Self-Modeling Mixture Analysis by Interactive Principal Component Analysis

Abstract

A key procedure for mixture analysis in self-modeling methods is to identify a pure wavelength (or pure variable) for each component in the mixture. A pure wavelength has intensity contributions from only one of the components in a mixture. In this paper, an interactive approach based on principal component analysis (IPCA) is presented for the pure wavelength selection. The approach is developed from a combination of key set factor analysis (KSFA) and SIMPLISMA (simple-to-use interactive self-modeling mixture analysis). Since all significant principal components are included and user interaction is available during the procedure of selecting pure wavelengths, this new approach effectively resolves complicated mixture data containing highly overlapping and nonlinear absorptivities. Moreover, the noise level of the original spectra is determined from secondary principal components and used in the scaling so that pure wavelength selection reflects the signal-to-noise ratio in the data. Simulated three-component mixture spectra are used to demonstrate the IPCA method; this is followed by a general approach for analyzing an esterification reaction using mid-infrared data. The KSFA, SIMPLISMA and IPCA methods are compared by analyzing a set of near-infrared spectra of methane, ethane, and propane mixtures. Results from the three pure wavelength methods are used as inputs to the method of alternating least-squares to produce predicted spectra very similar to the spectra of the pure components.