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A non-destructive distinctive method for discrimination of automobile lubricant variety by visible and short-wave infrared spectroscopy.

Jiang L, Liu F, He Y - Sensors (Basel) (2012)

Bottom Line: In order to improve prediction precision further, BVB was processed by UVE-PLS and the optimal cutoff threshold of UVE was found by SA.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.

View Article: PubMed Central - PubMed

Affiliation: Zhejiang Technology Institute of Economy, Hangzhou 310018, China. abbyu111@sina.com

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.

No MeSH data available.


Vis-SWNIR spectra of three varieties of lubricant.
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f2-sensors-12-03498: Vis-SWNIR spectra of three varieties of lubricant.

Mentions: Typical spectra of three varieties of lubricants are shown in Figure 2. Due to potential system imperfection, the scattering ray affects the accuracy of measurement; there is lots of noise in the 325–400 nm region. The spectra of samples from all three varieties have similar gross patterns of absorbance, there is a sharp absorption peak round 380 nm. After 400 nm, the absorbance values begin to decrease, and the spectral curve is flat. Though the trend of the spectra is similar, some latent differences and features exist according to the chemical components and color variance which could not be distinguished by the naked eye. With a closed observation, a small difference between Cc lubricant and the two other lubricants existed in the 400–450 nm region, which might have resulted from the color variance.


A non-destructive distinctive method for discrimination of automobile lubricant variety by visible and short-wave infrared spectroscopy.

Jiang L, Liu F, He Y - Sensors (Basel) (2012)

Vis-SWNIR spectra of three varieties of lubricant.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376611&req=5

f2-sensors-12-03498: Vis-SWNIR spectra of three varieties of lubricant.
Mentions: Typical spectra of three varieties of lubricants are shown in Figure 2. Due to potential system imperfection, the scattering ray affects the accuracy of measurement; there is lots of noise in the 325–400 nm region. The spectra of samples from all three varieties have similar gross patterns of absorbance, there is a sharp absorption peak round 380 nm. After 400 nm, the absorbance values begin to decrease, and the spectral curve is flat. Though the trend of the spectra is similar, some latent differences and features exist according to the chemical components and color variance which could not be distinguished by the naked eye. With a closed observation, a small difference between Cc lubricant and the two other lubricants existed in the 400–450 nm region, which might have resulted from the color variance.

Bottom Line: In order to improve prediction precision further, BVB was processed by UVE-PLS and the optimal cutoff threshold of UVE was found by SA.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.

View Article: PubMed Central - PubMed

Affiliation: Zhejiang Technology Institute of Economy, Hangzhou 310018, China. abbyu111@sina.com

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.

No MeSH data available.