Supercritical Fluid Chromatography with Chemical Pattern Recognition: Quality Evaluation using SFC and CPR - Couverture souple

Huang, Yang ; Wang, Tiejie ; Jiang, Zhengjin

9786200457035: Supercritical Fluid Chromatography with Chemical Pattern Recognition: Quality Evaluation using SFC and CPR

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ISBN 10 : 6200457034 ISBN 13 : 9786200457035

Editeur : LAP LAMBERT Academic Publishing, 2019

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2019
Langue: anglais
ISBN 10: 6200457034
ISBN 13: 9786200457035
Reliure: Broch�
Nombre de pages: 68
Coordonn�es du fabricant: non disponible
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Supercritical Fluid Chromatography with Chemical Pattern Recognition

Yang Huang

Edit� par LAP LAMBERT Academic Publishing Okt 2019, 2019

ISBN 10 : 6200457034 ISBN 13 : 9786200457035

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Mahonia bealei (Fort.) Carr. (M. bealei) plays an important role in the treatment of many diseases. In the present study, a comprehensive method combining supercritical fluid chromatography (SFC) fingerprints and chemical pattern recognition (CPR) for quality evaluation of M. bealei was developed. Similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) were applied to classify and evaluate the samples of wild M. bealei, cultivated M. bealei and its substitutes according to the peak area of 11 components but an accurate classification could not be achieved. PLS-DA was then adopted to select the characteristic variables based on variable importance in projection (VIP) values that responsible for accurate classification. Six characteristics peaks with higher VIP values (�1) were selected for building the CPR model. Based on the six variables, three types of samples were accurately classified into three related clusters. The model was further validated by a testing set samples and predication set samples. The results indicated the model was successfully established and predictive ability was also verified satisfactory. 68 pp. Englisch. N� de r�f. du vendeur 9786200457035

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Supercritical Fluid Chromatography with Chemical Pattern Recognition: Quality Evaluation using SFC and CPR

Huang, Yang; Wang, Tiejie; Jiang, Zhengjin

Edit� par LAP LAMBERT Academic Publishing, 2019

ISBN 10 : 6200457034 ISBN 13 : 9786200457035

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Supercritical Fluid Chromatography with Chemical Pattern Recognition: Quality Evaluation using SFC and CPR

Huang, Yang; Wang, Tiejie; Jiang, Zhengjin

Edit� par LAP LAMBERT Academic Publishing, 2019

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Supercritical Fluid Chromatography with Chemical Pattern Recognition: Quality Evaluation using SFC and CPR

Huang, Yang; Wang, Tiejie; Jiang, Zhengjin

Edit� par LAP LAMBERT Academic Publishing, 2019

ISBN 10 : 6200457034 ISBN 13 : 9786200457035

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Supercritical Fluid Chromatography with Chemical Pattern Recognition

Yang HUANG|Tiejie WANG|Zhengjin JIANG

Edit� par LAP LAMBERT Academic Publishing, 2019

ISBN 10 : 6200457034 ISBN 13 : 9786200457035

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: HUANG YangIn December 2017, Dr. Yang HUANG obtained a double Ph.D degree from Jinan University (Guangzhou, China) and Univeristy of Liege (Liege, Belgium). At present, his research topics are mainly focused on quality evaluation and . N� de r�f. du vendeur 497401952

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Supercritical Fluid Chromatography with Chemical Pattern Recognition

Yang Huang

Edit� par LAP LAMBERT Academic Publishing Okt 2019, 2019

ISBN 10 : 6200457034 ISBN 13 : 9786200457035

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Mahonia bealei (Fort.) Carr. (M. bealei) plays an important role in the treatment of many diseases. In the present study, a comprehensive method combining supercritical fluid chromatography (SFC) fingerprints and chemical pattern recognition (CPR) for quality evaluation of M. bealei was developed. Similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) were applied to classify and evaluate the samples of wild M. bealei, cultivated M. bealei and its substitutes according to the peak area of 11 components but an accurate classification could not be achieved. PLS-DA was then adopted to select the characteristic variables based on variable importance in projection (VIP) values that responsible for accurate classification. Six characteristics peaks with higher VIP values (�1) were selected for building the CPR model. Based on the six variables, three types of samples were accurately classified into three related clusters. The model was further validated by a testing set samples and predication set samples. The results indicated the model was successfully established and predictive ability was also verified satisfactory.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 68 pp. Englisch. N� de r�f. du vendeur 9786200457035

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Supercritical Fluid Chromatography with Chemical Pattern Recognition : Quality Evaluation using SFC and CPR

Yang Huang

Edit� par LAP LAMBERT Academic Publishing, 2019

ISBN 10 : 6200457034 ISBN 13 : 9786200457035

Neuf Taschenbuch

impression � la demande

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mahonia bealei (Fort.) Carr. (M. bealei) plays an important role in the treatment of many diseases. In the present study, a comprehensive method combining supercritical fluid chromatography (SFC) fingerprints and chemical pattern recognition (CPR) for quality evaluation of M. bealei was developed. Similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) were applied to classify and evaluate the samples of wild M. bealei, cultivated M. bealei and its substitutes according to the peak area of 11 components but an accurate classification could not be achieved. PLS-DA was then adopted to select the characteristic variables based on variable importance in projection (VIP) values that responsible for accurate classification. Six characteristics peaks with higher VIP values (�1) were selected for building the CPR model. Based on the six variables, three types of samples were accurately classified into three related clusters. The model was further validated by a testing set samples and predication set samples. The results indicated the model was successfully established and predictive ability was also verified satisfactory. N� de r�f. du vendeur 9786200457035

Contacter le vendeur