An automated Peak Group Analysis for vibrational spectra analysis

Abstract

Peak Group Analysis (PGA) is a multivariate curve resolution technique that attempts to extract single pure component spectra from time series of spectral mixture data. It requires that the mixture spectra consist of relatively sharp peaks, as is typical in IR and Raman spectroscopy. PGA aims to construct from individual peaks the associated pure component spectra in the form of nonnegative linear combinations of the right singular vectors of the spectral data matrix.This work presents an automated PGA (autoPGA) that starts with upstream peak detection applied to time series of spectra, combining different window-based peak detection techniques with balanced peak acceptance criteria and peak grouping to deal with repeated detections. The next step is a single-spectrum-oriented PGA analysis. This is followed by a downstream correlation analysis to identify pure component spectra that occur multiple times. AutoPGA provides a complete pure component factorization of the matrix of measured data. The algorithm is applied to FT-IR data sets on various rhodium carbonyl complexes and from an equilibrium of iridium complexes.