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Edmund R. Malinowski is well known for his factor analysis‐based work; he is clearly less well known for his chemical model‐based analyses of chemical data. In this contribution, we discuss the, at the time innovative, idea of subjecting the primary model‐free analysis results to a secondary quantitative model‐based evaluation. Two examples from his research serve as illustrations: the complexation of Cu2+ with cyclodextrin and the dimerisation and trimerisation of methylene blue. In both examples, the spectrophotometric titration data are first analysed by window factor analysis, WFA, resulting in the concentration profiles. These primary, model‐free results allowed Malinowski to gain qualitative insight in the chemical processes. Additional quantitative analyses of the concentration profiles, based on the previously obtained reaction model, resulted in the numerical values of the underlying equilibrium constants. This contribution relates and compares this methodology with alternative ways of analysing the same data. Model‐free analyses result in the shapes of concentration profiles and spectra. Secondary analysis of the concentration profiles is possible and can result in quantitative information, for example, rate or equilibrium constants. It is advantageous to avoid this 2‐step process by executing a straight model‐based analysis. [ABSTRACT FROM AUTHOR]

Paul Gemperline has been active in many areas of data analysis in chemistry. In this contribution, we build on his developments in advanced process analysis. The most prominent aspect is data fusion, the combined analysis of measurements taken on different instruments, using different measurement techniques, and different chemical conditions. We have successfully applied these principles to the investigation of the chemistry of CO2 in aqueous amine solutions. There are two fields of chemometrics analyses, those which attempt at analysing the data without prior knowledge and those which based the analysis on as much chemical knowledge as possible. Paul Gemperline has been active in both. In this contribution, we concentrate on the chemistry‐based analysis ideas that have been developed by Paul: hard modelling and the globalisation of the analysis of several data sets. This contribution applies both above ideas for the investigation of the aqueous chemistry of CO2. [ABSTRACT FROM AUTHOR]

Evolving factor analysis (EFA) investigates the evolution of the singular values of matrices formed by a series of measured spectra, typically, resulting from the spectral observation of an ongoing chemical process. In the original EFA, the logarithms of the singular values are plotted for submatrices that include an increasing number of spectra. A typical observation in these plots is that pairs of trajectories of the singular values are on a collision course, but finally, the curves seem to repel each other and then run in different directions. For parameter‐dependent square matrices, such a behaviour is known for the eigenvalues under the keyword of an avoidance of crossing. Here, we adjust the explanation of this avoidance of crossing to the curves of singular values of EFA. Further, a condition is studied that breaks this avoidance of crossing. We demonstrate that the understanding of this noncrossing allows us to design model data sets with a predictable crossing behaviour. The curves of singular values as considered in the evolving factor analysis are well known to show an avoidance of crossing behavior. This phenomenon is analyzed and a mathematical explanation is given. [ABSTRACT FROM AUTHOR]

Abstract: The modelling of chemical processes entails the computation of the concentration profiles of all reaction species as a function of the reaction time. The basis for the calculations is the system of differential equations (ODE''s) that is defined by the reaction mechanism. Most textbooks on chemical kinetics concentrate on those few reaction mechanisms that lead to ODE''s with explicit solutions. In this tutorial, we demonstrate that numerical integration is a viable alternative, that it can be applied to any mechanism, and that it is easy to do so. Matlab example programs illustrate the concepts and they allow the reader to explore the effects of changing conditions such as initial concentrations or rate constants etc. Example reaction mechanisms include a zero-th order enzymatic reaction and reactions at non-constant temperature and pH. [Copyright &y& Elsevier]

Abstract: Data fitting is an important tool for the analysis of chemical processes. Limitations in the traditional fitting programs require strict control of external parameters such as temperature, pH, etc. Recent developments in fitting programs combine the analysis of non-ideal data with the global analysis of several different measurements. There are several advantages, the methods circumvent the necessity of external control of these parameters (thermostatting, buffering), they simplify experimental design, and they deliver additional information such as activation parameters and reaction enthalpies. Several practical examples and applications are given. [Copyright &y& Elsevier]

Review: 12 refs. [ABSTRACT FROM AUTHOR]

Abstract: With modern online instrumentation it is possible to follow the course of a chemical reaction using a variety of techniques simultaneously (e.g. calorimetry, spectroscopy and gas consumption/production). The data from different instrumentation have different error structures, different information content and often observe different physical phenomenon. Current techniques for the fitting of chemical models to measured kinetic data struggle when faced with data from different types of instrumentation. Rather then combining the data into a single objective function, the ranking of solutions within the genetic algorithm is done using the multi-objective Pareto optimal set. The final output of the algorithm is an optimised Pareto optimal set showing the trade off between the different objective functions for a range of parameter values. The approach has been demonstrated using simultaneously measured calorimetric and mid-IR spectroscopic data of the two-step epoxidation of 2,5-di-tert-butyl-1,4-benzoquinone. The measurements have been evaluated against three different reaction mechanisms in three case studies. The results show that the multi-objective genetic algorithm is a powerful method for the evaluation of a chemical model against data from different sources and for the determination of the appropriateness of a chemical model. [Copyright &y& Elsevier]