A method for the determination of reaction mechanisms and rate constants from two-way spectroscopic data

Abstract We propose a method for resolving spectroscopic reaction monitoring data into concentration profiles and spectra for the pure chemical components. It can be used if a valid mechanistic model for the reaction can be postulated. In the analysis, the parameters of the model, i.e. the rate constants, are also determined. If several mechanistic models are possible, these can be tested and the model best describing the data can be selected. The method is based on refinement of concentration profiles from the mechanistic model by target testing. Subsequent rotation of the loadings from principal component analysis (PCA) yields the pure component spectra. The only input used is the spectroscopic data, the mechanistic model(s) to be tested/fitted and possibly, but not necessarily, the initial concentrations of the species participating in the reaction. The performance of the method has been evaluated for synthetic and experimental data described by some common and simple reaction models. A comparison with techniques for non-linear least squares fitting and self-modelling curve resolution is included in the text.

[1]  M. Kubista,et al.  Quantitative spectral analysis of multicomponent equilibria , 1995 .

[2]  Marcel Maeder,et al.  Second-order globalisation for the determination of activation parameters in kinetics , 1994 .

[3]  B. Kowalski,et al.  Multivariate curve resolution applied to spectral data from multiple runs of an industrial process , 1993 .

[4]  R. Tauler,et al.  Influence of selectivity and polyelectrolyte effects on the performance of soft-modelling and hard-modelling approaches applied to the study of acid-base equilibria of polyelectrolytes by spectrometric titrations , 1996 .

[5]  W. Windig,et al.  Self-modeling mixture analysis of second-derivative near-infrared spectral data using the SIMPLISMA approach , 1992 .

[6]  Klaas Faber,et al.  Critical evaluation of two F-tests for selecting the number of factors in abstract factor analysis , 1997 .

[7]  J. K. Strasters,et al.  STRATEGY FOR PEAK TRACKING IN LIQUID CHROMATOGRAPHY ON THE BASIS OF A MULTIVARIATE ANALYSIS OF SPECTRAL DATA , 1990 .

[8]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[9]  D. J. Leggett,et al.  Numerical analysis of multicomponent spectra , 1977 .

[10]  Evaluation techniques for two-way data from in situ fourier transform mid-infrared reaction monitoring in aqueous solution. , 1998, Analytical chemistry.

[11]  Paul J. Gemperline,et al.  Target transformation factor analysis with linear inequality constraints applied to spectroscopic-chromatographic data , 1986 .

[12]  Romà Tauler,et al.  Simultaneous analysis of several spectroscopic titrations with self-modelling curve resolution , 1993 .

[13]  Romà Tauler,et al.  Application of a new multivariate curve resolution procedure to the simultaneous analysis of several spectroscopic titrations of the copper(II)—polyinosinic acid system , 1995 .

[14]  M. Ameloot,et al.  A systematic study of the global analysis of multiexponential fluorescence decay surfaces using reference convolution , 1990 .

[15]  Desire L. Massart,et al.  Resolution of multicomponent overlapped peaks by the orthogonal projection approach, evolving factor analysis and window factor analysis , 1997 .

[16]  W. Windig,et al.  Interactive self-modeling mixture analysis , 1991 .

[17]  D. Massart,et al.  Orthogonal projection approach applied to peak purity assessment. , 1996, Analytical chemistry.

[18]  Yongnian Ni,et al.  Simultaneous spectrophotometric determination of mixtures of food colorants , 1997 .

[19]  P. Stilbs,et al.  Global least‐squares analysis of large, correlated spectral data sets and application to chemical kinetics and time‐resolved fluorescence , 1996 .

[20]  Jay R. Knutson,et al.  Simultaneous analysis of multiple fluorescence decay curves: A global approach , 1983 .

[21]  H. R. Keller,et al.  Evolving factor analysis , 1991 .

[22]  S. Wold Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[23]  Edmund R. Malinowski,et al.  Factor Analysis in Chemistry , 1980 .

[24]  Willem Windig,et al.  The use of second-derivative spectra for pure-variable based self-modeling mixture analysis techniques , 1994 .

[25]  B. Kowalski,et al.  Selectivity, local rank, three‐way data analysis and ambiguity in multivariate curve resolution , 1995 .

[26]  M. Maeder Evolving factor analysis for the resolution of overlapping chromatographic peaks , 1987 .

[27]  Avraham Lorber,et al.  Validation of hypothesis on a data matrix by target factor analysis , 1984 .

[28]  M. Kubista,et al.  Determination of equilibrium constants by chemometric analysis of spectroscopic data , 1993 .

[29]  Joel M. Harris,et al.  Resolution of multicomponent fluorescence spectra by an emission wavelength-decay time data matrix , 1981 .

[30]  Bruce R. Kowalski,et al.  An extension of the multivariate component-resolution method to three components , 1985 .

[31]  Desire L. Massart,et al.  Application of SIMPLISMA for the assessment of peak purity in liquid chromatography with diode array detection , 1994 .

[32]  G. Kateman,et al.  ASPECTS OF PSEUDORANK ESTIMATION METHODS BASED ON AN ESTIMATE OF THE SIZE OF THE MEASUREMENT ERROR , 1994 .

[33]  Marcel Maeder,et al.  Second order global analysis: the evaluation of series of spectrophotometric titrations for improved determination of equilibrium constants , 1997 .

[34]  J. Huvenne,et al.  Self-Modeling Mixture Analysis Applied to FT-Raman Spectral Data of Hydrogen Peroxide Activation by Nitriles , 1997 .

[35]  M. Maeder,et al.  Nonlinear least-squares fitting of multivariate absorption data , 1990 .

[36]  G. Kateman,et al.  Multicomponent self-modelling curve resolution in high-performance liquid chromatography by iterative target transformation analysis , 1985 .

[37]  E. A. Sylvestre,et al.  Self Modeling Curve Resolution , 1971 .