Study of the hydrolysis of a sulfonylurea herbicide using liquid chromatography with diode array detection and mass spectrometry by three-way multivariate curve resolution-alternating least squares

Multivariate curve resolution combined with estimation by cosine similarity mapping of analytical data

We developed a multivariate curve resolution (MCR) calculation combined with the mapping of cosine similarity (cos-s) for estimating multiple mixture spectra of chemicals. The cos-s map was obtained by calculating the similarities of the variation of the signal intensities at each scanning parameter, such as the wavelength. The cos-s map was utilized for the initial estimation of the spectra of pure chemicals and also for the restriction of the iterative least-squares calculation of the MCR. These calculations were performed without arbitrary parameters by introducing soft clustering to the cos-s map. The chemically meaningful initial estimation could prevent the convergence at an incorrect local minimum, which frequently happens for the wrong initial estimation of spectra far away from the real answer. Herein, we demonstrated the robustness of this calculation method by applying it for UV/Vis spectra and XRD patterns of multiple unknown chemical mixtures, whose shapes were totally different (broad overlapped peaks and multiple complicated peaks). Pure spectra/patterns were recovered as >84% consistency with the reference spectra, and <6% accuracy of the concentration ratios was demonstrated.

An initial estimation method using cosine similarity for multivariate curve resolution: application to NMR spectra of chemical mixtures

Mixture spectra is decomposed into pure spectra without prior knowledge, and the MCR calculation refines the spectra and provides the concentrations.

Multivariate curve resolution-alternating least squares (MCR-ALS) applied to spectroscopic data from monitoring chemical reactions processes

This paper overviews the application of multivariate curve resolution (optimized by alternating least squares) to spectroscopic data acquired by monitoring chemical reactions and other processes. The goals of the resolution methods and the principles for understanding their applications are described. Some of the problems arising from these evolving systems and the limitations of the multivariate curve resolution methods are also discussed. This article reviews most of the applications of multivariate curve resolution applied to reacting systems published between January 2000 and June 2007. Some basic papers dated before 2000 have also been included.

Kinetic analysis of reactions of Si-based epoxy resins by near-infrared spectroscopy, 13C NMR and soft–hard modelling

Soft- and hard-modelling strategy was applied to near-infrared spectroscopy data obtained from monitoring the reaction between glycidyloxydimethylphenyl silane, a silicon-based epoxy monomer, and aniline. On the basis of the pure soft-modelling approach and previous chemical knowledge, a kinetic model for the reaction was proposed. Then, multivariate curve resolution-alternating least squares optimization was carried out under a hard constraint, that compels the concentration profiles to fulfil the proposed kinetic model at each iteration of the optimization process. In this way, the concentration profiles of each species and the corresponding kinetic rate constants of the reaction, unpublished until now, were obtained. The results obtained were contrasted with 13C NMR. The joint interval test of slope and intercept for detecting bias was not significant (alpha=5%).

Simultaneous determination of cortisol and prednisolone in body fluids by using HPLC–DAD coupled with second-order calibration based on alternating trilinear decomposition

A novel method for simultaneous determination of cortisol and prednisolone in body fluids has been developed in this paper. Three-way data recorded by high-performance liquid chromatography with a diode array detector (HPLC-DAD) have been analyzed by second-order calibration based on the alternating trilinear decomposition (ATLD) algorithm. The chemometric methodology selected exploits the second-order advantage of the three-way data arrays, which allows one to obtain concentrations of individual calibrated analytes even in the presence of interferences not present in the calibration samples (e.g. background in urine or plasma). It was applied to simultaneous determination of cortisol and prednisolone in both plasma and urine samples. Though the chromatographic and spectral peaks of the analytes were heavily overlapped and interferents coeluted with the compounds studied, good recoveries of the analytes could be obtained with HPLC-DAD coupled with second-order calibration based on ATLD. Sample preparation was based on solvent extraction (SE), and quantification can be carried out with simple mobile phase. The time required for the quantification process is shorter than other methods.

Multivariate curve resolution-alternating least squares and kinetic modeling applied to near-infrared data from curing reactions of epoxy resins: mechanistic approach and estimation of kinetic rate constants

This study describes the combination of multivariate curve resolution-alternating least squares with a kinetic modeling strategy for obtaining the kinetic rate constants of a curing reaction of epoxy resins. The reaction between phenyl glycidyl ether and aniline is monitored by near-infrared spectroscopy under isothermal conditions for several initial molar ratios of the reagents. The data for all experiments, arranged in a column-wise augmented data matrix, are analyzed using multivariate curve resolution-alternating least squares. The concentration profiles recovered are fitted to a chemical model proposed for the reaction. The selection of the kinetic model is assisted by the information contained in the recovered concentration profiles. The nonlinear fitting provides the kinetic rate constants. The optimized rate constants are in agreement with values reported in the literature.

Application of multivariate resolution methods to the study of biochemical and biophysical processes

Multivariate resolution methods make up a set of mathematical tools that may be applied to the analysis and interpretation of spectroscopic data recorded when monitoring a physical or chemical process with multichannel detectors. The goal of resolution methods is the recovery of chemical and/or physical information from the experimental data. Such data include, for example, the number of intermediates present in a reaction, the rate or equilibrium constants, and the spectra for each one of those intermediates. Multivariate resolution methods have been shown to be useful for the study of biophysical and biochemical processes such as folding/unfolding of proteins or nucleic acids. The present article reviews the most frequently used resolution methods, the limitations on their use, and their latest applications in protein and nucleic acid research.

Multivariate curve resolution methods in imaging spectroscopy: influence of extraction methods and instrumental perturbations

Imaging spectroscopy is becoming a key field of analytical chemistry. In the face of more and more complex samples, we actually need accurate microscopic insight. Nowadays, the methods used to produce concentration maps of the pure compounds from spectral data sets are based on the classical univariate approach although multivariate approaches are sometimes investigated. But in any case, the analytical quality of the chemical images thus provided cannot be discussed since no reference methods are at our disposal. Thus the proposed research focuses on the application of multivariate methods such as Orthogonal Projection Approach (OPA), SIMPLE-to-use Self-modeling Mixture Analysis (SIMPLISMA), Multivariate Curve Resolution - Alterning Least Squares (MCR-ALS), and Positive Matrix Factorization (PMF) for imaging spectroscopy. A systematic and quantitative characterization of the accuracy of spectra and images extraction is investigated on mid-infrared spectral data sets. Of special interest is the influence of instrumental perturbations such as noise and spectral shift on the extraction ability to access the algorithm's robustness.

Studies of liquid-phase complexes in acetonitrile/water solutions by attenuated total reflection infrared spectroscopy with acetaldehyde as a model solute

Chemometric methods combined with infrared (IR) spectroscopy, using attenuated total reflectance (ATR) sampling, are employed here to characterize the stoichiometry of complexes of solvent molecules in the liquid phase. The spectral information provides insight into the liquid microstructure present in liquid chromatographic mobile phases. This information should make it easier to understand and predict the effects of changes in mobile phase composition on the results of chromatographic separations. In this paper, mobile phases consisting of 0 mol % to 100 mol % acetonitrile in water were studied, with the addition of acetaldehyde as a model solute at concentrations ranging from 3 to 8 mol %. Using three-way multivariate curve resolution by the alternating least squares method (MCR-ALS) it was possible to resolve eight unique spectra: four mobile phase components, and four unique spectra of acetaldehyde solvated in different environments. The directions of the shifts of the important acetaldehyde infrared bands show good correlation with those predicted by gas-phase ab initio calculations of small solvated clusters.