Extraction, separation and quantitative structure-retention relationship modeling of essential oils in three herbs

Predicting retention times of naturally occurring phenolic compounds in reversed-phase liquid chromatography: a Quantitative Structure-Retention Relationship (QSRR) approach

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance.

Prediction of the maximum absorption wavelength of azobenzene dyes by QSPR tools

The maximum absorption wavelength (λ(max)) of a large data set of 191 azobenzene dyes was predicted by quantitative structure-property relationship (QSPR) tools. The λ(max) was correlated with the 4 molecular descriptors calculated from the structure of the dyes alone. The multiple linear regression method (MLR) and the non-linear radial basis function neural network (RBFNN) method were applied to develop the models. The statistical parameters provided by the MLR model were R(2)=0.893, R(adj)(2)=0.893, q(LOO)(2)=0.884, F=1214.871, RMS=11.6430 for the training set; and R(2)=0.849, R(adj)(2)=0.845, q(ext)(2)=0.846, F=207.812, RMS=14.0919 for the external test set. The RBFNN model gave even improved statistical results: R(2)=0.920, R(adj)(2)=0.919, q(LOO)(2)=0.898, F=1664.074, RMS=9.9215 for the training set, and R(2)=0.895, R(adj)(2)=0.892, q(ext)(2)=0.895, F=314.256, RMS=11.6427 for the external test set. This theoretical method provides a simple, precise and an alternative method to obtain λ(max) of azobenzene dyes.