Data Fusion of Near Infrared, Fourier Transform Infrared and Raman Spectroscopy for Quantifying the Conversion of Poly Alpha Oil (Pao)

Abstract

The conversion of PAO is a key parameter in the production of lubricating oils, which can be analyzed by near infrared (NIR), Fourier Transform infrared (FT-IR) and Raman spectroscopy respectively, in combination with chemometrics methods. In order to improve the prediction accuracy, NPLS fusion strategy was proposed in this paper and utilized in this study. In addition, traditional data fusion strategies such as low-level, mid-level, high-level data fusion methods as well as sequential orthogonalized partial least squares fusion that has recently been proposed were also carried out in this study. Comparisons were conducted between models established based on fusion methods and individual spectroscopy. The results indicated that NPLS fusion method was more efficient than other fusion strategies which can be used to improve the model performance and robustness significantly.

Keywords: Data fusion, N-PLS, NIR spectroscopy, FT-IR spectroscopy, Raman spectroscopy, chemometrics