Anti-HIV activity for a set of 107 inhibitors of the HIV-1 reverse transcriptase, derivatives of 1-[2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT), was modeled with the aid of chemometric techniques. The activity of these compounds was estimated by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques and compared with the previous works. The results obtained using the MLR method indicate that the anti-HIV activity of the HEPT derivatives depends on the reverse of standard shadow area on the YZ plane and the ratio of the partial charges of the most positive atom to the most negative atom of the molecule. The best computational neural network model was a fully-connected, feed-forward method with a 6-6-1 architecture. The mean-square error for the prediction set using this network was 0.372 compared with 0.780 obtained using the MLR technique. Comparison of the quality of the ANN of this work with different MLR models shows that ANN has a better predictive power.

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) combined to multivariate curve resolution-alternating least-squares (MCR-ALS) is proposed for the resolution and quantification of very complex mixtures of compounds such as polycyclic aromatic hydrocarbons (PAHs) in heavy fuel oil (HFO). Different GC × GC-TOFMS data slices acquired during the analysis of HFO samples and PAH standards were simultaneously analyzed using the MCR-ALS method to resolve the pure component elution profiles in the two chromatographic dimensions as well as their pure mass spectra. Outstandingly, retention time shifts within and between GC × GC runs were not affecting the results obtained using the proposed strategy and proper resolution of strongly coeluted compounds, baseline and background contributions was achieved. Calibration curves built up with standard samples of PAHs allowed the quantification of ten of them in HFO aromatic fractions. Relative errors in their estimated concentrations were in all cases below 6%. The obtained results were compared to those obtained by commercial software provided with GC × GC-TOFMS instruments and to Parallel Factor Analysis (PARAFAC). Inspection of these results showed improvement in terms of data fitting, elution process description, concentration relative errors and relative standard deviations.

Rotational ambiguity is a central but not well investigated problem in all soft-modelling analyses of multivariate data. A novel method which is based on Resolving Factor Analysis (RFA) is proposed. Completely general and exhaustive results are presented for the two-component case. In particular the effects of noise and global analysis of series of measurements are investigated.

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A hybrid method consisting of principal component analysis (PCA), multiple linear regressions (MLR), and artificial neural network (ANN) was developed to predict the retention time of 149 C(3)-C(12) volatile organic compounds for a DB-1 stationary phase. PCA and MLR methods were used as feature-selection tools, and a neural network was employed for predicting the retention times. The regression method was also used as a calibration model for calculating the retention time of VOCs and investigating their linear characteristics. The descriptors of the total information index of atomic composition, IAC, Wiener number, W, solvation connectivity index, X1sol, and number of substituted aromatic C(sp(2)), nCaR, appeared in the MLR model and were used as inputs for the ANN generation. Appearance of these parameters shows the importance of the dispersion interactions in the mechanism of retention. Comparison of the MLR and 5-2-1 ANN models indicates the superiority of the ANN over that of the MLR model. The values of 0.913 and 0.738 were obtained for the standard error of prediction set of MLR and ANN models, respectively.

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