Efficient and Simplified Modeling for Kerosene Processing Quality Detection Using Partial Least Squares-Discriminant Analysis Regression

Issa, Hayder M. and Hama Salih, Rezan H. (2024) Efficient and Simplified Modeling for Kerosene Processing Quality Detection Using Partial Least Squares-Discriminant Analysis Regression. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 12 (1). pp. 135-142. ISSN 2410-9355

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Abstract

Kerosene from various refineries and crudes is used for heating and other purposes in many countries like Iraq; therefore, it is important to identify its source to recognize and tax any adulteration. In this study, a fast classification technique for kerosene marketed in Iraq was developed with the goal of identifying its quality. The samples were categorized using a supervised partial least squares discriminant analysis (PLS-DA) approach. Multivariate analyses using agglomerative hierarchal clustering and principal component analysis were utilized to identify outliers and sample dissimilarities. The dataset was divided into calibration and prediction sets. The prediction set was used to evaluate the model’s separation performance. The Q2 cross-validation was applied. The PLS-DA models achieved significant accuracy, sensitivity, and specificity, showing strong segregation ability, notably for the calibration set (100% accuracy and 1.00 sensitivity). It was found that kerosene processing can be classified rapidly and non-destructively without the need for complicated analyses, demonstrating the best results for classification even when compared with the classification outcomes of other fuels. This PLS-DA approach has never been looked at before for process quality detection, and the results are comparable to direct kerosene classification with soft independent modeling of class analogy and support vector machines.

Item Type:	Article
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Uncontrolled Keywords:	Supervised discrimination technique, Modeling, Kerosene processing, Quality control, PLS-DA, Machine learning tool
Subjects:	Q Science > QD Chemistry
Divisions:	ARO-The Scientific Journal of Koya University > VOL 12, NO 1 (2024)
Depositing User:	Dr Salah Ismaeel Yahya
Date Deposited:	02 Sep 2024 06:58
Last Modified:	02 Sep 2024 06:58
URI:	http://eprints.koyauniversity.org/id/eprint/479

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