Romà Tauler

Multivariate curve resolution applied to second order data

Abstract Application of multivariate curve resolution to second order data from hyphenated liquid chromatography with spectrometric diode array detection is shown. Chromatographic analysis of samples giving unresolved mixtures produces different data structures depending on the reproducibility of the elution process: (a) second order data where elution peaks of the same component in the different chromatographic runs have the same shape and appear at exactly the same elution times (synchronized); (b) second order data where elution peaks of the same component in the different chromatographic runs appear at different elution times (non-synchronized) although they are still of the same shape; and (c) second order data where elution peaks of the same component in the different chromatographic runs have different shapes and appear at different elution times. Multivariate curve resolution is easily adapted to analyze all these situations taking advantage in every case of the particular data structure. Multivariate curve resolution is also easily adapted to those situations where second order data has not a complete trilinear structure.

Multivariate Curve Resolution (MCR) from 2000: Progress in Concepts and Applications

This work is mainly oriented to give an overview of the progress of multivariate curve resolution methods in the last 5 years. Conceived as a review that combines theory and practice, it will present the basics needed to understand what is the use, prospects and limitations of this family of chemometric methods with the latest trends in theoretical contributions and in the field of analytical applications.

Chemometrics applied to unravel multicomponent processes and mixtures: Revisiting latest trends in multivariate resolution

Abstract Progress in the analysis of multicomponent processes and mixtures relies on the combination of sophisticated instrumental techniques and suitable data analysis tools focused on the interpretation of the multivariate responses obtained. Despite the differences in compositional variation, complexity and origin, the raw measurements recorded in a multicomponent chemical system can be very often described with a simple model consisting of the composition-weighted sum of the signals of their pure compounds. Multivariate resolution methods have been the tools designed to unravel this pure compound information from the non-selective mixed original experimental output. The evolution of these chemometric approaches through the improvement of exploratory tools, the adaptation to work with complex data structures, the ability to introduce chemical and mathematical information in the algorithms and the better quality assessment of the results obtained is revisited. The active research on these chemometric area has allowed the successful application of these methodologies to chemical problems as complex and diverse as the interpretation of protein folding processes or the resolution of spectroscopic images.

Combining hard- and soft-modelling to solve kinetic problems

Abstract A novel approach mixing the qualities of hard-modelling and soft-modelling methods is proposed to analyse kinetic data monitored spectrometrically. Taking as a basis the Multivariate Curve Resolution–Alternating Least Squares method (MCR–ALS), which obtains the pure concentration profiles and spectra of all absorbing species present in the raw measurements by using typical soft-modelling constraints, a new hard constraint is introduced to force some or all the concentration profiles to fulfill a kinetic model, which is refined at each iterative cycle of the optimisation process. This modification of MCR–ALS drastically decreases the rotational ambiguity associated with the kinetic profiles obtained using exclusively soft-modelling constraints. The optional inclusion of some or all the absorbing species into the kinetic model allows the successful treatment of data matrices whose instrumental response is not exclusively due to the chemical components involved in the kinetic process, an impossible scenario for classical hard-modelling approaches. Moreover, the possible distinct constraint of each of the matrices in a three-way data set allows for the simultaneous analysis of kinetic runs with diverse kinetic models and rate constants. Thus, the introduction of model-based and model-free features in the treatment of kinetic data sets yields more satisfactory results than the application of pure hard- or pure soft-modelling approaches. Simulated and real examples are used to confirm this statement.

Impact of pesticides used in agriculture and vineyards to surface and groundwater quality (North Spain).

An environmental monitoring program was carried out to determine the impact of eight pesticides on the surface and groundwater quality of agricultural areas within the Ebro, Duero and Miño river basins. Three triazines and their desethyl degradation products, metolachlor and metalaxyl, were monitored during 18 months in 63 sites. Solid-phase extraction (SPE) using OASIS HLB 60 mg cartridges and gas chromatography-mass spectrometry (GC-EI-MS) provided good analytical quality parameters and limits of quantification of 0.01 microg/L. Environmental data were assessed using descriptive statistical analysis and multivariate data analysis with principal component analysis (PCA) to elucidate the relevant contamination patterns and provide a description of their seasonal trends, according to the pesticide application timing. Duero was the site with the highest frequency of detection and highest concentration levels, followed by the Ebro and Miño basins. The frequency of detection of the studied compounds, considering all surface and groundwater samples, was atrazine>desethylatrazine>simazine>desethylsimazine>metolachlor>desethylterbuthylazine>terbuthylazine>metalaxyl. Over all results, and taking into consideration the European Union (EU) maximum residual limit of pesticides in groundwater, only 12% of the results exceeded the 0.1 microg/L limit. However, sporadic high levels up to 2.46 mug/L in groundwater and 0.63 microg/L in surface water were detected. PCA permitted to state that Duero and Ebro river basins were especially affected by a contamination pattern dominated by atrazine, the Ebro river basin being occasionally affected by a contamination pattern dominated by simazine. Only trace levels were rarely detected in the Miño river basin. Groundwater levels were higher than surface water levels for the studied pesticides.

Multivariate curve resolution applied to liquid chromatography—diode array detection

Abstract Multivariate curve resolution methods can be used for the quantitation of the overlapping components in a chromatographic peak. Initial qualitative solutions obtained by self-modelling curve resolution methods, such as evolving factor analysis, can be further optimized by simultaneous analysis of multiple chromatographic runs with alternating least-squares regression. Quantitation is achieved by constraining the pure unit spectra and elution profiles of the common analytes in the different chromatographic runs to be equal. The relationship between the proposed method and other higher-order calibration and resolution methods is examined.

Assessment of new constraints applied to the alternating least squares method

Abstract The introduction of constraints in multivariate curve resolution methods, such as the Alternating Least Squares (ALS), is commonly used to limit the span of possible solutions, guiding the iterative process to a final result as close as possible to the true situation. In the present work, two modifications of the unimodality constraint and a new constraint for chromatographic concentration profiles related to the prevention of fronting have been checked. Simulated data sets as well as real data have been used to evaluate the effect of these new constraints in the resolution results. The parameters measured to assess the goodness of the constraints are related to the recovery of the concentration profiles and the quality of the data fit.

Simultaneous analysis of several spectroscopic titrations with self-modelling curve resolution

Abstract Self-modelling curve resolution of individual spectroscopic titrations of a multiequilibria system based on evolving factor analysis does not always provide the right qualitative and quantitative solution, even when the constraints of non-negativity, unimodality and closure are applied. The ambiguities still present in that treatment can be removed if several spectroscopic titrations at different starting conditions (different initial concentrations of the constituents) are analyzed simultaneously and if the spectra of the common species in these different spectroscopic titrations are equal.

Spectroscopic imaging and chemometrics: a powerful combination for global and local sample analysis

Merging spectroscopic imaging and chemometrics enhances the outcomes of instrumental technology and data analysis. Multivariate exploratory and resolution methods can be adapted to image analysis and provide global and local information about pure compounds in an imaged sample. Knowing in detail how the chemical compounds are distributed over the scanned surface gives valuable information about essential issues in the manufacture and the characterization of products, such as evenness of composition and, therefore, homogeneity of the sample. The power to detect and to locate impurities is also greatly enhanced because these unwanted compounds could show locally large concentrations (and signals), even though their abundance on the surface is very low. The capabilities of this combination are shown in an example of pharmaceutical product control, where analysis of the end product requires chemical characterization and quantitative information at global and local levels. The approach used and the kind of information obtained is general and can be applied to the analysis of images in other fields.

Application of multivariate curve resolution alternating least squares (MCR-ALS) to the quantitative analysis of pharmaceutical and agricultural samples

Application of multivariate curve resolution alternating least squares (MCR-ALS), for the resolution and quantification of different analytes in different type of pharmaceutical and agricultural samples is shown. In particular, MCR-ALS is applied first to the UV spectrophotometric quantitative analysis of mixtures of commercial steroid drugs, and second to the near-infrared (NIR) spectrophotometric quantitative analysis of humidity and protein contents in forage cereal samples. Quantitative results obtained by MCR-ALS are compared to those obtained using the well established partial least squares regression (PLSR) multivariate calibration method.