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Proceedings Paper

Improving the optimization efficiency and precision of least squares support vector regression (LSSVR) for pear property prediction
Author(s): Yong Hao; Yande Liu; Hailiang Zhang; Xuemei Liu; Yuanyuan Pan
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Paper Abstract

In this study, Visible/near-infrared (Vis/NIR) diffuse reflectance spectroscopy at 530-1560 nm region was investigated for the analysis of the soluble solids content (SSC) and color of pear. Least squares support vector regression (LSSVR) has been proven to be a powerful tool for modeling complex samples through the use of adapted kernel functions. However, one of the major drawbacks of LSSVR is that the optimization of the regularization and kernel meta-parameters is time-consuming during training the model, and the modeling results are sensitive to spectral noise. Wavelet compression pretreatment is an effective method for spectral information extraction and noise elimination. The calibration set was composed of 75 pear samples and 32 pear samples were used as the validation set. The raw and pretreated spectra by wavelet compression were modeled using LSSVR, It was shown that wavelet compression procedure not only shortened the modeling time, but also improved the predictive precision. The correlation coefficient (r) was improved from 0.78 to 0.93 for SSC, and from 0.95 to 0.96 for color, respectively. The root mean square error of prediction (RMSEP), optimization time and calibration variables were reduced from 0.68, 0.33s and 1031 to 0.41, 0.03s and 24 for SSC, while from 1.10, 0.33s and 1031 to 1.07, 0.03s and 40 for color. The results indicated that Vis/NIR spectroscopy combined with wavelet compression procedure and LSSVR is a reliable approach for predicting the SSC and color of pear.

Paper Details

Date Published: 12 October 2010
PDF: 6 pages
Proc. SPIE 7656, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 76566X (12 October 2010); doi: 10.1117/12.865737
Show Author Affiliations
Yong Hao, East China Jiaotong Univ. (China)
Yande Liu, East China Jiaotong Univ. (China)
Hailiang Zhang, East China Jiaotong Univ. (China)
Xuemei Liu, East China Jiaotong Univ. (China)
Yuanyuan Pan, East China Jiaotong Univ. (China)


Published in SPIE Proceedings Vol. 7656:
5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment
Yudong Zhang; Jose M. Sasian; Libin Xiang; Sandy To, Editor(s)

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