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Article: Variable selection for discriminating herbal medicines with chromatographic fingerprints

TitleVariable selection for discriminating herbal medicines with chromatographic fingerprints
Authors
KeywordsBayes discrimination analysis
Chromatographic fingerprint
Herbal medicine
Variable selection
Issue Date2006
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/aca
Citation
Analytica Chimica Acta, 2006, v. 572 n. 2, p. 265-271 How to Cite?
AbstractWhen discriminating herbal medicines with pattern recognition based on chromatographic fingerprints, typically, the majority of variables/data points contain no discrimination information. In this paper, chemometric approaches concerning forward selection and key set factor analysis using principal component analysis (PCA), unweighted and weighted methods based on the inner- and outer-variances, Fisher coefficient from the between- and within-class variations were investigated to extract representative variables. The number of variables retained was determined based on the cumulative variance percent of principal components, the ratio of observations to variables and the factor indicative function (IND). In order to assess the methods for variable selection and criteria levels to determine the number of variables retained, the original and reduced datasets were compared with Procrustes analysis and a weighted measure of similarity. Moreover, the tri-variate plots of the first three PCA scores were used to visually examine the reduced datasets in low dimensional space. Herbal samples were finally discriminated by use of Bayes discrimination analysis with the reduced subsets. The case study for 79 herbal samples showed that, the methods of forward selection associating the variables with the loadings closest to 0 and key set factor analysis were preferable to determine the representative variables. Procrustes analysis and the weighted measure were not indicative to extract representative variables. High matching between the original and reduced datasets did not suggest high prediction accuracy. Visually examining the PC1-PC2-PC3 scores projection plots with the reduced subsets, not all the herb samples could be separated due to the complexity of chromatographic fingerprints. © 2006 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/69322
ISSN
2015 Impact Factor: 4.712
2015 SCImago Journal Rankings: 1.548
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorGong, Fen_HK
dc.contributor.authorWang, BTen_HK
dc.contributor.authorLiang, YZen_HK
dc.contributor.authorChau, FTen_HK
dc.contributor.authorFung, YSen_HK
dc.date.accessioned2010-09-06T06:12:35Z-
dc.date.available2010-09-06T06:12:35Z-
dc.date.issued2006en_HK
dc.identifier.citationAnalytica Chimica Acta, 2006, v. 572 n. 2, p. 265-271en_HK
dc.identifier.issn0003-2670en_HK
dc.identifier.urihttp://hdl.handle.net/10722/69322-
dc.description.abstractWhen discriminating herbal medicines with pattern recognition based on chromatographic fingerprints, typically, the majority of variables/data points contain no discrimination information. In this paper, chemometric approaches concerning forward selection and key set factor analysis using principal component analysis (PCA), unweighted and weighted methods based on the inner- and outer-variances, Fisher coefficient from the between- and within-class variations were investigated to extract representative variables. The number of variables retained was determined based on the cumulative variance percent of principal components, the ratio of observations to variables and the factor indicative function (IND). In order to assess the methods for variable selection and criteria levels to determine the number of variables retained, the original and reduced datasets were compared with Procrustes analysis and a weighted measure of similarity. Moreover, the tri-variate plots of the first three PCA scores were used to visually examine the reduced datasets in low dimensional space. Herbal samples were finally discriminated by use of Bayes discrimination analysis with the reduced subsets. The case study for 79 herbal samples showed that, the methods of forward selection associating the variables with the loadings closest to 0 and key set factor analysis were preferable to determine the representative variables. Procrustes analysis and the weighted measure were not indicative to extract representative variables. High matching between the original and reduced datasets did not suggest high prediction accuracy. Visually examining the PC1-PC2-PC3 scores projection plots with the reduced subsets, not all the herb samples could be separated due to the complexity of chromatographic fingerprints. © 2006 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/acaen_HK
dc.relation.ispartofAnalytica Chimica Actaen_HK
dc.subjectBayes discrimination analysisen_HK
dc.subjectChromatographic fingerprinten_HK
dc.subjectHerbal medicineen_HK
dc.subjectVariable selectionen_HK
dc.titleVariable selection for discriminating herbal medicines with chromatographic fingerprintsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0003-2697&volume=572&spage=265&epage=271&date=2006&atitle=Variable+selection+for+discriminating+herbal+medicines+with+chromatographic+fingerprints+en_HK
dc.identifier.emailFung, YS:ysfung@hku.hken_HK
dc.identifier.authorityFung, YS=rp00697en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.aca.2006.05.032en_HK
dc.identifier.pmid17723488-
dc.identifier.scopuseid_2-s2.0-33745699962en_HK
dc.identifier.hkuros122198en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33745699962&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume572en_HK
dc.identifier.issue2en_HK
dc.identifier.spage265en_HK
dc.identifier.epage271en_HK
dc.identifier.isiWOS:000239234900015-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridGong, F=7007086187en_HK
dc.identifier.scopusauthoridWang, BT=9842521200en_HK
dc.identifier.scopusauthoridLiang, YZ=7403499334en_HK
dc.identifier.scopusauthoridChau, FT=7005284545en_HK
dc.identifier.scopusauthoridFung, YS=13309754700en_HK

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