S. P. Mushtakova

Chemometrics-assisted spectrophotometric method for simultaneous determination of vitamins in complex mixtures

An advanced independent component analysis algorithm (MILCA) is applied for simultaneous chemometric determination of fat- and water-soluble vitamins in complex mixtures. The analysis is based on the decomposition of spectra of multicomponent mixtures in the UV region. The key features of the proposed method are simplicity, accuracy, and reliability. Comparisons between the new algorithm and other established methods (MCR-ALS, SIMPLISMA, other ICA techniques) were made. Our results indicate that in most cases, MILCA is comparable or even outperforms other chemometrics methods taken for comparisons. The influence of different factors (abundance of components, noise, step of spectral scan, and scan speed) on decomposition performance has been investigated. The optimal conditions for spectroscopic registration have been identified. The proposed method was used for analysis of model mixtures and real objects (multivitamin drugs, food additives, and energy drinks). The resolved concentrations match well with the declared amounts and the results of reference methods.

Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis: application to foods and related products

The major challenge facing NMR spectroscopic mixture analysis is the overlapping of signals and the arising impossibility to easily recover the structures for identification of the individual components and to integrate separated signals for quantification. In this paper, various independent component analysis (ICA) algorithms [mutual information least dependent component analysis (MILCA); stochastic non‐negative ICA (SNICA); joint approximate diagonalization of eigenmatrices (JADE); and robust, accurate, direct ICA algorithm (RADICAL)] as well as deconvolution methods [simple‐to‐use‐interactive self‐modeling mixture analysis (SIMPLISMA) and multivariate curve resolution‐alternating least squares (MCR‐ALS)] are applied for simultaneous 1H NMR spectroscopic determination of organic substances in complex mixtures. Among others, we studied constituents of the following matrices: honey, soft drinks, and liquids used in electronic cigarettes. Good quality spectral resolution of up to eight‐component mixtures was achieved (correlation coefficients between resolved and experimental spectra were not less than 0.90). In general, the relative errors in the recovered concentrations were below 12%. SIMPLISMA and MILCA algorithms were found to be preferable for NMR spectra deconvolution and showed similar performance. The proposed method was used for analysis of authentic samples. The resolved ICA concentrations match well with the results of reference gas chromatography–mass spectrometry as well as the MCR‐ALS algorithm used for comparison. ICA deconvolution considerably improves the application range of direct NMR spectroscopy for analysis of complex mixtures. Copyright

Investigations into the catalytic activity of rhodium(III) in red-ox reactions by capillary zone electrophoresis

Speciation of rhodium(III) in different acidic media has been studied by capillary zone electrophoresis (CZE). Depending on the nature of the acid, rhodium was shown to occur in the form of positive, neutral and/or negatively charged complexes. The relationship between the distribution of rhodium forms and its catalytic action on the oxidation of N-methyldiphenylamine-4-sulfonic acid by periodate ions has been investigated. It was found that only positively charged complexes of rhodium, such as those dominating in perchloric acid solutions, catalyzed a given reaction to form a colored oxidation product. The rate of the catalyzed reaction was optimized with respect to the pH, reagent and oxidant concentration levels, ionic strength, concentration of the catalyst, as well as the presence of interfering ions. The developed kinetic spectrophotometric method features rather high sensitivity (limit of determination 10 mug l(-1)) and tolerance for most platinum metals and was applied to a complex industrial sample of a platinum concentrate.

Methods of the decomposition of spectra of various origin in the analysis of complex mixtures

Different algorithms of the decomposition of spectral curves are compared by the precision of identification and quantitative analysis of complex mixtures. The available conventional methods of self-modeling curve resolution (SIMPLISMA, MCR-ALS) and algorithms implementing the independent component analysis (MILCA, SNICA) are used. The results are illustrated by a series of examples of different spectral signals (UV, IR, Raman, fluorescence).

Spectral-chemometric and quantum-chemical study of the water-acetonitrile system

Association of molecules in the solution of acetonitrile and its binary mixtures with water has been studied using spectroscopy in the near-IR region (800–1100 nm) and the chemometric method of independent components implemented as a MILCA algorithm. The decomposition of the spectral curves of solutions has aimed to determine the number, structure, size, and stability of the associates in the wide concentration range (0–100%). The well-known MCR-ALS algorithm has been used to confirm the accuracy of the independent component analysis. Quantum-chemical computation has been performed for particles occurring in the solution. The results explain some abnormalities observed for water-acetonitrile solutions in reversephase chromatography.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements.

Independent component analysis algorithms for spectral decomposition in UV/VIS analysis of metal-containing mixtures including multimineral food supplements and platinum concentrates

Various independent component analysis (ICA) algorithms (MILCA, JADE, SIMPLISMA, RADICAL) are applied for simultaneous spectroscopic determination of two groups of transition metals: Co(II)–Fe(III)–Cu(II)–Zn(II)–Ni(II) and Pt(IV)–Pd(II)–Ir(IV)–Rh(III)–Ru(III)) in complex mixtures. The analysis is based on the decomposition of spectra of multicomponent mixtures in the UV-VIS region based on the natural absorbance of metal salts, or, when a better sensitivity is desirable, based on the absorbance of their complexes with 4-(2-pyridylazo)resorcinol (PAR) and ethylenediaminetetraacetic acid (EDTA). Good quality spectral resolution of up to seven-component mixtures was achieved (correlation coefficients between resolved and experimental spectra are not less than 0.90). In general, the relative errors in the recovered concentrations are at levels of only several percent. While being superior to other ICA algorithms, MILCA is comparable or even outperforms other classical chemometric methods for quantitative analysis that were used for comparison purposes (Partial Least Squares (PLS), Principal Component Regression (PCR), Alternating Least Squares (ALS)). Simultaneous quantitative analysis is possible for mixtures containing up to five metals in the broad concentration ranges even when individual spectra show 99% overlap. A small excess of derivatization reagent (till threefold excess to the sum of metal concentrations) is optimal to obtain good quantitative results. The proposed method was used for analysis of authentic samples (multimineral supplements and platinum concentrates). The resolved ICA concentrations match well with the labelled amounts and the results of other chemometric methods (ALS, PLS). ICA decomposition considerably improves the application range of spectroscopy for metal quantification in mixtures.

Association in solutions of monoatomic alcohols and their mixtures with water

The association of certain monoatomic aliphatic alcohols in tetrachloromethane and in wateralcohol mixtures was studied by spectroscopy (from 800 to 1100 nm), the self-modelling curve resolution (using the MILCA algorithm), and quantum chemistry. The decomposition of spectral curves was used to determine the number, composition, and stability of associates over a wide range of concentrations (from 0 to 100 wt %). The ranges of concentration were determined for the existence of homo- and heteromolecular associates of alcohols. Our conclusions based on chemometric method were confirmed by quantum chemical simulations of the particles that can exist in a solution, and the data on the structure of associates of monoatomic alcohols were supplemented.

Standardless Spectral Analysis of Independent Mixture Components: Experimental Case Studies

We applied recently proposed methods for mixture decomposition in statistically independent components (MILCA and SNICA) to solve practical problems in analytical spectroscopy. These methods aim at reconstructing the spectra of individual mixture components and their concentrations from linear mixtures. Here, they were applied to experimental standardless qualitative and quantitative analysis in UV and visible absorption spectroscopy. However, this family of methods can be coupled with almost any sort of spectroscopic measurements. The results are presented through a series of experimental case studies, including the analysis of major ecotoxicants, polyaromatic hydrocarbons.

Application of modern chemometric methods to the study of equilibria in solutions

The possibility of the spectrometric-chemometric study of equilibria in solutions is demonstrated for substances with strongly overlapping spectra, in particular, using the independent component analysis (MILCA and SIMPLISMA algorithms) and the alternating least squares algorithm (MCR-ALS). Using the chemometric approach allows one to resolve spectral curves, identify species present in the solution, and calculate the characteristics of equilibria. The proposed approach is illustrated on a series of examples (study of a tautomeric equilibrium and complexation reactions).