Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity

The calibration performance of partial least squares for one response variable (PLS1) can be improved by elimination of uninformative variables. Many methods are based on so-called predictive variable properties, which are functions of various PLS-model parameters, and which may change during the variable reduction process. In these methods variable reduction is made on the variables ranked in descending order for a given variable property. The methods start with full spectrum modelling. Iteratively, until a specified number of remaining variables is reached, the variable with the smallest property value is eliminated; a new PLS model is calculated, followed by a renewed ranking of the variables. The Stepwise Variable Reduction methods using Predictive-Property-Ranked Variables are denoted as SVR-PPRV. In the existing SVR-PPRV methods the PLS model complexity is kept constant during the variable reduction process. In this study, three new SVR-PPRV methods are proposed, in which a possibility for decreasing the PLS model complexity during the variable reduction process is build in. Therefore we denote our methods as PPRVR-CAM methods (Predictive-Property-Ranked Variable Reduction with Complexity Adapted Models). The selective and predictive abilities of the new methods are investigated and tested, using the absolute PLS regression coefficients as predictive property. They were compared with two modifications of existing SVR-PPRV methods (with constant PLS model complexity) and with two reference methods: uninformative variable elimination followed by either a genetic algorithm for PLS (UVE-GA-PLS) or an interval PLS (UVE-iPLS). The performance of the methods is investigated in conjunction with two data sets from near-infrared sources (NIR) and one simulated set. The selective and predictive performances of the variable reduction methods are compared statistically using the Wilcoxon signed rank test. The three newly developed PPRVR-CAM methods were able to retain significantly smaller numbers of informative variables than the existing SVR-PPRV, UVE-GA-PLS and UVE-iPLS methods without loss of prediction ability. Contrary to UVE-GA-PLS and UVE-iPLS, there is no variability in the number of retained variables in each PPRV(R) method. Renewed variable ranking, after deletion of a variable, followed by remodelling, combined with the possibility to decrease the PLS model complexity, is beneficial. A preferred PPRVR-CAM method is proposed.     

NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).     

Untersuchung von Trinkwasser, Kesselspeisewasser, Abwasser

Ohne Zusammenfassung     

Expert systems for chemical analysis (ESPRIT Project 1570)

Summary This paper gives an overview of the structure and processes going on in the project Expert Systems for Chemical Analysis (ESCA), an international project within the ESPRIT research programme of the European Community. The application area, HPLC method development, is explained and broken down into four domains. The methods used for representing knowledge in the project are discussed.     

The potential of field spectroscopy for the assessment of sediment properties in river floodplains

Investigations have shown that visible-near-infrared (VNIR) spectroscopy can accurately determine soil properties under laboratory conditions. In situ assessment of soil properties is of great benefit for several applications, as spectra can be acquired fast and almost continuously. The present study used partial least squares (PLS) regression to establish a relationship between soil reflectance spectra measured under field conditions and the organic matter and clay content of the soil. Spectra were acquired with a fieldspectrometer in a recently reconstructed floodplain along the river Rhine in The Netherlands. Several spectral pre-processing methods were employed to improve the performance and robustness of the models. Results indicate that, under varying surface conditions, field spectroscopy in combination with multivariate calibration does result in a qualitative relation for organic matter (R²=0.45) and clay content (R²=0.43) while under laboratory conditions more accurate results are obtained (R²=0.69 and 0.92, respectively). Soil moisture and vegetation cover had a negative influence on the prediction capabilities for both soil properties. Although the performance of the spectra measured in situ is not as accurate as physical analysis, the accuracy obtained is useful for rapid soil characterisation and remote sensing applications.     

Relationship between gas chromatographic behaviour and topological, physicochemical, and quantum chemically calculated charge parameters for neuroleptica

Gas chromatographic retention indices for a number of neuroleptic drugs on an apolar phase, OV-101, and a polar phase, OV-17, are correlated with parameters describing various properties of the separated molecules. The gas chromatographic behaviour is related to the same parameters as those used in quantitative structure activity relationships. The molecular connectivity indices and log k' in a reversed-phase HPLC system were chosen as parameters describing the apolar interactions of the molecules with the stationary phase. As properties involved in more specific interactions, and related to the presence of overall or local polarity, the molecular dipole moment and the charge on the N-atom were selected and quantum chemically calculated. It is found that the retention indices on the apolar phase, OV-101, can be successfully correlated with molecular connectivity indices, which are also used in QSAR studies. The retention indices on OV-17 show a high correlation with the charge on the N-atom. Evidence of the importance of this N-atom in pharmacological activity is known.     

Assessment of techniques for DOSY NMR data processing

Diffusion-ordered spectroscopy (DOSY) NMR is based on a pulse-field gradient spin-echo NMR experiment, in which components experience diffusion. Consequently, the signal of each component decays with different diffusion rates as the gradient strength increases, constructing a bilinear NMR data set of a mixture. By calculating the diffusion coefficient for each component, it is possible to obtain a two-dimensional NMR spectrum: one dimension is for the conventional chemical shift and the other for the diffusion coefficient. The most interesting point is that this two-dimensional NMR allows non-invasive “chromatography” to obtain the pure spectrum for each component, providing a possible alternative for LC-NMR that is more expensive and time-consuming. Potential applications of DOSY NMR include identification of the components and impurities in complex mixtures, such as body fluids, or reaction mixtures, and technical or commercial products, e.g. comprising polymers or surfactants.

Data processing is the most important step to interpret DOSY NMR. Single channel methods and multivariate methods have been proposed for the data processing but all of them have difficulties when applied to real-world cases. The big challenge appears when dealing with more complex samples, e.g. components with small differences in diffusion coefficients, or severely overlapping in the chemical shift dimension. Two single channel methods, including SPLMOD and continuous diffusion coefficient (CONTIN), and two multivariate methods, called direct exponential curve resolution algorithm (DECRA) and multivariate curve resolution (MCR), are critically evaluated by simulated and real DOSY data sets. The assessments in this paper indicate the possible improvement of the DOSY data processing by applying iterative principal component analysis (IPCA) followed by MCR-alternating least square (MCR-ALS).     

Gas chromatographic behaviour and pharmacological activity of neuroleptica

Biological activity in vitro, quantified as equilibrium inhibition constants to the dopamine receptor, of a series of neuroleptica are correlated with parameters describing polar and apolar interactions of these molecules with the receptor. Gas-chromatographic retention indices on stationary phases of different polarity are compared with parameters that are classically used in such quantitative structure/activity studies. To describe a polar interactions, a series of classical parameters such as several valence molecular indices and log k′ in a reversed-phase h.p.l.c. system are included; retention indices on the apolar stationary phase, OV101, are used as the gas-chromatographic (g.c.) parameter. To take the more specific polar interactions into account, quantum-chemical charge parameters such as the dipole moment and the charge on atoms directly involved in the interaction were calculated. Retention indices on the more polar phase OV17 are taken as the g.c. parameter for polar interactions. It is shown that the retention indices on OV101 can replace classical parameters describing aspecific or apolar interactions. The retention indices of OV17 do not correlate with biological activity and are worse than the charge parameters.