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We have recently suggested using clustering of variables (CLoVA) based on unsupervised pattern recognition for partitioning variables into informative and redundant ones. Because data clustering plays a central role in CLoVA, in the present study, we compared the efficiency of different clustering methods including the Kohonen self-organizing map (SOM), principal component analysis, fuzzy c-means clustering, K-means clustering, and hierarchical cluster analysis for clustering spectroscopic data and molecular descriptors to build multivariate calibration and quantitative structure-activity (QSAR) models. To investigate which clustering methods are more efficient for CLoVA, four data sets (three spectroscopic and one QSAR) were analyzed. Most of the CLoVA-based models obtained by SOM resulted in the least root mean square errors of cross-validation and prediction, suggesting a higher efficiency of SOM for clustering variables. In all cases, the results obtained by the CLoVA-based method were compared with those obtained by conventional principal component regression as well as genetic algorithm and successive projection algorithm partial least square regression. Interestingly, models produced by the CLoVA-based method were more predictive with respect to that of the other methods, as indicated by the lowest root mean square error of prediction. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Total current in the electroanalytical data is assumed to be consisting of three main constituents: faradaic current, step charging current and induced charging current. Both charging currents can cause an interfering effect on precise determination of faradaic currents, and hence insert direct effects on sensitivity and detection limit of the electroanalytical techniques. Despite the widespread techniques introduced until now, the extraction of the net faradaic current from total current still remains a challenge. In this work, by using multivariate curve resolution-alternating least square (MCR-ALS) as a powerful curve resolution-based chemometrics method, a straightforward method has been introduced for resolving faradaic current from the two types of charging currents (step charging current and induced charging current) in single potential step and staircase cyclic voltammetric methods. By simultaneous analyses of the current data matrices for different electrochemical systems, the three sources of current were successfully identified and their contributions in the total signal were easily calculated. Also, in this manner, the cell time constant can be obtained easily. Contrary to the previously reported methods, the present method does not need any pre-determined mathematical method; particularly there is no need to know the cell time constant. [Copyright &y& Elsevier]

The combined data splitting feature selection (CDFS) is a new strategy in quantitative structure-property relation (QSPR) analysis, in which the sampling of training set is performed repeatedly to find a subset of molecular descriptors producing a stable QSPR model insensitive to the presence or absence of one or more compounds in the training set. Here, we used genetic algorithm-partial least square (GA-PLS) as modeling method in CDFS methodology and applied it to QSPR study of the GC retention of the analgesic drugs. A set of 58 analgesic drugs with known Kovats retention index were selected and a large number of theoretical descriptors was calculated for each molecule. The random sampling of the training set (80% of data) was performed 20 times and the remaining molecules were used as validation set. Each time, the most appropriate QSPR model was produced by GA-PLS. The selected descriptors of each run were then analyzed for similarity and frequency distribution. The final QSPR model, which obtained from the common descriptors between 50% of runs, possessed squared correlation coefficient of 0.924, 0.865 and 0.903 for training, validation and cross-validation, respectively. In addition, it was able to reproduce 85% of variances in the retention factor of external test set compounds. [ABSTRACT FROM AUTHOR]

Abstract: Although there are some distinctive peaks in mid-IR region of the electromagnetic spectrum for carbonyl compounds, it is very difficult to assign a FT-IR spectrum to a specific carbonyl functional group due to the presence of other functional groups, which change the position of the distinctive peaks. Here, we analyzed the FT-IR spectra of a large set of carbonyl compounds by chemometrics methods to differentiate between the different carbonyl functional groups. FT-IR spectra of 370 carbonyl compounds (149 carboxylic acids, 47 aldehydes, 110 esters and 64 ketones) were collected from the Spectral Database for Organic Compounds and then were converted to digital data using a home-made program. The extended canonical variate analysis combined with partial least squares discriminate analysis method (ECVA-PLS-DA) was employed as a supervised classification method. Classification analysis by ECVA-PLS-DA resulted in a suitable classification model such that one can discriminate between the different carbonyl compounds using FT-IR spectra with a small error. The classification errors (reported as percentage of misclassified compounds) were 1.8% and 7.8% for training and test sets, respectively. Considering high structural diversity of the studied compounds and the employment of different methods for acquiring the spectra (i.e., KBr disk, CCl4 solution, liquid film and Nujol moll) there are acceptable errors. Thus, it is concluded that with the help of chemometrics methods, one is able to differentiate the carbonyl compounds using their IR spectra without need to extra spectroscopic information. This can be considered as a significant improvement in structural characterization of organic compounds using only IR spectroscopy. [Copyright &y& Elsevier]

Abstract: The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS–GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS–GA algorithm performs better than that produced by recursive partitioning procedure. [Copyright &y& Elsevier]

new source of amino acid (AA) indices based on quantum topological molecular similarity (QTMS) descriptors has been proposed for use in QSAR study of peptides. For each bond of the chemical structure of AA, eight electronic properties were calculated using the approaches of bond critical point and theory of atom in molecule. Thus, for each molecule a data matrix of QTMS descriptors (having information from both topology and electronic features) were calculated. Using four different criterion based on principal component analysis of the QTMS data matrices, four different sets of AA indices were generated. The indices were used as the input variables for QSAR study (employing genetic algorithm-partial least squares) of three peptides' data sets, namely, angiotensin-converting enzyme inhibitors, bactericidal peptides and the peptides binding to the HLA-A*0201 molecule. The obtained models had better prediction ability or a comparable one with respect to the previously reported models. In addition, by using the proposed indices and analysis of the variable important in projection, the active site of the peptides which plays a significant role in the biological activity of interest, was identified. [ABSTRACT FROM AUTHOR]

Quantitative structure activity relationship (QSAR) analyses using a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices were operated to describe and compare the mechanisms of toxicity of phenols toward five different strains ( i.e., Tetrahymena pyriformis, L1210 Leukemia , Pseudomonas putida, Raja japonica and Cucumis sativus). The appropriate QSAR models for the toxicity data were obtained separately employing partial least squares (PLS) regression combined with genetic algorithms (GA), as a variable selection method. The resulting QSAR models were used to identify molecular fragments of phenol derivatives whose electronic properties contribute significantly to the observed toxicities. Using this information, it was feasible to discriminate between the mechanisms of action of phenol toxicity to the studied strains. It was found that toxicities of phenols to all strains, except with L1210 Leukemia, are significantly affected by electronic features of the phenolic hydroxyl group (C-O-H). Meanwhile, the resulting models can describe the inductive and resonance effects of substituents on various toxicities. [ABSTRACT FROM AUTHOR]

This article describes the use of the net analyte signal (NAS) concept and rank annihilation factor analysis (RAFA) for building two different multivariate standard addition models called “SANAS” and “SARAF.” In the former, by the definition of a new subspace, the NAS vector of the analyte of interest in an unknown sample as well as the NAS vectors of samples spiked with various amounts of the standard solutions are calculated and then their Euclidean norms are plotted against the concentration of added standard. In this way, a simple linear standard addition graph similar to that in univariate calibration is obtained, from which the concentration of the analyte in the unknown sample and the analytical figures of merit are readily calculated. In the SARAF method, the concentration of the analyte in the unknown sample is varied iteratively until the contribution of the analyte in the response data matrix is completely annihilated. The proposed methods were evaluated by analyzing simulated absorbance data as well as by the analysis of two indicators in synthetic matrices as experimental data. The resultant predicted concentrations of unknown samples showed that the SANAS and SARAF methods both produced accurate results with relative errors of prediction lower than 5% in most cases. [ABSTRACT FROM AUTHOR]

Quantum topological molecular similarity produces a two-dimensional array of descriptors for each molecule while a three-dimensional array is obtained by placing the descriptor data matrices of a set of molecules beside each other. Here, we use the multiway data analysis method called molecular maps (MOLMAP) of atom-level properties in a new way. We transferred the three-dimensional array of quantum topological molecular similarity descriptors into new two-dimensional parameters using Kohonen networks, followed by partial least squares. Six different data sets were analyzed by the proposed procedure, which were previously analyzed ( Eur. J. Med. Chem. 2006 41 862) by partial least squares applied to unfolded data. They include: (i) the p Ka of imidazoles, (ii) the ability of a set of indole derivatives to displace [3H] flunitrazepam from binding to bovine cortical membranes, (iii) the inhibitory effect of a set of benzimidazoles on the influenza virus, (iv) the interaction of amides with liver alcohol dehydrogenase, (v) inhibition of carbonic anhydrase by sulfonamides and (vi) the toxicity of a set of chlorophenols. Overall, the results showed better statistical results compared with simple unfolding. Furthermore, variable important in projection plots confirmed previous findings about active centers and even in some cases showed more accurate results. [ABSTRACT FROM AUTHOR]

Abstract: Nimesulide, a Cox-2 inhibitor anti-inflammatory drug, is a light sensitive compound and its biological activity is decreased upon photodegradation. The photodegradation kinetic of nimesulide was investigated spectrophotometrically using multivariate curve resolution analysis to overcome spectral overlapping of reactant and degradation products. The absorbance spectra of the nimesulide methanolic solution, exposed to daily sunlight, were recorded at different times. Three absorbing chemical species, coexisted in the reaction system, were detected by application of factor analysis to the absorbance data matrix. The soft-modeling multivariate curve resolution-alternative least square (MCR-ALS) analysis of the evolutionary absorbance data revealed that nimesulide undergoes photodegradation through a two-step consecutive manner, where both steps obey first-order kinetic. By application of the kinetic hard model constraint to the MCR-ALS analysis, an excellent agreement was obtained between the fitted concentration profiles and those obtained by soft method. The first-order rate constants of the first and second degradation products were calculated as 0.052 (±0.007) and 0.009 (±0.001)h−1, respectively. Finally, the pure spectra of the resolved chemical species were calculated, where that of nimesulide was the same as that obtained experimentally. [Copyright &y& Elsevier]

The usefulness of a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices for describing the quantitative effects of molecular electronic environments on the O-methylation kinetic of substituted phenols has been investigated. QTMS theory produces for each molecule a matrix of descriptors, containing bond (or structure) information in one dimension and electronic effects in another dimension, instead of other methods producing a vector of descriptors for each molecule. A collection of chemometrics tools including principal component analysis (PCA), partial least squares (PLS), and genetic algorithms (GA) were used to model the structure-kinetic data. PCA separated the bond and descriptor effects, and PLS modeled the effects of these parameters on the rate constant data, and GA selected the most relevant subset of variables. The model performances were validated by both cross-validation and external validation. The results indicated that the proposed models could explain about 95% of variances in the rate constant data. The significant effects of variables on the reaction kinetic were identified by calculating variable important in projection (VIP). It was found that the rate constant of esterification of phenols is highly influenced by the electronic properties of the C2&bond;C1&bond;O&bond;H fragment of the parent molecule. Indeed, the C2&bond;X and C4&bond;X bonds (corresponding to ortho and para substituents) were found as highly influential parameters. All of the eight calculated QTMS indices were found significant however, λ1, λ2, λ3, ε, and K(r) were detected as highly influential parameters. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2008 [ABSTRACT FROM AUTHOR]

Three-way analyses of quantum topological molecular similarity descriptors were used for quantitative structure property relationship modeling of the acidity constant of some phenol derivatives. A three-way data was built for different molecules by constructing a data matrix for each molecule. The matrix was produced by considering different bonds in each molecule and different descriptors in each bond. The three-way models parallel factor analysis and N-way partial least squares, and two-way models including partial least squares were used for modeling structure–acidity relationships. Comparison of the results showed that the three-way arrays produced more predictive models with lower over-fitting. The bilinear partial least square model resulted in a biased estimation of acidity constant of prediction set with average relative error of prediction of 1.87%, whereas that obtained by parallel factor analysis and N-way partial least squares was near to zero (i.e. −0.41 and −0.33, respectively). Additionally, the three-way methods allowed investigating the significant impact of different bonds and different descriptors using leverages of the parallel factor analysis loadings. [ABSTRACT FROM AUTHOR]

A quantitative structure activity relationship study has been applied to a data set of 18 sulfa drugs with carcinogenesis activity. Semi-empirical quantum chemical calculation (AM1 method) was used to find the optimum 3D geometry of the studied molecules. Two types of molecular descriptors including chemical and electronic was used to derive a quantitative relation between the carcinogenesis activity and structural properties. Two multi-parametric equations with good statistical qualities were obtained using genetic algorithms multiple linear regression (GA-MLR) methods. In addition, genetic algorithm-partial least squares (GA-PLS) regression was used to model the structure–activity relationships, more accurately. The results confirmed the superiority of the results obtained by GA-PLS relative to GA-MLR. The significant effect of the HOMO energy on the carcinogenic activity was explained in the context of the shape of this orbital. [Copyright &y& Elsevier]

• BSA well interacted with CYG through enthalpy driven hydrogen bonds and van der Waals forces. • A tiny amount of BSA could bind all CYG molecules (1:13). • Partially unfolded BSA induced by CYG could not change BSA denaturation temperature. • The stability of anthocyanins was increased after adding BSA. Anthocyanins as one of the main natural groups of food colorants undergo quick color fading, which can be diminished through protein association. The stabilization of cyanidin-3-glucoside (CYG) through binding to bovine serum albumin (BSA) was investigated at pH 3.0 using atomic force microscopy and differential scanning calorimetry along with UV–Vis absorption, steady-state fluorescence, circular dichroism, and three-dimensional emission spectral analyses merged with the multivariate curve resolution-alternative least square method. The stabilized CYG molecules were found at the site II of BSA with combined static and dynamic quenching mechanisms. Approximately 93% of the BSA binding sites were occupied in the BSA-CYG complex through hydrogen bonds and van der Waals forces with the binding constant and stoichiometry ratio of 1.88 × 105 M−1 and 1:13, respectively. The results also revealed that CYG molecules caused partial unfolding of the BSA structure, while it was not enough for significant alteration of denaturation temperature. The binding results also indicated that the reduction of H 2 O 2 -induced-CYG oxidation rate (34.78%) at pH 3.0 was mainly driven via the BSA-hemiketal association, although the colored species of CYG had a greater affinity towards BSA in the equilibrated system at pHs 1.0 and 5.0. [ABSTRACT FROM AUTHOR]

Edible oils are used in the preparation of foods as a part of their recipe or for frying. So to ensure of food safety, checking the quality of the oils before and after usage is an important subject in food control laboratories. In this study, edible oils from four different sources (canola, corn, sunflower and frying) were heated for 36 h at 170 °C and sampling was done every 6 h. The free fatty acid, peroxide value and the content of some fatty acids (C16:0, C18:0, C18:1, C18:2, C18:3) of the oil samples were determined by standard methods. Then, the ATR-FTIR spectra of the samples were collected. The partial least squares (PLS) regression combined with genetic algorithm was performed on the spectroscopic data to obtain the appropriate predictive models for the simultaneous estimation of acid value, peroxide value and the percentage of five kinds of fatty acids. The effect of some preprocessing methods on these models was also investigated. Preprocessing of data by orthogonal signal correction (OSC) resulted in the best predictive models for all oil properties. The correlation coefficients of calibration set (>0.99) and validation set (>0.86 and in most case >0.94) of the OSC-PLS model suggested suitable predictive modeling for all studied parameters in the oil samples. This method could be suggested as a rapid, economical and environmental friendly technique for simultaneous determination of seven noted parameters in the edible oils. [ABSTRACT FROM AUTHOR]