Cluster and principal component analysis for Kováts’ retention indices on apolar and polar stationary phases in gas chromatography

Dallos, András; Ngo, H.S.; Kresz, Richárd; Héberger, Károly

doi:10.1016/j.chroma.2007.11.034

Cited by 16 publications

(6 citation statements)

References 29 publications

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“…The betweengroups linkage or the unweighted pair group method with arithmetic mean (UPGMA) technique, which defines the distance between two clusters as the average of all the pairs of distances between elements of both clusters, was adopted. 24,31,32 Similarities and dissimilarities were quantified by Square Euclidean distance measurements. The data analysis was realized using the SPSS v.13.0 statistical package (SPSS, 2005).…”

Section: Multivariate Statistical Analysismentioning

confidence: 99%

Discrimination and classification of tobacco wastes by identification and quantification of polyphenols with LC-MS/MS

Wang

Zhao

et al. 2010

J Serb Chem Soc

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The chemical composition of polyphenols in tobacco waste was identified by HPLC-PAD-ESI/MS/MS and the contents of chlorogenic acids and rutin in 10 varieties of tobacco wastes were determined by HPLC-UV. The relationships between the contents of active polyphenols and the varieties of tobacco wastes were interpreted by hierarchical cluster analysis (HCA) and principal component analysis (PCA). The results showed that 15 polyphenols were identified in a methanolic extract of dried tobacco waste. The tobacco wastes were characterized by high levels of chlorogenic acids (3-CQA, 5-CQA, and 4-CQA) and rutin; their ranges in the 10 tobacco varieties were 0.116-0.196, 0.686-1.781, 0.094- 0.192, and 0.413-0.998 %, respectively. According to multivariate statistics models, two active compound variables can be considered important for the discrimination of the varieties of tobacco wastes: chlorogenic acids and rutin. Consequently, samples of 10 tobacco varieties were characterized into three groups by HCA based on the PCA pattern. In conclusion, tobacco waste could be used as a new pharmaceutical material for the production of natural chlorogenic acids and rutin in the ethnopharmacological industry.

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Section: Multivariate Statistical Analysismentioning

confidence: 99%

Discrimination and classification of tobacco wastes by identification and quantification of polyphenols with LC-MS/MS

Wang

Zhao

et al. 2010

J Serb Chem Soc

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“…The biplots associated to the QSRR model for Z and U are almost identical according to the fact that the same set of molecular descriptors was selected, and the two responses are highly correlated, therefore, PCA results referring to the first case were not reported in Figure 4. PCA also offers a graphical tool to rank the chromatographic phases, this approach being already used to classify the columns based on various kind of empirical descriptors [9,21,30,31]. It must be noted that the plots displayed in Figure 4 do not change appreciably if the experimental response is removed from the variable set subjected to PCA.…”

Section: Resultsmentioning

confidence: 99%

Characterisation of Gas-Chromatographic Poly(Siloxane) Stationary Phases by Theoretical Molecular Descriptors and Prediction of McReynolds Constants

D’Archivio¹,

Giannitto²

2019

IJMS

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Retention in gas–liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure–retention relationship (QSRR) approaches, characterisation of stationary phases is historically based on empirical scales, the McReynolds system of phase constants being one of the most popular. In this work, poly(siloxane) stationary phases, which occupy a dominant position in modern gas–liquid chromatography, were characterised by theoretical molecular descriptors. With this aim, the first five McReynolds constants of 29 columns were modelled by multilinear regression (MLR) coupled with genetic algorithm (GA) variable selection applied to the molecular descriptors provided by software Dragon. The generalisation ability of the established GA-MLR models, evaluated by both external prediction and repeated calibration/evaluation splitting, was better than that reported in analogous studies regarding nonpolymeric (molecular) stationary phases. Principal component analysis on the significant molecular descriptors allowed to classify the poly(siloxanes) according to their chemical composition and partitioning properties. Development of QSRR-based models combining molecular descriptors of both solutes and stationary phases, which will be applied to transfer retention data among different columns, is in progress.

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“…During the last decades, the motivation for applying variable selection techniques in quantitative structure–activity relationships (QSAR) has shifted from being an illustrative example to becoming a real prerequisite for model building . In contrast to other dimensionality reduction techniques like those based on projection or compression, variable selection techniques do not alter the origin representation of the variables, but merely select a subset of them.…”

Section: Methodsmentioning

confidence: 99%

Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine

Yan

Huang

et al. 2013

J of Separation Science

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Retention indices for frequently reported compounds of plant essential oils on three different stationary phases were investigated. Multivariate linear regression, partial least squares, and support vector machine combined with a new variable selection approach called random-frog recently proposed by our group, were employed to model quantitative structure-retention relationships. Internal and external validations were performed to ensure the stability and predictive ability. All the three methods could obtain an acceptable model, and the optimal results by support vector machine based on a small number of informative descriptors with the square of correlation coefficient for cross validation, values of 0.9726, 0.9759, and 0.9331 on the dimethylsilicone stationary phase, the dimethylsilicone phase with 5% phenyl groups, and the PEG stationary phase, respectively. The performances of two variable selection approaches, random-frog and genetic algorithm, are compared. The importance of the variables was found to be consistent when estimated from correlation coefficients in multivariate linear regression equations and selection probability in model spaces.

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Cluster and principal component analysis for Kováts’ retention indices on apolar and polar stationary phases in gas chromatography

Cited by 16 publications

References 29 publications

Discrimination and classification of tobacco wastes by identification and quantification of polyphenols with LC-MS/MS

Discrimination and classification of tobacco wastes by identification and quantification of polyphenols with LC-MS/MS

Characterisation of Gas-Chromatographic Poly(Siloxane) Stationary Phases by Theoretical Molecular Descriptors and Prediction of McReynolds Constants

Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine

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