Your browser doesn't support javascript.
loading
Rapid identification and determination of adulteration in medicinal Arnebiae Radix by combining near infrared spectroscopy with chemometrics.
Li, Xiaolong; Zhong, Yongqi; Li, Jiaqi; Lin, Zhaozhou; Pei, Yanling; Dai, Shengyun; Sun, Fei.
Afiliação
  • Li X; School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China.
  • Zhong Y; School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China.
  • Li J; School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China.
  • Lin Z; Beijing Zhongyan Tongrentang Medicine R&D Co. Ltd, Beijing, China.
  • Pei Y; Hebei Xinminhe Pharmaceutical Technology Development Co., Ltd, Hebei, China.
  • Dai S; National Institutes for Food and Drug Control, Beijing, China. Electronic address: daishengyun1228@163.com.
  • Sun F; School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China. Electronic address: sunfei2017@gdpu.edu.cn.
Spectrochim Acta A Mol Biomol Spectrosc ; 318: 124437, 2024 Oct 05.
Article em En | MEDLINE | ID: mdl-38772180
ABSTRACT
The medicinal Arnebia Radix (AR) is one of widely-used Chinese herbal medicines (CHMs), usually adulterated with non-medicinal species that seriously compromise the quality of AR and affect patients' health. Detection of these adulterants is usually performed by using expensive and time-consuming analytical instruments. In this study, a rapid, non-destructive, and effective method was proposed to identify and determine the adulteration in the medicinal AR by near-infrared (NIR) spectroscopy coupled with chemometrics. 37 batches of medicinal AR samples originated from Arnebia euchroma (Royle) Johnst., 11 batches of non-medicinal AR samples including Onosma paniculatum Bur. et Franch and Arnebia benthamii (Wall. ex G. Don) Johnston, and 72 batches of adulterated AR samples were characterized by NIR spectroscopy. The data driven-soft independent modeling by class analogy (DD-SIMCA) and partial least squares-discriminant analysis (PLS-DA) were separately used to differentiate the authentic from adulterated AR samples. Then the PLS and support vector machine (SVM) were applied to predict the concentration of the adulteration in the adulterated AR samples, respectively. As a result, the classification accuracies of DD-SIMCA and PLS-DA models were 100% for the calibration set, and 96.7% vs. 100% for the prediction set. Moreover, the relative prediction deviation (RPD) values of PLS models reached 11.38 and 7.75 for quantifying two adulterants species, which were obviously superior to the SVM models. It can be concluded that the NIR spectroscopy coupled with chemometrics is feasible to identify the authentic from adulterated AR samples and quantify the adulteration in adulterated AR samples.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Medicamentos de Ervas Chinesas / Contaminação de Medicamentos / Espectroscopia de Luz Próxima ao Infravermelho / Boraginaceae / Quimiometria Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Medicamentos de Ervas Chinesas / Contaminação de Medicamentos / Espectroscopia de Luz Próxima ao Infravermelho / Boraginaceae / Quimiometria Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China