Source identification in crude oil samples by a couple of colorimetric paper-based E-nose and chemometrics data analysis techniques

Crude oil source identification is crucial in discussing oil pollution or oil spills [1]. The chemical composition of crude oil is not identical, and depending on its source and origin, it has a wide range of compositions. Many studies have been conducted to identify the differences in crude oils and define unique markers to make distinguishments. In recent years, special efforts have been made to develop simple and inexpensive sensing methods to analyze the chemical composition of crude oil and use these results in crude oil source identification [2]. An E-Nose is one of these sensing devices that can sense and recognize odors and flavors using an electronic chemical sensor array system. This sensor array comprises a series of sensors, each exhibiting different characteristics when exposed to the volatiles emitted by the analyte [3]. The combination of this system with appropriate pattern recognition methods is fascinating in the analysis of crude oil [4]. In this research, the colorimetric paper-based E-nose is instructed by pH indicators. These prepared E-noses are used for the discrimination of crude oil samples based on their source and origin. Crude oil samples were collected from several oil wells belonging to the Mansouri oil field and with two geological formations, Ilam and Saruk. The National Iranian South Oil Company provided these samples. The colorimetric sensor arrays were made by spotting the pH and redox indicators on the silica-gel plates; the fabricated sensor was stacked to the petri dish cap and exposed to vapors of the VOC of crude oil samples. The petri dish should be transferred into an oven with a certain temperature for a specified period. For the colorimetric part of this sensor, the image of the sensors was recorded by a scanner before and after its exposure to the vapors of the analyte, and RGB values were analyzed with ImageJ software. Pattern recognition methods such as PCA and HCA were used to investigate the discrimination ability of the developed sensor array systems in the discrimination of crude oils. The crude oil samples were clustered in two classes in 3-dimentional PC space based on PCA results. The total variance of data for three first PCs was 98 %. Based on PCA results, discrimination of crude oils based on the type of geological formations was successful. HCA dendrogram represented excellent discrimination without misclassification among all types of crude oil samples.