Clustering of Infrared Spectra of Lubricating Base Oils Using Adaptive Resonance Theory

Abstract

Adaptive resonance theory (ART2) is applied to the classification of lubricating base oils on the basis of their infrared spectra. Fifty-nine data samples are used which were collected from 12 refineries representing eight crude oil origins. The ART2 neural network is an unsupervised machine learning approach which is different from supervised learning, such as back-propagation neural networks, in that it determines both the number of classes and the assignments of data samples. The ART2 groups the 59 data samples into seven classes. Five of the seven classes are found to perfectly match the crude oil origins. Two of the seven classes (eight samples in total) are combined to form a single class. The class assignment of one data sample (sample 35) does not match the crude oil origin but is consistent with the prior observation of its spectrum. The work demonstrates that ART2 represents a useful alternative tool for infrared spectra interpretation.