Application of multivariate self-modeling curve resolution to the quantitation of trace levels of organophosphorus pesticides in natural waters from interlaboratory studies

Multivariate self-modeling curve resolution is applied to the quantitation of coeluted organophosphorus pesticides: fenitrothion, azinphos-ethyl, diazinon, fenthion and parathion-ethyl. Analysis of these pesticides at levels of 0.1 to 1 μg/l in the presence of natural interferences is achieved using automated on-line liquid-solid extraction (Prospekt) coupled to liquid chromatography and diode array detection followed by a recently developed multivariate self-modeling curve resolution method. The proposed approach uses only 100 ml of natural water sample and has improved resolution of the coeluted organophosphorus insecticides and their quantitation at trace level. The results have been compared with those obtained by different laboratories participating in the Aquacheck interlaboratory exercise (WRC, Medmenham, UK) where more conventional analytical techniques are being used.     

Determination of phenolic acids in strawberry samples by means of fast liquid chromatography and multivariate curve resolution methods

Use of Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) is evaluated in the analysis of nine phenolic acids, both in standards mixture samples and in strawberry juice samples, by liquid chromatography with diode array detection (LC-DAD). Chromatographic coelution problems either because of unknown matrix interferences or because of the increase of organic modifier to reduce chromatographic analysis times are investigated. pH (4.25) and proportion of organic modifier in acetonitrile-water ratios (11:89, v/v) used as mobile phases have been optimized for separation of mixture of nine phenolic acids. Results obtained in the resolution and quantitation of phenolic acids in standards mixture samples and strawberry samples at two proportions of organic modifier (11:89 and 40:60 acetonitrile-water (v/v) ratios) show that the proposed MCR-ALS approach reduces analysis times and solvent expenses and improves determinations in case of strong coelution. Limits on the use of MCR-ALS are investigated in the analysis of phenolic acids in strawberry samples.     

Temporal characterisation of river waters in urban and semi-urban areas using physico-chemical parameters and chemometric methods

Three sampling campaigns were carried out in rivers located at two hydrographic basins affected by urban and semi-urban areas around the Metropolitan area of A Coruña (ca. 500,000 inhabitants, NW-Spain) to study local and temporal variations of 21 physicochemical parameters (pH, conductivity, Cl⁻, SO₄²⁻, SiO₂, Ca²⁺, Mg²⁺, Na⁺, K⁺, hardness, NO₃⁻, NO₂⁻, NH₄⁺, COD, PO₄³⁻, Zn²⁺, Cu²⁺, Mn²⁺, Pb²⁺, alkalinity and acidity) in 23 sampling points. The temporal evolution of the water quality was assessed by matrix augmentation principal components analysis (MA-PCA) and parallel factor analysis (PARAFAC). Moreover, classical principal components analysis (PCA) (one per sampling campaign) was applied with exploratory and comparison purposes. The first factor of the different studies comprised variables associated to the mineral content and it differentiated the samples according to their hydrographic basins. The second factor was mainly associated to organic matter, from domestic wastes and decomposition of natural debris. The temporal evolution of the water quality was mostly related to seasonal increments of the physicochemical parameters defining the decomposition of the organic matter.The three models applied (PCA, MA-PCA and PARAFAC) led to similar conclusions, nonetheless, MA-PCA excelled, since the refolding of scores provided more straightforward and convenient overview of sample time and geographical variations than individual PCA and it is more flexible and adaptable to environmental studies than PARAFAC.     

Spectroscopic study of the interaction of actinomycin D with oligonucleotides carrying the central base sequences -XGCY- and -XGGCCY- using multivariate methods

The interactions of actinomycin D (ACTD) with the oligonucleotides 5′-CAAAGCTTTG-3′, 5′-CATGGCCATG-3′ and 5′-TATGGCCATA-3′ were investigated by means of acid–base titrations and mole-ratio and melting experiments monitored by molecular absorption and circular dichroism (CD) spectroscopies. For each experiment, CD and molecular absorption spectra were recorded at each point in the experiment, and later analyzed via appropriate multivariate data analysis methods. The study of the interactions between these oligonucleotides and ACTD at 25 °C showed the formation of an interaction complex with a stoichiometry of 1:1 (ACTD:duplex) and values for the log(formation constant) of 5.1 ± 0.3, 6.4 ± 0.2, and 5.6 ± 0.2, respectively. An additional interaction complex at higher temperatures was also detected, which might be related to the single-stranded forms of the oligonucleotides. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

Extraction of climatic signals from fossil organic compounds in marine sediments up to 11.7 Ma old (IODP-U1318)

This study focuses on the extraction of climate signals and processes using a combined approach which includes the analysis of a high number of lipid molecules in marine sediments, and the chemometric analysis of the acquired data. Neutral and acidic fractions of marine sediments from site IODP-U1318 (south-west of the UK, Porcupine Seabight) were quantified by GC–MS. The alkenone unsaturation index, U^k′₃₇, was estimated from the composition of C₃₇ alkenones and it was then used for the estimation of sea surface temperatures (SST) for reference. Principal component analysis (PCA), explained 77.45% of the total data variance, and differentiated neutral fraction GC–MS total ion current (TIC) profiles according to SST values of the different sediment sections. GC–MS TIC chromatograms were correlated to sea surface temperatures (SST) by partial least squares regression (PLSR). The compounds more robustly in line with SST values at each sediment section explained 93% of the SST variance and they were identified using the variable importance in projection (VIP) scores method. The proposed approach enables an objective identification of organic compounds sensitive to SST variability throughout complete chromatographic profiles. As a result of this multivariate unbiased approach, lipid composition of sediments was differentiated between compounds of marine (long chain n-alkanes, long chain n-alkan-1-ols) and terrestrial (short chain n-alkan1-ols, alkenols, cholesterol, squalene) origin, whose concentrations were directly and inversely correlated to SST, respectively.     

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis—ASCA, regularized MANOVA–rMANOVA, and Group-wise ANOVA-simultaneous component analysis—GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method.     

Monitoring polymorphic transformations by using in situ Raman hyperspectral imaging and image multiset analysis

Polymorphism is often encountered in many crystalline compounds. To control the quality of the products, it is relevant knowing the potential presence of polymorph transformations induced by different agents, such as light exposure or temperature changes. Raman images offer a great potential to identify polymorphs involved in a process and to accurately describe this kind of solid-state transformation in the surface scanned.     

Analytical investigation of secondary metabolites extracted from Camellia sinensis L. leaves using a HPLC‐DAD‐ESI/MS data fusion strategy and chemometric methods

Considerable attention has been paid to the study of green tea leaves because of their high consume and beneficial effects on human health. In this work, an appropriate strategy is proposed to investigate and resolve the major metabolites extracted from Camellia sinensis tea leaves. Statistical design mixtures of ethanol, ethyl acetate, dichloromethane, and chloroform were used to study the effects of different solvents and their mixtures on the extraction of the secondary metabolites of C. sinensis tea leaves from two different harvest seasons. Extracted samples were analyzed by high performance liquid chromatography‐diode array detection‐electrospray ionization mass spectrometer allowing the resolution of a large amount of tea metabolites with high relative abundances, especially when their extraction was performed in pure ethanol and with solvent mixtures with ethanol. Resolution of the more relevant metabolites was achieved by the simultaneous analysis of the fused diode array detection and mass spectrometer detectors data from the same samples using the multivariate curve resolution‐alternating least squares chemometric method. Peak areas finally resolved were further analyzed by orthogonal signal correction and partial least squares‐discrimination analysis to discriminate among C. sinensis tea samples. Using the Variable Importance in Projection variable selection method, epigallocatechin and caffeine were finally selected as the two more important chemical constituents of tea leaves that were discriminating more between the tea samples from two different harvest seasons. Copyright © 2016 John Wiley & Sons, Ltd.     

Multivariate curve resolution of incomplete fused multiset data from chromatographic and spectrophotometric analyses for drug photostability studies

An advanced and powerful chemometric approach is proposed for the analysis of incomplete multiset data obtained by fusion of hyphenated liquid chromatographic DAD/MS data with UV spectrophotometric data from acid–base titration and kinetic degradation experiments. Column- and row-wise augmented data blocks were combined and simultaneously processed by means of a new version of the multivariate curve resolution-alternating least squares (MCR-ALS) technique, including the simultaneous analysis of incomplete multiset data from different instrumental techniques. The proposed procedure was applied to the detailed study of the kinetic photodegradation process of the amiloride (AML) drug. All chemical species involved in the degradation and equilibrium reactions were resolved and the pH dependent kinetic pathway described.     

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.