Exploratory analysis of biodiesel/diesel blends by Kohonen neural networks and infrared spectroscopy

Abstract

In this work, a rapid and non-destructive methodology was proposed for the evaluation of biodiesel/diesel blends with respect to the biodiesel feedstock type. For this comparison, mid-infrared spectroscopy data were analyzed with Principal Component Analysis (PCA) and Self-Organizing Map (SOM) chemometrics methods. The results showed that the SOM method was able to identify most of the samples according to their raw material while the PCA method did not differentiate biodiesel blends efficiently. In addition, using SOM subgroups of blends within the same origin were identified.