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 chemicalcomposition of crude oil is not identical, and depending on its source and origin, it has a widerange of compositions. many studies have been conducted to identify the differences in crude oilsand define unique markers to make distinguishments. in recent years, special efforts have beenmade to develop simple and inexpensive sensing methods to analyze the chemical composition ofcrude oil and use these results in crude oil source identification [2]. an e-nose is one of thesesensing devices that can sense and recognize odors and flavors using an electronic chemical sensorarray system. this sensor array comprises a series of sensors, each exhibiting differentcharacteristics when exposed to the volatiles emitted by the analyte [3]. the combination of thissystem 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. theseprepared e-noses are used for the discrimination of crude oil samples based on their source andorigin. crude oil samples were collected from several oil wells belonging to the mansouri oil fieldand with two geological formations, ilam and saruk. the national iranian south oil companyprovided these samples. the colorimetric sensor arrays were made by spotting the ph and redoxindicators on the silica-gel plates; the fabricated sensor was stacked to the petri dish cap andexposed to vapors of the voc of crude oil samples. the petri dish should be transferred into anoven with a certain temperature for a specified period. for the colorimetric part of this sensor, theimage of the sensors was recorded by a scanner before and after its exposure to the vapors of theanalyte, and rgb values were analyzed with imagej software. pattern recognition methods suchas pca and hca were used to investigate the discrimination ability of the developed sensor arraysystems in the discrimination of crude oils. the crude oil samples were clustered in two classes in3-dimentional pc space based on pca results. the total variance of data for three first pcs was98 %. based on pca results, discrimination of crude oils based on the type of geologicalformations was successful. hca dendrogram represented excellent discrimination withoutmisclassification among all types of crude oil samples.