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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.


Stability distribution of each variable, and the two red dotted lines indicate the lower and upper cutoff.
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f7-sensors-12-03498: Stability distribution of each variable, and the two red dotted lines indicate the lower and upper cutoff.

Mentions: Figure 6 shows the result of the optimal cutoff and the best fitness function, and the optimal cutoff was 38.1088, and the best fitness function value of 0.8904 was obtained by SA. The stability of each variable in the BVB was shown in Figure 7. In the Figure 7, the dotted lines show the cutoff threshold, the vertical bar in Figure 7 indicated the stability range. Variables whose stability lies within the dotted lines were eliminated, and the variables whose stability lies out of the dotted lines were retained. Finally, only five variables in the BVB were retained.


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)

Stability distribution of each variable, and the two red dotted lines indicate the lower and upper cutoff.
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-12-03498: Stability distribution of each variable, and the two red dotted lines indicate the lower and upper cutoff.
Mentions: Figure 6 shows the result of the optimal cutoff and the best fitness function, and the optimal cutoff was 38.1088, and the best fitness function value of 0.8904 was obtained by SA. The stability of each variable in the BVB was shown in Figure 7. In the Figure 7, the dotted lines show the cutoff threshold, the vertical bar in Figure 7 indicated the stability range. Variables whose stability lies within the dotted lines were eliminated, and the variables whose stability lies out of the dotted lines were retained. Finally, only five variables in the BVB were retained.

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.