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A Rapid Discrimination of Authentic and Unauthentic Radix Angelicae Sinensis Growth Regions by Electronic Nose Coupled with Multivariate Statistical Analyses

Radix Angelicae Sinensis, known as Danggui in China, is an effective and wide applied material in Traditional Chinese Medicine (TCM) and it is used in more than 80 composite formulae. Danggui from Minxian County, Gansu Province is the best in quality. To rapidly and nondestructively discriminate Dan...

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Autores principales: Liu, Jie, Wang, Weixin, Yang, Yaojun, Yan, Yuning, Wang, Wenyi, Wu, Haozhong, Ren, Zihe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279474/
https://www.ncbi.nlm.nih.gov/pubmed/25350503
http://dx.doi.org/10.3390/s141120134
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author Liu, Jie
Wang, Weixin
Yang, Yaojun
Yan, Yuning
Wang, Wenyi
Wu, Haozhong
Ren, Zihe
author_facet Liu, Jie
Wang, Weixin
Yang, Yaojun
Yan, Yuning
Wang, Wenyi
Wu, Haozhong
Ren, Zihe
author_sort Liu, Jie
collection PubMed
description Radix Angelicae Sinensis, known as Danggui in China, is an effective and wide applied material in Traditional Chinese Medicine (TCM) and it is used in more than 80 composite formulae. Danggui from Minxian County, Gansu Province is the best in quality. To rapidly and nondestructively discriminate Danggui from the authentic region of origin from that from an unauthentic region, an electronic nose coupled with multivariate statistical analyses was developed. Two different feature extraction methods were used to ensure the authentic region and unauthentic region of Danggui origin could be discriminated. One feature extraction method is to capture the average value of the maximum response of the electronic nose sensors (feature extraction method 1). The other one is to combine the maximum response of the sensors with their inter-ratios (feature extraction method 2). Multivariate statistical analyses, including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and hierarchical clustering analysis (HCA) were employed. Nineteen samples were analyzed by PCA, SIMCA and HCA. Then the remaining samples (GZM1, SH) were projected onto the SIMCA model to validate the models. The results indicated that, in the use of feature extraction method 2, Danggui from Yunnan Province and Danggui from Gansu Province could be successfully discriminated using the electronic nose coupled with PCA, SIMCA and HCA, which suggested that the electronic-nose system could be used as a simple and rapid technique for the discrimination of Danggui between authentic and unauthentic region of origin.
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spelling pubmed-42794742015-01-15 A Rapid Discrimination of Authentic and Unauthentic Radix Angelicae Sinensis Growth Regions by Electronic Nose Coupled with Multivariate Statistical Analyses Liu, Jie Wang, Weixin Yang, Yaojun Yan, Yuning Wang, Wenyi Wu, Haozhong Ren, Zihe Sensors (Basel) Article Radix Angelicae Sinensis, known as Danggui in China, is an effective and wide applied material in Traditional Chinese Medicine (TCM) and it is used in more than 80 composite formulae. Danggui from Minxian County, Gansu Province is the best in quality. To rapidly and nondestructively discriminate Danggui from the authentic region of origin from that from an unauthentic region, an electronic nose coupled with multivariate statistical analyses was developed. Two different feature extraction methods were used to ensure the authentic region and unauthentic region of Danggui origin could be discriminated. One feature extraction method is to capture the average value of the maximum response of the electronic nose sensors (feature extraction method 1). The other one is to combine the maximum response of the sensors with their inter-ratios (feature extraction method 2). Multivariate statistical analyses, including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and hierarchical clustering analysis (HCA) were employed. Nineteen samples were analyzed by PCA, SIMCA and HCA. Then the remaining samples (GZM1, SH) were projected onto the SIMCA model to validate the models. The results indicated that, in the use of feature extraction method 2, Danggui from Yunnan Province and Danggui from Gansu Province could be successfully discriminated using the electronic nose coupled with PCA, SIMCA and HCA, which suggested that the electronic-nose system could be used as a simple and rapid technique for the discrimination of Danggui between authentic and unauthentic region of origin. MDPI 2014-10-27 /pmc/articles/PMC4279474/ /pubmed/25350503 http://dx.doi.org/10.3390/s141120134 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Liu, Jie
Wang, Weixin
Yang, Yaojun
Yan, Yuning
Wang, Wenyi
Wu, Haozhong
Ren, Zihe
A Rapid Discrimination of Authentic and Unauthentic Radix Angelicae Sinensis Growth Regions by Electronic Nose Coupled with Multivariate Statistical Analyses
title A Rapid Discrimination of Authentic and Unauthentic Radix Angelicae Sinensis Growth Regions by Electronic Nose Coupled with Multivariate Statistical Analyses
title_full A Rapid Discrimination of Authentic and Unauthentic Radix Angelicae Sinensis Growth Regions by Electronic Nose Coupled with Multivariate Statistical Analyses
title_fullStr A Rapid Discrimination of Authentic and Unauthentic Radix Angelicae Sinensis Growth Regions by Electronic Nose Coupled with Multivariate Statistical Analyses
title_full_unstemmed A Rapid Discrimination of Authentic and Unauthentic Radix Angelicae Sinensis Growth Regions by Electronic Nose Coupled with Multivariate Statistical Analyses
title_short A Rapid Discrimination of Authentic and Unauthentic Radix Angelicae Sinensis Growth Regions by Electronic Nose Coupled with Multivariate Statistical Analyses
title_sort rapid discrimination of authentic and unauthentic radix angelicae sinensis growth regions by electronic nose coupled with multivariate statistical analyses
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279474/
https://www.ncbi.nlm.nih.gov/pubmed/25350503
http://dx.doi.org/10.3390/s141120134
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