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• W2118226249 abstract "A quantitative structure-property relationship (QSPR) study is suggested for the prediction of retention times of volatile organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structure of compounds. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and artificial neural network (ANN). The stepwise regression was used for the selection of the variables which gives the best-fitted models. After variable selection ANN, MLR methods were used with leave-one-out cross validation for building the regression models. The prediction results are in very good agreement with the experimental values. MLR as the linear regression method shows good ability in the prediction of the retention times of the prediction set. This provided a new and effective method for predicting the chromatography retention index for the volatile organic compounds." @default.
• W2118226249 created "2016-06-24" @default.
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• W2118226249 date "2012-05-02" @default.
• W2118226249 modified "2023-10-16" @default.
• W2118226249 title "A quantitative structure- property relationship of gas chromatographic/mass spectrometric retention data of 85 volatile organic compounds as air pollutant materials by multivariate methods" @default.
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• W2118226249 doi "https://doi.org/10.1186/1752-153x-6-s2-s4" @default.
• W2118226249 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3395126" @default.
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• W2118226249 hasPublicationYear "2012" @default.
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