Efficient Identification of Crude Oil via Combined Terahertz Time-Domain Spectroscopy and Machine Learning

Abstract

The quality of crude oil varies significantly according to its geographical origin. The efficient identification of the source region of crude oil is pivotal for petroleum trade and processing. However, current methods, such as mass spectrometry and fluorescence spectroscopy, suffer problems such as complex sample preparation and a long characterization time, which restrict their efficiency. In this work, by combining terahertz time-domain spectroscopy (THz-TDS) and a machine learning analysis of the spectra, an efficient workflow for the accurate and fast identification of crude oil was established. Based on THz-TDS of 83 crude oil samples obtained from six countries, a machine learning protocol involving the dimension reduction of spectra and classification was developed to identify the geological origins of crude oil, with an overall accuracy of 96.33%. This work demonstrates that THz spectra combined with a modern numerical scheme analysis can be readily employed to categorize crude oil products efficiently.