Analysis of the ambiguity in the determination of quantum yields from spectral data on a photoinduced isomerization

Abstract Multivariate curve resolution (MCR) helps to uncover the spectra and concentration profiles of the pure components from sequences of spectra measured at a chemical reaction system. However, the underlying matrix factorization problem has often multiple solutions. This fact is known under the keyword rotational ambiguity and explains why different MCR methods can provide different decompositions for the same data. Kinetic reaction models can be used in order to constrain the feasible concentration profiles. This reduces the rotational ambiguity. Especially in the case that a first-order reaction model is assumed, the remaining ambiguity can be described completely analytically. A hard-model based MCR method is used for the simultaneous analysis of multiple data sets. The method is tested for a reversible two-step photokinetic model. The kinetic model cannot enforce a single, unique solution. Instead the remaining ambiguity is fully investigated. The practical benefit of the method is demonstrated for an experimental UV/Vis data set of a photoinduced isomerization.