Yu. B. Monakhova

Chemometric methods in NMR spectroscopic analysis of food products

The main achievements of high-resolution NMR spectroscopy in combination with different chemometric methods in the analysis of food products in the last 40 years are reviewed. The essence of chemometric methods used for the analysis and interpretation of NMR spectra is briefly described. Sample preparation for NMR-spectroscopic analysis is characterized. Methods for the mathematical treatment of NMR spectra (smoothing, Fourier transformation, bucketing, normalization, and selection of spectral ranges) are considered. Currently available methods for the suppression of the signals of macrocomponents, including those for the simultaneous suppression of several signals, are described. The results are illustrated based on examples of analysis of different classes of foodstuffs and beverages with the use of NMR spectroscopy and chemometric methods for classification and discrimination (geographical and botanical origin as well as validating checking the brand authenticity).

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.

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.

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).

Independent components analysis as an alternative to principal component analysis and discriminant analysis algorithms in the processing of spectrometric data

The possibility of the application of independent component analysis (ICA) to searching patterns in the spectrometric datasets and to discriminating objects is demonstrated. The data of XRF analysis of base enamels, IR spectra of automotive lacquers, and 1H NMR spectra of wines from different regions of Germany are selected for the study. In all three cases, ICA reliably separates groups of objects, increasing the percentage of correct predictions for new samples not included into the model. Moreover, ICA gives results comparable with specialized discriminant analysis methods (linear discriminant analysis, projections to latent structures discriminant analysis, and factorial discriminant analysis) in the classification of the NMR spectra of wines.

Processing of NMR, UV, and IR spectrometric data prior to chemometric simulation by independent component and principal component analysis

We consider methods for the mathematical preprocessing of signals in the spectrometric analysis of multicomponent mixtures using chemometric algorithms aimed at adjusting the baseline, experimental noise, and random shift of spectral bands. Practical examples of using simple mathematical operations (scaling, centering, derivatization) are given. The effectiveness of algorithms is illustrated by a wide range of spectroscopic signals (electronic absorption, IR, and NMR spectra) combined with chemometric methods of principal component analysis and independent component analysis.

Estimation of the main characteristics of reagents of the diphenylamine series using quantum chemistry and chemometrical methods

The possibility of using quantum chemistry and chemometrical methods for the a priori estimation of the major analytical characteristics of analytical reagents has been demonstrated by the example of compounds of the diphenylamine series. The employed methods for the estimation of the characteristics of compounds are simple and rapid and are equal to the known experimental approaches in terms of precision.