Quantitative Fourier transform infrared spectroscopy of binary mixtures of fatty acid esters using partial least squares regression

This work describes a quantitative spectroscopic method for the analysis of binary mixtures of fatty acid esters using multivariate data models based upon Fourier Transform Infra Red (FT-IR) spectroscopy. Multivariate calibration of binary mixtures has been performed using Partial Least Squares regression (PLS), with two approaches being applied for fitting the inner relation namely a standard linear function and a polynomial function. The use of a polynomial function with PLS (polyPLS) allows what appears to be a nonlinear component in the system to be modelled effectively. Autoscaling the spectra provided the best method of data transformation for improved accuracy of prediction. The prediction abilities of the various models is illustrated using both ribbon and hexagonal plots. The percentage error in the prediction for the two PLS methods was found to be in the ranges of 4–14% and 3–9%, for the linear and nonlinear functions respectively.