Application of robust principal component analysis-multivariate adaptive regression splines for the determination of API gravity in crude oil samples using ATR-FTIR spectroscopy

The robust principal component analysis-multivariate adaptive regression splines (r-PCA-MARS) has been applied and verified for analysis of the quantitative determination of American Petroleum Institute (degrees API) gravity values in crude oils. Seven principal component (PC) scores using 95.00% variance by principal component analysis (PCA) were applied as inputs in the MARS model. The calibration and prediction sets were obtained using duplex algorithm for the construction of the model and the then for the validation of the model. The calibration set (67*7) was used for the r-PCA-MARS model. The partial least squares regression (PLS-R), and support vector machine regression (SVM-R) models were utilized for comparison the quantitative value of the degrees API gravity in crude oils. In this paper, we also conducted a comparison study of Kennardstone (KS) and duplex splitting methods on PLS-R and SVM-R models. The efficiency of the rPCA-MARS model was evaluated using coefficient of determination (R-2), R-2 estimated by generalized cross-validation (R(2)GCV), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and mean absolute error (MAE). The optimal r-PCA-MARS model uses 32 basis functions to characterize the degrees API gravity values in crude oils. The correlation coefficients value for calibration and prediction sets were 0.997 and 0.926, respectively. The RMSEC, RMSEP, MAE, and R(2)GCV in the piecewise-cubic r-PCA-MARS model was 6.726*10(-13), 0.538, 0.290 and 0.988, respectively. According to the results, the r-PCA-MARS model provided high efficiency than commonly used regression models for prediction of degrees API gravity values in crude oils. The result of this study confirmed that the r-PCA-MARS model is the best model with more successful than the PLS-R and SVM-R models. It can be concluded that the r-PCA-MARS model is an appropriate model for describing the physicochemical properties of crude oil samples in the oil industry. (C) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).