Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint–Chemical Pattern Recognition

Phellodendri Chinensis Cortex (PCC) and Phellodendri Amurensis Cortex (PAC) are increasingly being used as traditional herbal medicines, but they are often mistaken for each other. In this study, the fingerprints of PCC from six different geographical sources were obtained by high-performance liquid...

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Main Authors: Xuexiao Cao, Lili Sun, Di Li, Guangjiao You, Meng Wang, Xiaoliang Ren
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Molecules
Subjects:
Online Access:http://www.mdpi.com/1420-3049/23/9/2307
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spelling doaj-78e309f2b9d64ae3a9fd61e2c28d37572020-11-25T00:42:04ZengMDPI AGMolecules1420-30492018-09-01239230710.3390/molecules23092307molecules23092307Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint–Chemical Pattern RecognitionXuexiao Cao0Lili Sun1Di Li2Guangjiao You3Meng Wang4Xiaoliang Ren5School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, ChinaSchool of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, ChinaSchool of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, ChinaSchool of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, ChinaTianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, ChinaSchool of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, ChinaPhellodendri Chinensis Cortex (PCC) and Phellodendri Amurensis Cortex (PAC) are increasingly being used as traditional herbal medicines, but they are often mistaken for each other. In this study, the fingerprints of PCC from six different geographical sources were obtained by high-performance liquid chromatography, and multivariate chemometric methods were used for comprehensive analysis. Two unsupervised pattern recognition models (principal component analysis and hierarchical cluster analysis) and a supervised pattern recognition model (partial least squares discriminant analysis) were established on the basis of the chemical composition and physical traits of PCC and PAC. PCC and PAC were found to be distinguishable by these methods. The PCC category was divisible into two categories, one with more crude cork and a maximum thickness of ~1.5 mm, and the other with less net crude cork and a maximum thickness of 0.5 mm. According to the model established by partial least squares discriminant analysis (PLS-DA), the important chemical marker berberine hydrochloride was obtained and analyzed quantitatively. From these results combined with chemometric and content analyses, the preliminary classification standards for phellodendron were established as three grades: superior, first-order and mixed. Compared with the traditional identification methods of thin layer chromatography identification and microscopic identification, our method for quality evaluation is relatively simple. It provides a basis and reference for identification of PCC and enables establishment of grade standards. It also could be applied in quality control for compound preparations containing PCC.http://www.mdpi.com/1420-3049/23/9/2307Phellodendri Chinensis Cortex (PCC)Phellodendri Amurensis Cortex (PAC)fingerprintchemical pattern recognitionquality evaluation
collection DOAJ
language English
format Article
sources DOAJ
author Xuexiao Cao
Lili Sun
Di Li
Guangjiao You
Meng Wang
Xiaoliang Ren
spellingShingle Xuexiao Cao
Lili Sun
Di Li
Guangjiao You
Meng Wang
Xiaoliang Ren
Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint–Chemical Pattern Recognition
Molecules
Phellodendri Chinensis Cortex (PCC)
Phellodendri Amurensis Cortex (PAC)
fingerprint
chemical pattern recognition
quality evaluation
author_facet Xuexiao Cao
Lili Sun
Di Li
Guangjiao You
Meng Wang
Xiaoliang Ren
author_sort Xuexiao Cao
title Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint–Chemical Pattern Recognition
title_short Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint–Chemical Pattern Recognition
title_full Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint–Chemical Pattern Recognition
title_fullStr Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint–Chemical Pattern Recognition
title_full_unstemmed Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint–Chemical Pattern Recognition
title_sort quality evaluation of phellodendri chinensis cortex by fingerprint–chemical pattern recognition
publisher MDPI AG
series Molecules
issn 1420-3049
publishDate 2018-09-01
description Phellodendri Chinensis Cortex (PCC) and Phellodendri Amurensis Cortex (PAC) are increasingly being used as traditional herbal medicines, but they are often mistaken for each other. In this study, the fingerprints of PCC from six different geographical sources were obtained by high-performance liquid chromatography, and multivariate chemometric methods were used for comprehensive analysis. Two unsupervised pattern recognition models (principal component analysis and hierarchical cluster analysis) and a supervised pattern recognition model (partial least squares discriminant analysis) were established on the basis of the chemical composition and physical traits of PCC and PAC. PCC and PAC were found to be distinguishable by these methods. The PCC category was divisible into two categories, one with more crude cork and a maximum thickness of ~1.5 mm, and the other with less net crude cork and a maximum thickness of 0.5 mm. According to the model established by partial least squares discriminant analysis (PLS-DA), the important chemical marker berberine hydrochloride was obtained and analyzed quantitatively. From these results combined with chemometric and content analyses, the preliminary classification standards for phellodendron were established as three grades: superior, first-order and mixed. Compared with the traditional identification methods of thin layer chromatography identification and microscopic identification, our method for quality evaluation is relatively simple. It provides a basis and reference for identification of PCC and enables establishment of grade standards. It also could be applied in quality control for compound preparations containing PCC.
topic Phellodendri Chinensis Cortex (PCC)
Phellodendri Amurensis Cortex (PAC)
fingerprint
chemical pattern recognition
quality evaluation
url http://www.mdpi.com/1420-3049/23/9/2307
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