Univ Chinese Med, Sch Chinese Mat Med, Nanjing 210023, Peoples R China
2.Chinese Acad Sci, Zhongshan Inst Drug Discovery, Shanghai Inst Mat Med, Zhongshan 528400, Peoples R China
3.Guangdong Pharmaceut Univ, Sch Pharm, Guangzhou 510006, Peoples R China
推荐引用方式
GB/T 7714		 Wen, Qiuyi,Wei, Wenlong,Li, Yun,et al. Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants from Curcuma L. in Rhizomes and Tuberous Roots[J]. SENSORS,2025,25(1):14.
APA Wen, Qiuyi.,Wei, Wenlong.,Li, Yun.,Chen, Dan.,Zhang, Jianqing.,...&Guo, De-an.(2025).Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants from Curcuma L. in Rhizomes and Tuberous Roots.SENSORS,25(1),14.
MLA Wen, Qiuyi,et al."Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants from Curcuma L. in Rhizomes and Tuberous Roots".SENSORS 25.1(2025):14.

入库方式： OAI收割

来源：上海药物研究所

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