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 analys is of binary mixtures of fatty acid esters using multivariate data mod els based upon Fourier Transform Infra Red (FT-IR) spectroscopy. Multi variate calibration of binary mixtures has been performed using Partia l Least Squares regression (PLS), with two approaches being applied fo r fitting the inner relation namely a standard linear function and a p olynomial 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 predi ction abilities of the various models is illustrated using both ribbon and hexagonal plots. The percentage error in the prediction for the t wo PLS methods was found to be in the ranges of 4-14% and 3-9%, for th e linear and nonlinear functions respectively.