Development of a Model to Predict Partition Coefficient of Organic Pollutants in Cloud Point Extraction Process

Study of monoprotic acid-base equilibria in aqueous micellar solutions of nonionic surfactants using spectrophotometry and chemometrics

Many studies have shown the distribution of solutes between aqueous phase and micellar pseudo-phase in aqueous micellar solutions. However, spectrophotometric studies of acid-base equilibria in these media do not confirm such distribution because of the collinearity between concentrations of chemical species in the two phases. The collinearity causes the number of detected species to be equal to the number of species in a homogenous solution that automatically misinterpreted as homogeneity of micellar solutions, therefore the collinearity is often neglected. This interpretation is in contradiction to the distribution theory in micellar media that must be avoided. Acid-base equilibrium of an indicator was studied in aqueous micellar solutions of a nonionic surfactant to address the collinearity using UV/Visible spectrophotometry. Simultaneous analysis (matrix augmentation) of the equilibrium and solvation data was applied to eliminate the collinearity from the equilibrium data. A model was then suggested for the equilibrium that was fitted to the augmented data to estimate distribution coefficients of the species between the two phases. Moreover, complete resolution of concentration and spectral profiles of species in each phase was achieved.

A quantitative structure property relationship for prediction of solubilization of hazardous compounds using GA-based MLR in CTAB micellar media

QSPR studies for estimating the incorporation organic hazardous compounds in cationic surfactant (CTAB) were developed by application of the structural descriptors and multiple linear regression (MLR) method. Various structure-related descriptors were studied in order to derive information on hydrophobic, electronic and steric properties of solute molecules. Theoretical molecular descriptors selected by genetic algorithms-procedure were followed to predict logKs values by a stepwise-MLR method. A simple model with low standard errors and high correlation coefficients was selected. It was also found that MLR method could model the relationship between solubility and structural descriptors perfectly. The proposed methodology was validated using full cross validation and external validation using division of the available data set into training and test sets. The squared regression coefficient of prediction for the MLR model was 0.9624. The results illustrated that the linear techniques such as MLR combined with a successful variable selection procedure are capable to generate an efficient QSPR model for predicting the solubility of different compounds. The proposed model can be used adequately for the prediction and description of the solubility of organic compounds in micellar solutions.

Prediction of gas chromatography/electron capture detector retention times of chlorinated pesticides, herbicides, and organohalides by multivariate chemometrics methods

A quantitative structure-retention relationship (QSRR) study, has been carried out on the gas chromatograph/electron capture detector (GC/ECD) system retention times (t(R)s) of 38 diverse chlorinated pesticides, herbicides, and organohalides by using molecular structural descriptors. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and partial least squares (PLS) regression. The stepwise regression using SPSS was used for the selection of the variables that resulted in the best-fitted models. Appropriate models with low standard errors and high correlation coefficients were obtained. Three types of molecular descriptors including electronic, steric and thermodynamic were used to develop a quantitative relationship between the retention times and structural properties. MLR and PLS analysis has been carried out to derive the best QSRR models. After variables selection, MLR and PLS methods used with leave-one-out cross validation for building the regression models. The predictive quality of the QSRR models were tested for an external prediction set of 12 compounds randomly chosen from 38 compounds. The PLS regression method was used to model the structure-retention relationships, more accurately. However, the results surprisingly showed more or less the same quality for MLR and PLS modeling according to squared regression coefficients R2 which were 0.951 and 0.948 for MLR and PLS, respectively.

Micellar solid-phase microextraction for determining partition coefficients of substituted polycyclic aromatic hydrocarbons in micellar media: possible prediction of hydrocarbon–micelle behaviour

Micellar solid-phase microextraction (MSPME) coupled to gas chromatography-mass spectrometry (GC-MS) has been used to obtain partition coefficients of a group of 18 substituted aromatic hydrocarbons to ionic and nonionic micelles. Statistical and factor analyses have been utilized to establish some general equations relating molecular descriptors of non-substituted polycyclic aromatic hydrocarbons and their partition coefficients obtained by MSPME. The obtained equations have correlation coefficients higher than 0.94. They are used to predict hydrocarbon-micelle partition coefficients for a group of hydrocarbons with reported literature values giving a correlation coefficient of 0.98 and a standard deviation of the prediction of 0.182. The predictive model was also applied to substituted polycyclic aromatic hydrocarbons with partition coefficient values obtained by MSPME, with a 69% level of success.