INVESTIGATION OF THE ALLOMERIZATION REACTION OF CHLOROPHYLL a: USE OF DIODE ARRAY HPLC, MASS SPECTROMETRY AND CHEMOMETRIC FACTOR ANALYSIS FOR THE DETECTION OF EARLY PRODUCTS

Abstract –The products of chlorophyll allomerization in methanol were isolated and analyzed by open column sucrose chromatography, liquid chromatography mass spectrometry (LCMS) and DAD-HPLC (diode-array high-performance liquid chromatography). Four main bands were found with molecular ions of (a) 908, (b) 938, (c) 938 and (d) 938, consistent with the structures (a) 13²-hydroxy-chlorophyll a (II), (b) and (c) Mg(II)-3¹,3²-didehydro-15¹-hydroxy-15¹-methoxy-rhodochlorin-15 acetic acid δ-lactone 15²-methyl 17³-phytyl ester and its epimer (III) and (d) Mg(II)-3¹,3²-didehydro-rhodochlorin-15-glyoxylic acid 13¹,15²-dimethyl 17³-phytyl ester (IV), evidence enhanced by UV/visible spectroscopy, chromatographic coelutions and chemometrics. Chlorophyll a was degraded both in the dark and light, under O₂ and N². DAD-HPLC of the resultant degradation mixtures were analyzed using the chemometric heuristic-evolving latent projection method for resolution. Ultraviolet/visible spectra of II and III are reproducibly extracted from the mixtures after a short degradation time, whereas III and IV are the dominant compounds after longer degradation times. Changes in relative elution order of IV using open column chromatography and reverse-phase HPLC are established. A possible allomerization pathway is proposed.     

Model building in chemistry,a unified approach

The generation, confirmation and reduction of models is discussed. The latent-variable approach is extended so as to encompass the possibility of interaction (nonadditivity) between variables. Various congruence coefficients and association measures are used in the model building process: (i) to examine models for general factors; (ii) to reveal marker variables, i.e., variables that are insensitive to all but one chemical process or phenomenon; and (iii) to examine latent-variable models for nonadditivity. Marker variables, carrying the same information as the latent variables in a model, represent the link between the latent-variable and multiple regression approaches to model building. Thus, in principle, any multiple linear regression model is obtainable from corresponding higher-order latent-variable models. By combining the marker approach as used for model reduction with various simple techniques for data transformation, several classes of chemical and physical models are shown to correspond to subsets of the more general latent-variable model.     

Modeling based on subspace orthogonal projections for QSAR and QSPR research

A novel projection modeling method for quantitative structure activity relationship (QSAR) and quantitative structure property relationship (QSPR) is developed in this paper. Orthogonalization of block variables is introduced to deal with the problem of variable selection. Projections based on least squares are used to construct the modeling space in order to search for the best regression directions for chemical modeling. A suitable prediction space for such a model is further defined to confine the usage range of the model. Three real data sets were analyzed to check the performance of the proposed modeling method. The results obtained from Monte‐Carlo cross‐validation (MCCV) showed that the proposed modeling method might provide better results for QSAR and QSPR modeling than PCR and PLS with respect to both fitting and prediction abilities. Copyright © 2007 John Wiley & Sons, Ltd.     

Visualizing information in multivariate data: Applications to petroleum geochemistry : Part 1. Projection methods

General procedures are developed for revealing and comparing the information carried by experimental data obtained for different petroleum fractions. Oblique rotation of principal components is used to obtain projections amenable to geochemical interpretation. A systematic method is given for selecting markers, i.e., compounds related to specific geochemical factors. By projecting samples onto axes defined by markers, their potential for describing a petroleum fraction can be evaluated. The similarity between samples or groups (classes) of samples is revealed by projecting samples onto “geochemicalrdquo; axes and by comparing their residual distances to class models. In class modelling, projections on markers are proposed as a method for eliminating differences induced by alteration processes not related directly to the source of the oils, e.g., maturity.     

High-throughput, low-volume, multianalyte quantification of plasma metabolites related to one-carbon metabolism using HPLC-MS/MS

Risk of chronic diseases, like cardiovascular disease and cancer, has been associated with biomarkers related to one-carbon metabolism, which comprises a metabolic network of cross-talking pathways. To address this complexity in epidemiological studies, we have established an isotope dilution HPLC-MS/MS method for quantification of 12 biomarkers and metabolites. All sample handling is performed by a robotic workstation. The assay uses 45 μL of plasma, and sample treatment consists of protein precipitation by trichloroacetic acid. The analytes were separated on a Fortis Phenyl column using an isocratic mobile phase that contained water, methanol and acetic acid. Methionine, methionine sulfoxide, choline, betaine, dimethylglycine, arginine, asymmetric dimethylarginine, symmetric dimethylarginine, homoarginine, creatinine, cystathionine and trimethyllysine all showed limits of detection well below the 5th percentile of plasma distributions in healthy humans, coefficients of variation were in the range 2.2–12.3 %, and recoveries were 80–131 %. Simple sample processing, low-volume consumption, multiplexing and high capacity/short run time of this method make it suitable for large-scale metabolic profiling of precious biobank samples.     

X‐tended target projection (XTP)—comparison with orthogonal partial least squares (OPLS) and PLS post‐processing by similarity transformation (PLS + ST)

Target projection (TP) also called target rotation (TR) was introduced to facilitate interpretation of latent‐variable regression models. Orthogonal partial least squares (OPLS) regression and PLS post‐processing by similarity transform (PLS + ST) represent two alternative algorithms for the same purpose. In addition, OPLS and PLS + ST provide components to explain systematic variation in X orthogonal to the response. We show, that for the same number of components, OPLS and PLS + ST provide score and loading vectors for the predictive latent variable that are the same as for TP except for a scaling factor. Furthermore, we show how the TP approach can be extended to become a hybrid of latent‐variable (LV) regression and exploratory LV analysis and thus embrace systematic variation in X unrelated to the response. Principal component analysis (PCA) of the residual variation after removal of the target component is here used to extract the orthogonal components, but X‐tended TP (XTP) permits other criteria for decomposition of the residual variation. If PCA is used for decomposing the orthogonal variation in XTP, the variance of the major orthogonal components obtained for OPLS and XTP is observed to be almost the same, showing the close relationship between the methods. The XTP approach is tested and compared with OPLS for a three‐component mixture analyzed by infrared spectroscopy and a multicomponent mixture measured by near infrared spectroscopy in a reactor. Copyright © 2008 John Wiley & Sons, Ltd.     

Detection and use of selective ions for providing initial estimates of the concentration vectors in iterative target transformation factor analysis

The characteristic of discreteness in mass spectral direction distinguishes GC/MS from other spectral techniques. Based on this feature, we propose a new method to construct the initial concentration vectors for iterative target transformation factor analysis (ITTFA). For each chemical component, a search for a selective ion with good signal-to-noise ratio is first conducted using evolving factor analysis (EFA) and information entropy. The corresponding chromatogram of the selective ion is subsequently applied to construct an initial concentration vector for ITTFA. Special strategies are developed to cope with chromatographic patterns with embedded peaks and complex multicomponent structure. Results from three simulated and one real mixture and comparison with results from heuristic evolving latent projections (HELP) and a previously published method for definition of the initial profiles for ITTFA, indicate that selective ion chromatogram (SIC) ITTFA represents a fast, automatic and accurate method for resolution of GC/MS data.     

Quantitative Determination of Asphaltenes and Resins in Solution by Means of Near-Infrared Spectroscopy. Correlations to Emulsion Stability

Near-infrared (NIR) spectroscopy in the range 1100-2250 nm together with a latent-variable regression technique is used to analyze the content of asphaltene and resins in solution. It is shown that this technique is capable of determining the amount of these components individually. w/o emulsions were prepared from the separated components of asphaltenes and resins from crude oils. The stability was directly determined with the critical voltage in a dielectric instrumentation. The emulsion stability decreased linearly with an increase in the resin/asphaltene ratio. A final linear model correlating the critical voltage and the analytical concentrations (from the NIR spectra) could be established for this model system. Copyright 2000 Academic Press.     

Resolution of two-way data: theoretical background and practical problem-solving. Part 1: theoretical background and methodology

This paper reviews recent progress in the resolution of two-way data obtained from hyphenated instruments. Special emphasis is placed on the solution of practical problems. Methods for estimating the number of chemical components both statistically and visually (the first step in solving the resolution problem) and methods for resolving the pure profiles (the second step in solving the resolution problem) are discussed in detail. To deal with real-world problems, pitfalls in the chemometric analysis of the two-way data from the instrumental measurements are also pointed out. Applications of methods for solving some difficult practical problems in environmental chemistry, pharmaceutical chemistry, and physical chemistry will be discussed in the second part of this paper.     

Routine analyses of crude oil fractions by principal component modelling of gas chromatographic profiles

The naphtha fraction of 13 crude North Sea oils was analyzed by high-resolution gas chromatography. Principal component modelling shows that prior normalization to percentages is unnecessary if logarithmic data are used. When the variation in absolute amounts is large (e.g., as a result of manual injection or complicated work-up of samples), the first principal component accounts for differences in chromatographic profiles. In this case, normalization does not change the relations between samples as is observed on the dominant principal components.