Multivariate calibrations in Fourier transform infrared spectrometry for prediction of kerosene properties

Seven aircraft fuel quality properties: density, freezing point, flash point, aromatic content, initial boiling point, final boiling point and viscosity, have been predicted from the Fourier transform infrared (FT-IR) spectra in the range of 4000 to 600 cm−1, using three multivariate techniques. Multiple linear regression (using the all-variables and stepwise methods), principal components regression (using the all-variables and stepwise methods) and partial least squares (PLS) models, have been employed and their predictive capabilities evaluated. Although the standard error of prediction (SEP) has been the main parameter considered to select the “best model”, repeatability and reproducibility have been also considered. FT-IR-PLS repeatability and reproducibility values fall well within the ASTM ranges and SEP values are really good. Sample manipulation was improved by using a stopped-flow system.