Comparison between NIR, FT-IR and Raman for quantitative analysis of the conversion of poly alpha oil (PAO)

Abstract

PAO base oil is the main component of high-quality synthetic lubricating oil. Its product quality is related to the conversion of monomer α-olefin. Spectral analysis method combined with chemometrics can be used to examine the conversion rate with fast analysis speed and low cost. In the study, we compared the performance of near-infrared (NIR), Fourier Transform infrared (FT-IR) and Raman spectroscopy to characterize the conversion of PAO base oil, established three calibration models for conversion of PAO by partial least square regression, and evaluated the performance with several preprocessing methods of the first and second derivative, multiplicative scatter correction (MSC), standard normal variate (SNV), adaptive iteratively reweighted penalized least squares (airPLS). The results show that, Raman spectrum pretreated with MSC can provide a good prediction performance with an accuracy indicator root mean square error in cross validation (RMSEP) of 0.62, but the test repeatability is unacceptable. In contrary, NIR can provide a better repeatability but a lower prediction accuracy with the RMSEP indicator of 1.02. The FT-IR spectrum pretreated with second derivative has the best prediction accuracy with a RMSEP of 0.54 and excellent repeatability, which can be regarded as the most suitable spectral technique for rapid analysis of the conversion of PAO base oil.