Multivariate Calibration of Infrared Spectra for Quantitative Analysis Using Designed Experiments

Abstract

The principal component regression (PCR) and partial least-squares (PLS) methods are used to calibrate and validate models for quantitative prediction of the composition of mixtures from FT-IR spectra. An experimental system of two- and three-component mixtures of xylene isomers was sampled with the use of statistical experimental designs. For two-component mixtures, the prediction error of independent validation samples decreased with increasing numbers of design points in the calibration. Four design points were needed to achieve a prediction accuracy of 0.0013 weight fraction. For three-component mixtures, a Scheffé {3,3} simplex lattice design, which has ten design points, achieved an equivalent accuracy of 0.002 weight fraction. There was little difference in performance between PLS and PCR computations. The results demonstrate the application of statistical methodology to the calibration of infrared spectra and show the importance of including an adequate number of samples in the calibration. The F test on the residual spectrum is shown to be a valuable tool for the identification of spurious data.