Resolution of Differential Pulse Voltammetric Peaks Using Genetic Algorithm Based Variable Selection‐Partial Least Squares and Principal Component‐Artificial Neural Networks

Differential Pulse Voltammetry has been used for the simultaneous determination of cysteine, tyrosine and trptophan on the unmodified glassy carbon electrode. In the analysis of these analytes in the same samples, the main difficulty is the high degree of overlapping of voltammograms. The relationships between the currents and the concentrations are complex and highly nonlinear. The predictive ability of principal component regression (PCR), partial least squares regression (PLS), genetic algorithm‐partial least squares regression (GA‐PLS) and principal component‐artificial neural networks (PC‐ANNs) were examined for simultaneous determination of three amino acids. For a regression model, everything that could not help in constructing the model may be considered as noise without further specification. PC‐ANN and GA‐PLS use significant data and show superiority over other applied multivariate methods. The proposed method was also applied satisfactorily to determination of analytes in some synthetic samples.     

A novel algorithm for linear multivariate calibration based on the mixed model of samples

We present a novel algorithm for linear multivariate calibration that can generate good prediction results. This is accomplished by the idea of that testing samples are mixed by the calibration samples in proper proportion. The algorithm is based on the mixed model of samples and is therefore called MMS algorithm. With both theoretical support and analysis of two data sets, it is demonstrated that MMS algorithm produces lower prediction errors than partial least squares (PLS2) model, has similar prediction performance to PLS1. In the anti-interference test of background, MMS algorithm performs better than PLS2. At the condition of the lack of some component information, MMS algorithm shows better robustness than PLS2.     

Chemometric data analysis of pollutants in wastewater—a case study

In this study, chemometric techniques such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and partial least squares (PLS) were used to analyse the wastewater dataset to identify the factors which affect the composition of sewage of domestic origin, spatial and temporal variations, similarity/dissimilarity among the wastewater characteristics of cis- and trans-drains and discriminating variables. Samples collected from 24 wastewater drains in Lucknow city and from three sites on Gomti river in the month of January/February, May, August and November during the period of 5 years (1994-1999) were characterized for 32 parameters. The multivariate techniques successfully described the similarities/dissimilarities among the sewage drains on the basis of their wastewater characteristics and sources signifying the effect of routine domestic/commercial activities in respective drainage areas. Spatial and seasonal variations in wastewater composition were also determined successfully. CA generated six groups of drains on the basis of similar wastewater characteristic. PCA provided information on seasonal influence and compositional differences in sewage generated by domestic and industrial waste dominated drains and showed that drains influenced by mixed industrial effluents have high organic pollution load. DA rendered six variables (TDS, alkalinity, F, TKN, Cd and Cr) discriminating between cis- and trans-drains. PLS-DA showed dominance of Cd, Cr, NO₃, PO₄ and F in cis-drains wastewater. The results suggest that biological-process based STPs could treat wastewater both from the cis- as well as trans-drains, however, prior removal of toxic metals will be required from the cis-drains sewage. Further, seasonal variations in wastewater composition and pollution load could be the guiding factor for determining the STPs design parameters. The information generated would be useful in selection of process type and in designing of the proposed sewage treatment plants (STPs) for safe disposal of wastewater.     

Comparison of partial least squares regression and multi-layer neural networks for quantification of nonlinear systems and application to gas phase Fourier transform infrared spectra

The performance of back-propagation artificial neural networks (NN) and partial least squares (PLS) regression for the calibration of linear and nonlinear systems has been investigated by using six types of synthetic data. Three PLS methods, conventional linear-PLS and two nonlinear-PLS methods, have been used in the study. In all but one of the synthetic data types, the band intensities varied nonlinearly with concentration. These five data types were designed to represent the effect of band shifts with increasing concentration, a nonlinear relationship between peak height and concentration, or a combination of both types of nonlinearities. The results showed that NNs perform better than PLS for all the nonlinear datasets. When a band shift is the major reason for the nonlinearity, the relative performance of NNs and PLS depends on the overlap of the absorption bands. If there is no band overlap, neither NN nor PLS can calibrate the data accurately but the results could be improved by convolving the spectral features with a Gaussian broadening function. The results indicate that a combination of peak position shift and peak height change is the most difficult nonlinearity to calibrate. NN and PLS were also used to determine the concentration of CHCl₃ in pure component and mixtures of CHCl₃ and CH₂Cl₂ using their Fourier transform infrared (FT-IR) spectra, a dataset that has been proved nonlinear in high concentrations due to the nonlinear response of the detector. The best results for the experimental data were obtained by applying one hidden layer NN to the mean-centered absorbance spectra.     

Multivariate analytical sensitivity in the determination of selenium, copper, lead and cadmium by stripping voltammetry when using soft calibration

A new approach to the multivariate sensitivity concept based on the determination of the capability of discrimination of a method of analysis is shown. Thus the analytical sensitivity is defined in this work by the analyte concentration that a analytical method is able to discriminate, which implies the estimation of the ‘false noncompliance’ and ‘false compliance’. In this approach the estimation of the multivariate analytical sensitivity is independent of scale factors and calibration models, and allows one to study the behavior of a analytical method for several concentrations and matrix. The estimation of this parameter in the simultaneous determination of selenium, copper, lead and cadmium by stripping voltammetry when using soft calibration is carried out, showing that different multivariate analytical sensitivities are obtained for each metal.     

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks

This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.     

A new method of comparing 2D-PAGE maps based on the computation of Zernike moments and multivariate statistical tools

The aim of this work was to obtain the correct classification of a set of two-dimensional polyacrylamide gel electrophoresis map images using the Zernike moments as discriminant variables. For each 2D-PAGE image, the Zernike moments were computed up to a maximum p order of 100. Partial least squares discriminant analysis with variable selection, based on a backward elimination algorithm, was applied to the moments calculated in order to select those that provided the lowest error in cross-validation. The new method was tested on four datasets: (1) samples belonging to neuroblastoma; (2) samples of human lymphoma; (3) samples from pancreatic cancer cells (two cell lines of control and drug-treated cancer cells); (4) samples from colon cancer cells (total lysates and nuclei treated or untreated with a histone deacetylase inhibitor). The results demonstrate that the Zernike moments can be successfully applied for fast classification purposes. The final aim is to build models that can be used to achieve rapid diagnosis of these illnesses.     

Determination of oil and water content in olive pomace using near infrared and Raman spectrometry. A comparative study

Near infrared (NIR) reflectance and Raman spectrometry were compared for determination of the oil and water content of olive pomace, a by-product in olive oil production. To enable comparison of the spectral techniques the same sample sets were used for calibration (1.74–3.93% oil, 48.3–67.0% water) and for validation (1.77–3.74% oil, 50.0–64.5% water). Several partial least squares (PLS) regression models were optimized by cross-validation with cancellation groups, including different spectral pretreatments for each technique. Best models were achieved with first-derivative spectra for both oil and water content. Prediction results for an independent validation set were similar for both techniques. The values of root mean square error of prediction (RMSEP) were 0.19 and 0.20–0.21 for oil content and 2.0 and 1.8 for water content, using Raman and NIR, respectively. The possibility of improving these results by combining the information of both techniques was also tested. The best models constructed using the appended spectra resulted in slightly better performance for oil content (RMSEP 0.17) but no improvement for water content.     

Fast chromatographic method for the determination of dyes in beverages by using high performance liquid chromatography—Diode array detection data and second order algorithms

A fast chromatographic methodology is presented for the analysis of three synthetic dyes in non-alcoholic beverages: amaranth (E123), sunset yellow FCF (E110) and tartrazine (E102). Seven soft drinks (purchased from a local supermarket) were homogenized, filtered and injected into the chromatographic system. Second order data were obtained by a rapid LC separation and DAD detection. A comparative study of the performance of two second order algorithms (MCR-ALS and U-PLS/RBL) applied to model the data, is presented. Interestingly, the data present time shift between different chromatograms and cannot be conveniently corrected to determine the above-mentioned dyes in beverage samples. This fact originates the lack of trilinearity that cannot be conveniently pre-processed and can hardly be modelled by using U-PLS/RBL algorithm. On the contrary, MCR-ALS has shown to be an excellent tool for modelling this kind of data allowing to reach acceptable figures of merit. Recovery values ranged between 97% and 105% when analyzing artificial and real samples were indicative of the good performance of the method. In contrast with the complete separation, which consumes 10 mL of methanol and 3 mL of 0.08 mol L⁻¹ ammonium acetate, the proposed fast chromatography method requires only 0.46 mL of methanol and 1.54 mL of 0.08 mol L⁻¹ ammonium acetate. Consequently, analysis time could be reduced up to 14.2% of the necessary time to perform the complete separation allowing saving both solvents and time, which are related to a reduction of both the costs per analysis and environmental impact.     

Tracing the origin of extra virgin olive oils by infrared spectroscopy and chemometrics: A case study

In this paper, the potential of coupling mid- and near-infrared spectroscopic fingerprinting techniques and chemometric classification methods for the traceability of extra virgin olive oil samples from the PDO Sabina was investigated. To this purpose, two different pattern recognition algorithm representative of the discriminant (PLS-DA) and modeling (SIMCA) approach to classification were employed. Results obtained after processing the spectroscopic data by PLS-DA evidenced a rather high classification accuracy, NIR providing better predictions than MIR (as evaluated both in cross-validation and on an external test set). SIMCA confirmed these results and showed how the category models for the class Sabina can be rather sensitive and highly specific. Lastly, as samples from two harvesting years (2009 and 2010) were investigated, it was possible to evidence that the different production year can have a relevant effect on the spectroscopic fingerprint. Notwithstanding this, it was still possible to build models that are transferable from one year to another with good accuracy.     

The study of the relationship between the new topological index A(m) and the gas chromatographic retention indices of hydrocarbons by artificial neural networks

The newly developed topological indices A_m1-A_m3 and the molecular connectivity indices ^mX were applied to multivariate analysis in structure-property correlation studies. The topological indices calculated from the chemical structures of some hydrocarbons were used to represent the molecular structures. The prediction of the retention indices of the hydrocarbons on three different kinds of stationary phase in gas chromatography can be achieved applying artificial neural networks and multiple linear regression models. The results from the artificial neural networks approach were compared with those of multiple linear regression models. It is shown that the predictive ability of artificial neural networks is superior to that of multiple linear regression method under the experimental conditions in this paper. Both the topological indices ²X and A_m1 can improve the predicted results of the retention indices of the hydrocarbons on the stationary phase studied.     

Combining chromatography and chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional data—A review

The characterization and authentication of fats and oils is a subject of great importance for market and health aspects. Identification and quantification of triacylglycerols in fats and oils can be excellent tools for detecting changes in their composition due to the mixtures of these products. Most of the triacylglycerol species present in either fats or oils could be analyzed and identified by chromatographic methods. However, the natural variability of these samples and the possible presence of adulterants require the application of chemometric pattern recognition methods to facilitate the interpretation of the obtained data. In view of the growing interest in this topic, this paper reviews the literature of the application of exploratory and unsupervised/supervised chemometric methods on chromatographic data, using triacylglycerol composition for the characterization and authentication of several foodstuffs such as olive oil, vegetable oils, animal fats, fish oils, milk and dairy products, cocoa and coffee.