Where does the crude oil originate? The role of near-infrared spectroscopy in accurate source detection

Abstract

Accurate tracing of crude oil origins is essential for thwarting deceptive trade practices, including origin falsification to evade taxes, thereby preventing economic losses and security threats for importing nations. Traditional crude oil origin determination methods require complex sample preparation, expensive instrumentation, and stable testing environments, rendering them impractical for real-time analysis at locations such as ports. This paper introduces a novel approach utilizing near-infrared spectroscopy (NIRS) combined with deep learning algorithms to expedite and enhance the precision of crude oil source identification. To effectively eliminate outliers, an improved Mahalanobis distance is introduced, incorporating regularization principles and global-local concepts. This approach addresses the challenges of inverting covariance matrices in high-dimensional spectra and excludes samples with localized aberrations. Furthermore, the integration of multi-receptive fields perception, Transformer-based global information interaction, and the scSE attention mechanism has led to the development of an MG-Unet model, designed to resolve spectral peak overlap issues and capture long-range feature dependencies. The proposed method achieves state-of-the-art accuracy of 96.92%, demonstrating significant potential for reliable crude oil source tracing.