Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine.
J Sep Sci
; 36(15): 2464-71, 2013 Aug.
Article
em En
| MEDLINE
| ID: mdl-23720406
ABSTRACT
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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Base de dados:
MEDLINE
Assunto principal:
Plantas
/
Algoritmos
/
Óleos Voláteis
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article