Spectral Pattern Recognition of in Situ FT-IR Spectroscopic Reaction Data Using Minimization of Entropy and Spectral Similarity (MESS): Application to the Homogeneous Rhodium Catalyzed Hydroformylation of Isoprene

Abstract

An improved algorithm using minimization of entropy and spectral similarity (MESS) was tested to recover pure component spectra from in situ experimental Fourier transform infrared (FT-IR) reaction spectral data, which were collected from a homogeneous rhodium catalyzed hydroformylation of isoprene. The experimental spectra are complicated and highly overlapping because of the presence of multiple intermediate products in this reaction system. The traditional entropy minimization method fails to resolve real reaction mixture spectra, but MESS can successfully reconstruct pure component spectra of unknown intermediate products for real reaction systems by the addition of minimization of spectral similarity. The quantitative measure of spectral similarity between two spectra was given by their inner products. The results indicate that MESS is a stable and useful algorithm for spectral pattern recognition of highly overlapped experimental reaction spectra. Comparison is also made between MESS, entropy minimization, simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), interactive principle component analysis (IPCA), and orthogonal projection approach–alternating least squares (OPA-ALS).