A feature selection method for overlapping peaks in vibrational spectroscopy using nonnegatively constrained classical least squares

Abstract

Chemometric models for multicomponent mixture analysis are reliant upon representative and accurate training data. However, the required amount of training data can increase exponentially with the number of constituents. Shifting spectral baselines and changing process objectives may necessitate recurring calibration data collection. Furthermore, all possible constituents may not be known prior to analysis. Here, we introduce a preprocessing procedure that reduces the burden of collecting extensive calibration datasets by filtering out non-target species (species that are not part of the calibration data) in real-time. The method, nonnegatively constrained classical least squares (NCCLS), utilizes a spectral nonnegativity constraint on non-target species to remove them from mixture spectra. The preprocessing method is physically motivated, does not rely on time-series data, and can operate in real-time. NCCLS is compared to established methods using in silico data and an experimental dataset comprised of Raman spectra and attenuated total reflectance - Fourier transform infrared spectra.