Comparative Study of Hard- and Soft-Modeling Algorithms for Kinetic Data Processing

Abstract

This article critically compares the efficacy of three algorithms, namely Alternating Least-squares Multi Curve Resolution (ALS-MCR), Hard Modeling Alternating Least-squares (HM-ALS), and classical Hard Modeling Multi Curve Resolution (HM-MCR) in finding the true values of rate constants associated with a kinetic model. Simulated experiments on the simple system (A1⟶A2⟶A3) indicate that soft-modeling ALS-MRC methodology, which is subject only to linear constraints, does not ensure that experimental responses are correctly deconvolved, thus preventing further calculations to determine the true rate constants. Inclusion of the kinetic model in the ALS scheme, which gives rise to the HM-ALS methodology, was found to yield a correct assessment of the rate coefficients but had a large computational cost. Numerical experiments employing a more complex model (A1⇌A2⇌A3) were also carried out, mainly to evaluate strategies for performing efficient searches on multidimensional multimodal least-squares surfaces using HM-ALS and HM-MCR. This study again revealed the efficiency and reliability of classical HM-MCR methods. Results from simulations were corroborated by analysis of data from an experimental study of chromate reduction by hydrogen peroxide; the mechanism of which is similar in complexity to those considered in simulations. The present work suggests that HM-MCR algorithms implementing a multiminimum search strategy are the method of choice for analyzing two-dimensional kinetic data.