Abstract
Multivariate spectroscopic data is increasingly abundant in the chemical and pharmaceutical industries. However, it is often challenging to estimate reaction kinetics directly from it. Recent advances in obtaining kinetic parameter estimates from spectroscopic data based on large-scale nonlinear programming (NLP), maximum likelihood principles, and discretization on finite elements lead to increased speed and efficiency (Chen et al., 2016). These new techniques have great potential for widespread use in parameter estimation. However they are currently limited due to their applicability to relatively small problem sizes. In this work, we extend the open-source package for estimating reaction kinetics directly from spectra or concentration data, KIPET, for use with multiple experimental datasets, or multisets (Schenk et al., 2020). Through a detailed initialization scheme and by taking advantage of large-scale nonlinear programming techniques and problem structure, we are able to solve large problems obtained from multiple experiments, simultaneously. The enhanced KIPET package can solve problems wherein multiple experiments contain different reactants and kinetic models, different dataset sizes with shared or unshared individual species’ spectra, and can obtain confidence intervals quickly based on the NLP sensitivities. In addition, we propose a new variance estimation technique based on maximum likelihood derivations for unknown covariances from two sample populations. This new variance estimation technique is compared to the previously proposed iterative-heuristics-based algorithm of Chen et al. (2016) for distinguishing between variances of the noise in model variables and in the spectral measurements. We demonstrate the new techniques on a variety of example problems, with sample code, to show the utility of the approach and its ease of use. We also include the curve-fitting problem to cases where we have concentration data given directly, and are required to estimate kinetic parameters across multiple experimental datasets.