Enantiomeric separation of chiral polychlorinated biphenyls on beta-cyclodextrin capillary columns by means of heart-cut multidimensional gas chromatography and comprehensive two-dimensional gas chromatography. Application to food samples

Three commercially available chiral capillary columns, Chirasil-Dex, BGB-176SE, and BGB-172, have been evaluated for the separation into enantiomers of the 19 chiral polychlorinated biphenyls (PCB) congeners stable at room temperature. The enantiomers of 15 chiral PCBs were, at least to some extent, separated using these beta-cyclodextrin based columns. Multidimensional techniques, such as heart-cut multi-dimensional gas chromatography (heart-cut MDGC) and comprehensive two-dimensional gas chromatography (GC x GC), were investigated for their ability to solve coelution problems with other PCBs present in commercial mixtures and real-life samples. Heart-cut MDGC improved the separation as compared to one-dimensional GC, and enantiomeric fractions of the investigated chiral PCBs could be determined free from interferences. However, limitations on the number of target compounds that can be transferred to the second column in a single run and, therefore, the time consumption, have led to the evaluation of GC x GC as an alternative for this type of analysis. With GC x GC, two column set-ups were tested, both having a chiral column as first-dimension column, and two different polar stationary phase columns in the second dimension. On using both column combinations, congeners 84, 91, 95, 132, 135, 136, 149, 174, and 176 could be determined free from coelutions with other PCBs. Results on the application of heart-cut MDGC to food samples such as milk and cheese are given, as well as the first results on the application of GC x GC to this type of samples.     

Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme

Summary Inter-residue contacts map prediction is one of the most important intermediate steps to the protein folding problem. In this paper, we focus on the problem of protein inter-residue contacts map prediction based on neural network technique. Firstly, we use a genetic algorithm (GA) to optimize the radial basis function widths and hidden centers of a radial basis function neural network (RBFNN), then a novel binary encoding scheme is employed to train the network for the purpose of learning and predicting the inter-residue contacts patterns of protein sequences got from the protein data bank (PDB). The experimental evidence indicates the utility of our proposed encoding strategy and GA optimized RBFNN. Moreover, the simulation results demonstrate that the network got a better performance for these proteins, whose residue length falls into the area of (100, 300), and the predicted accuracy with a contact threshold of 7 Å scores higher than the other 3 values with 5, 6, and 8 Å .     

A simple,fast, and accurate thermodynamic‐based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part III: Retention time prediction on target column

This is the third part of a three‐part series of papers. In Part I, we presented a method for determining the actual effective geometry of a reference column as well as the thermodynamic‐based parameters of a set of probe compounds in an in‐house mixture. Part II introduced an approach for estimating the actual effective geometry of a target column by collecting retention data of the same mixture of probe compounds on the target column and using their thermodynamic parameters, acquired on the reference column, as a bridge between both systems. Part III, presented here, demonstrates the retention time transfer and prediction from the reference column to the target column using experimental data for a separate mixture of compounds. To predict the retention time of a new compound, we first estimate its thermodynamic‐based parameters on the reference column (using geometric parameters determined previously). The compound's retention time on a second column (of previously determined geometry) is then predicted. The models and the associated optimization algorithms were tested using simulated and experimental data. The accuracy of predicted retention times shows that the proposed approach is simple, fast, and accurate for retention time transfer and prediction between gas chromatography columns.     

Extending the working calibration ranges of four hexachlorocyclohexane isomers in gas chromatography-electron capture detector by radial basis function neural network

A radial basis function neural network (RBFNN) method was developed for the first time to model the nonlinear calibration curves of four hexachlorocyclohexane (HCH) isomers, aiming to extend their working calibration ranges in gas chromatography-electron capture detector (GC-ECD). Other 14 methods, including seven parametric curve fitting methods, two nonparametric curve fitting methods, and five other artificial neural network (ANN) methods, were also developed and compared. Only the RBFNN method, with logarithm-transform and normalization operation on the calibration data, was able to model the nonlinear calibration curves of the four HCH isomers adequately. The RBFNN method accurately predicted the concentrations of HCH isomers within and out of the linear ranges in certified test samples. Furthermore, no significant difference (p > 0.05) was found between the results of HCH isomers concentrations in water samples calculated with RBFNN method and ordinary least squares (OLS) method (R² > 0.9990). Conclusively, the working calibration ranges of the four HCH isomers were extended from 0.08-60 ng/ml to 0.08-1000 ng/ml without sacrificing accuracy and precision by means of RBFNN. The outstanding nonlinear modeling capability of RBFNN, along with its universal applicability to various problems as a “soft” modeling method, should make the method an appealing alternative to traditional modeling methods in the calibration analyses of various systems besides the GC-ECD.     

A simple,fast, and accurate thermodynamic‐based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part I: Estimation of reference column geometry and thermodynamic parameters

The transfer of retention times based on thermodynamic models between columns can aid in separation optimization and compound identification in gas chromatography. Although earlier investigations have been reported, this problem remains unsuccessfully addressed. One barrier is poor predictive accuracy when moving from a reference column or system to a new target column or system. This is attributed to challenges associated with the accurate determination of the effective geometric parameters of the columns. To overcome this, we designed least squares‐based models that account for geometric parameters of the columns and thermodynamic parameters of compounds as they partition between mobile and stationary phases. Quasi‐Newton‐based algorithms were then used to perform the numerical optimization. In this first of three parts, the model used to determine the geometric parameters of the reference column and the thermodynamic parameters of compounds subjected to separation is introduced. As will be shown, the overall approach significantly improves the predictive accuracy and transferability of thermodynamic data (and retention times) between columns of the same stationary phase chemistry. The data required for the determination of the thermodynamic parameters and retention time prediction are obtained from fast and simple experiments. The proposed model and optimization algorithms were tested and validated using simulated and experimental data.