A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy

Abstract

A novel method which is a combination of wavelet packet transform (WPT), uninformative variable elimination by partial least squares (UVE-PLS) and simulated annealing (SA) to extract best variance information among different varieties of lubricants is presented. A total of 180 samples (60 for each variety) were characterized on the basis of visible and short-wave infrared spectroscopy (VIS-SWNIR), and 90 samples (30 for each variety) were randomly selected for the calibration set, whereas, the remaining 90 samples (30 for each variety) were used for the validation set. The spectral data was split into different frequency bands by WPT, and different frequency bands were obtained. SA was employed to look for the best variance band (BVB) among different varieties of lubricants. In order to improve prediction precision further, BVB was processed by UVE-PLS and the optimal cutoff threshold of UVE was found by SA. Finally, five variables were mined, and were set as inputs for a least square-support vector machine (LS-SVM) to build the recognition model. An optimal model with a correlation coefficient (R) of 0.9850 and root mean square error of prediction (RMSEP) of 0.0827 was obtained. The overall results indicated that the method of combining WPT, UVE-PLS and SA was a powerful way to select diagnostic information for discrimination among different varieties of lubricating oil, furthermore, a more parsimonious and efficient LS-SVM model could be obtained.

Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis

“…In other words, at the beginning of these detail coefficients contain a wealth of high-frequency information, which indicates that there is some high-frequency information at the beginning of the spectra [ 29 ]. Actually, due to potential system imperfection and limitation of spectroradiometer measurement, the scattering ray usually results in noise and disturbance at the beginning and the end of the spectral data [ 14 ], so this information at the beginning of these detail coefficients is likely caused by imperfections of the system and the spectroradiometer used in this research, so only approximate coefficients cA 3 are taken as characteristic features for further analysis.…”

“…A good model should have a low RMSEC, a low RMSEP, a high r , and a small difference between RMSEC and RMSEP [14]. …”

“…The quality of the regression model was quantified by root mean squared error of calibration (RMSEC), root mean squared error of prediction (RMSEP), and the correlation coefficient ( r ) between the predicted and measured parameters [ 27 ]. A good model should have a low RMSEC, a low RMSEP, a high r , and a small difference between RMSEC and RMSEP [ 14 ].…”

Color Measurement of Tea Leaves at Different Drying Periods Using Hyperspectral Imaging Technique

“…Partial least squares (PLS) is an effective method which has been widely used for establishing calibration models in many spectral studies [14] , [15] , [16] , [17] , [18] . This algorithm is very efficient when predicting many different measured variables that are collinear.…”