Extension of quadrature orthogonal signal corrected two-dimensional (QOSC 2D) correlation spectroscopy: Principal component analysis and partial least squares-based QOSC 2D

The basic idea of orthogonal signal correction (OSC) is to remove a certain portion of the systematic variations of data not directly affected by the pertinent controlling variables. We have explored the combination of such OSC filtering and two-dimensional (2D) correlation analysis together to improve the quality of 2D correlation spectra. In particular, we proposed the quadrature OSC (QOSC) filtering method to deal with the problem of losing the portion of information, which is perpendicular to the external controlling variable but being quite significant to the 2D asynchronous correlation analysis. The present study will describe the concept of either PCA- or PLS-based QOSC 2D analysis, and their application to a simulated spectral dataset with one strong contaminating peak on temperature-dependent IR spectra of poly(N-isopropylacrylamide) (PNiPa). The comparison of the different QOSC techniques, e.g., directly external variable based, PCA-based, and PLS-based QOSC, on the new spectral dataset is also performed.