Fast and Noninvasive Determination of Viscosity of Lubricating Oil Based on Visible and Near Infrared Spectroscopy

Visible and near infrared (Vis/NIR) spectroscopy was investigated for the fast and nondestructive determination of viscosity of lubricating oil. A total of 150 oil samples were scanned, and different calibration models were developed with the pretreatment of smoothing and standard normal variate. The input variables of calibration were the principal component selected by principal component analysis (PCA) and characteristic wavelengths selected by successive projections algorithm (SPA). The calibration model were developed using partial least squares (PLS), multiple linear regression (MLR) and back propagation neural networks (BPNN). The results indicated that PCA-BPNN and SPA-BPNN models were better than the linear models (PCA-PLS, PCA-MLR, SPA-PLS and SPA-MLR). The correlation coefficients were 0.971 for PCA-BPNN and 0.964 for SPA-BPNN. This demonstrated that BPNN could make good use of the nonlinear information in spectral data, and SPA was a powerful way for the selection of characteristic wavelengths. The selected wavelengths were helpful for the development of portable lubricating oil viscosity detection instrument.