Combining hard- and soft-modelling to solve kinetic problems

A novel approach mixing the qualities of hard-modelling and soft-modelling methods is proposed to analyse kinetic data monitored spectrometrically. Ta king as a basis the Multivariate Curve Resolution-Alternating Least Squares method (MCR-ALS), which obtains the pure concentration profiles and spectr a of all absorbing species present in the raw measurements by using typical soft-modelling constraints, a new hard constraint is introduced to force s ome or all the concentration profiles to fulfill a kinetic model, which is refined at each iterative cycle of the optimisation process. This modification of MCR-ALS drastically decreases the rotational ambiguity associated with the kinetic profiles obtained using exclusively soft-model ling constraints. The optional inclusion of some or all the absorbing speci es into the kinetic model allows the successful treatment of data matrices whose instrumental response is not exclusively due to the chemical componen ts involved in the kinetic process, an impossible scenario for classical ha rd-modelling approaches. Moreover, the possible distinct constraint of each of the matrices in a three-way data set allows for the simultaneous analys is of kinetic runs with diverse kinetic models and rate constants. Thus, th e introduction of model-based and model-free features in the treatment of k inetic data sets yields more satisfactory results than the application of p urr: hard- or pure self-modelling approaches. Simulated and real examples a re used to confirm this statement. (C) 2000 Elsevier Science B.V. All right s reserved.