Raman Spectral Analysis of Low-content Benzene Concentration in Gasoline with Partial Least Squares Based on Interference Peak Subtraction.

Raman spectroscopy is adopted to detect the low-content benzene concentrations in gasoline products. Due to the peak overlap of benzene and other species in the gasoline spectrum, the associated statistical regression methods cannot make stable predictions unless there are enough training samples. To extend their extrapolation to small-size training sets, we propose the method of partial least squares based on a spectral pretreatment of interference peak subtraction (IPS-PLS). During the analysis, after spectral interpolation and baseline removal, we extract the benzene peak by interference peak subtraction (IPS), and then partial least squares (PLS) is applied to make a prediction. The experimental results demonstrate that, IPS can extract benzene information effectively, and help to decrease principal components needed by PLS, thus IPS-PLS is superior to direct PLS with small-size training sets, and depends less on the training sample distribution. Meanwhile, IPS-PLS can reach the standard of ASTM 3606-10 with the least of 9 training samples, while keeping its max predictive error less than 0.1254% (v/v), which shows promising prospects in gasoline quality test.