Enhanced data point importance: Layered significance of variables in multivariate calibration.

Background: The Enhanced Data Point Importance (EDPI) method, a systematic approach for evaluating the importance of data points in multivariate calibration, is introduced. Factor decomposition methods allow for the evaluation of the impact of variables on maintaining the structural pattern of data in the abstract space. Essential data points play a key role in these patterns and the method of Data Point Importance (DPI) aims to evaluate the essential data points in terms of their importance.

A New Type of Valence Tautomerism in Cobalt Dioxolene Complexes - Temperature-Induced Transition from a Cobalt(III) Catecholate to a Low-Spin Cobalt(II) Semiquinonate State.

The synthesis and characterization of the monocationic cobalt(III) catecholate complex [Co(L-N Bu )(Cl cat)] (L-N Bu =N,N'-Di-tert.-butyl-2,11-diaza[3.3](2,6)pyridinophane, Cl cat =4,5-dichlorocatecholate) are presented.

An active constraint approach to identify essential spectral information in noisy data.

Multivariate curve resolution (MCR) methods aim at extracting pure component profiles from mixed spectral data and can be applied to high-dimensional data, e.g., from process spectroscopy or hyperspectral imaging techniques.

Model-based signal tracking in the quantitative analysis of time series of NMR spectra.

Hard modeling of NMR spectra by Gauss-Lorentz peak models is an effective way for dimensionality reduction. In this manner high-dimensional measured data are reduced to low-dimensional information as peak centers, amplitudes or peak widths. For time series of spectra these parameters can be assumed to be smooth functions in time.

A multi-method chemometric analysis in spectroelectrochemistry: Case study on molybdenum mono-dithiolene complexes.

Spectroelectrochemical (SEC) analyses combine spectroscopic measurements with electrochemical techniques and can provide deep insight into complex multi-component chemical reaction systems. SEC experiments typically produce large amounts of spectroscopic data. Chemometric techniques are required for the data analysis and aim at extracting the underlying pure component information.

Reaction rate ambiguities for perturbed spectroscopic data: Theory and implementation.

The analysis of reaction systems and their kinetic modeling is important for both exploratory research and process design. Multivariate curve resolution (MCR) methods are state-of-the-art tools for the analysis of spectral series, but are also affected by an unavoidable solution ambiguity that impacts the obtained concentration profiles, spectra and model parameters. These uncertainties depend on the underlying model and the magnitude of the measurement perturbations.

On the analysis of chromatographic biopharmaceutical data by curve resolution techniques in the framework of the area of feasible solutions.

Monitoring preparative protein chromatographic steps by in-line spectroscopic tools or fraction analytics results in medium or large sized data matrices. Multivariate Curve Resolution (MCR) serve to compute or to estimate the concentration values of the pure components only from these data matrices. However, MCR methods often suffer from an inherent solution ambiguity which underlies the factorization problem.

Unraveling VEALYL Amyloid Formation Using Advanced Vibrational Spectroscopy and Microscopy.

Intermediate species are hypothesized to play an important role in the toxicity of amyloid formation, a process associated with many diseases. This process can be monitored with conventional and two-dimensional infrared spectroscopy, vibrational circular dichroism, and optical and electron microscopy. Here, we present how combining these techniques provides insight into the aggregation of the hexapeptide VEALYL (Val-Glu-Ala-Leu-Tyr-Leu), the B-chain residue 12-17 segment of insulin that forms amyloid fibrils (intermolecularly hydrogen-bonded β-sheets) when the pH is lowered below 4.

Application of a new method for simultaneous phase and baseline correction of NMR signals (SINC).

Recently, we presented a new approach for simultaneous phase and baseline correction of nuclear magnetic resonance (NMR) signals (SINC) that is based on multiobjective optimization. The algorithm can automatically correct large sets of NMR spectra, which are commonly acquired when reactions and processes are monitored with NMR spectroscopy. The aim of the algorithm is to provide spectra that can be evaluated quantitatively, for example, to calculate the composition of a mixture or the extent of reaction.

A chemometric study in the area of feasible solution of an acid-base titration of -methyl-6-oxyquinolone.

Multivariate curve resolution methods aim at recovering the underlying chemical components from spectroscopic data on chemical reaction systems. In most cases the spectra and concentration profiles of the pure components cannot be uniquely determined from the given spectral data. Instead continua of possible factors exist.

Multi-objective optimization for an automated and simultaneous phase and baseline correction of NMR spectral data.

Spectral data preprocessing is an integral and sometimes inevitable part of chemometric analyses. For Nuclear Magnetic Resonance (NMR) spectra a possible first preprocessing step is a phase correction which is applied to the Fourier transformed free induction decay (FID) signal. This preprocessing step can be followed by a separate baseline correction step.

A ray casting method for the computation of the area of feasible solutions for multicomponent systems: Theory, applications and FACPACK-implementation.

Multivariate curve resolution methods suffer from the non-uniqueness of the solutions. The set of possible nonnegative solutions can be represented by the so-called Area of Feasible Solutions (AFS). The AFS for an s-component system is a bounded (s-1)-dimensional set.

On the ambiguity of the reaction rate constants in multivariate curve resolution for reversible first-order reaction systems.

If for a chemical reaction with a known reaction mechanism the concentration profiles are accessible only for certain species, e.g. only for the main product, then often the reaction rate constants cannot uniquely be determined from the concentration data.

A review of recent methods for the determination of ranges of feasible solutions resulting from soft modelling analyses of multivariate data.

Soft modelling or multivariate curve resolution (MCR) are well-known methodologies for the analysis of multivariate data in many different application fields. Results obtained by soft modelling methods are very likely impaired by rotational and scaling ambiguities, i.e.

A multiresolution approach for the convergence acceleration of multivariate curve resolution methods.

Modern computerized spectroscopic instrumentation can result in high volumes of spectroscopic data. Such accurate measurements rise special computational challenges for multivariate curve resolution techniques since pure component factorizations are often solved via constrained minimization problems. The computational costs for these calculations rapidly grow with an increased time or frequency resolution of the spectral measurements.

An operando FTIR spectroscopic and kinetic study of carbon monoxide pressure influence on rhodium-catalyzed olefin hydroformylation.

The influence of carbon monoxide concentration on the kinetics of the hydroformylation of 3,3-dimethyl-1-butene with a phosphite-modified rhodium catalyst has been studied for the pressure range p(CO)=0.20-3.83 MPa.

On the area of feasible solutions and its reduction by the complementarity theorem.

Multivariate curve resolution techniques in chemometrics allow to uncover the pure component information of mixed spectroscopic data. However, the so-called rotational ambiguity is a difficult hurdle in solving this factorization problem. The aim of this paper is to combine two powerful methodological approaches in order to solve the factorization problem successfully.

Exploring between the extremes: conversion-dependent kinetics of phosphite-modified hydroformylation catalysis.

The kinetics of the hydroformylation of 3,3-dimethyl-1-butene with a rhodium monophosphite catalyst has been studied in detail. Time-dependent concentration profiles covering the entire olefin conversion range were derived from in situ high-pressure FTIR spectroscopic data for both, pure organic components and catalytic intermediates. These profiles fit to Michaelis-Menten-type kinetics with competitive and uncompetitive side reactions involved.