Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran.
Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 May 5;232:118157. doi: 10.1016/j.saa.2020.118157. Epub 2020 Feb 16.
Classification based on °API gravity is very important to estimate the parameters related to the extraction, purification, toxicity, and pricing of crude oils. Spectroscopy methods show some advantages over ASTM and API methods for crude oil analysis. The attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with chemometric methods has been applied as a quick and non-destructive method for crude oil analysis. In this work, a new analytical method using ATR-FTIR spectroscopy associated with chemometric methods were proposed for adressing regression and classification tasks for crude oils analysis based on °API gravity values. The designed methods are rapid, economic, and nondestructive ways in production process of oil industry. The spectral data were used for estimation of °API gravity using two approaches according to PLS-R and SVM-R algorithm, separately. The ATR-FTIR spectral data were also analyzed by classification method using the partial least squares-discriminant analysis (PLS-DA) for crude oil classification. The samples were classified into three classes based on their °API gravity values. The SVM-R model showed better results than PLS-R for °API gravity values using the F-test at 95% of confidence. The result of classification, showed about 100% accuracy and a zero classification error for calibration and prediction samples in PLS-DA algorithm.
基于 API 度值的分类对于估计与原油的提取、纯化、毒性和定价相关的参数非常重要。与 ASTM 和 API 方法相比,光谱方法在原油分析方面具有一些优势。衰减全反射傅里叶变换红外(ATR-FTIR)光谱结合化学计量学方法已被用作一种快速且无损的原油分析方法。在这项工作中,提出了一种新的分析方法,使用 ATR-FTIR 光谱结合化学计量学方法,用于解决基于 API 度值的原油分析的回归和分类任务。所设计的方法在石油工业的生产过程中是快速、经济和无损的。光谱数据分别使用偏最小二乘回归(PLS-R)和支持向量机回归(SVM-R)算法用于估计 API 度值。ATR-FTIR 光谱数据也通过偏最小二乘判别分析(PLS-DA)进行分析,用于对原油进行分类。根据 API 度值,样品分为三类。使用置信度为 95%的 F 检验,SVM-R 模型在 API 度值方面显示出比 PLS-R 更好的结果。在 PLS-DA 算法中,分类结果显示校准和预测样品的准确率约为 100%,且无分类错误。