Multivariate Calibration by Variable Selection for Blends of Raw Soybean Oil/Biodiesel from Different Sources Using Fourier Transform Infrared Spectroscopy (FTIR) Spectra Data

Abstract

The partial least-squares (PLS) calibration method as a chemometric tool was used to develop a calibration model using Fourier transform infrared spectroscopy (FTIR) spectra data of biodiesel samples from different sources, such as cotton, castor, and palm, which were mixed with raw soybean oil to simulate an adulteration system. The PLS calibration method was applied with and without variable selection to quantify the amount of raw soybean oil present in these samples. Classic methods of variable selection, such as forward and stepwise, were applied to all origins together and each one separately. Variable selection improves not only the stability of the model to the colinearity in multivariate spectra but also the interpretability of the relationship between the model and the sample composition, which means that it becomes easier to determine and quantify the amount of raw soybean oil mixed in each biodiesel source.