文献类型： 外文期刊

作者： Wu Di ¹ ; Cao Fang ¹ ; Zhang Hao ² ; Sun Guang-ming ¹ ; Feng Lei ¹ ; He Yong ¹ ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310029, Zhejiang, Peoples R China

2.Zhejiang Acad Agr Sci, Digital Agr Res Ctr, Hangzhou 310021, Zhejiang, Peoples R China

关键词： Visible and near infrared (Vis-NIR) spectroscopy;Rice panicle blast;Uninformative variable elimination (UVE);Successive projections algorithm (SPA);Variable selection

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2009 年 29 卷 12 期

页码：

收录情况： SCI

摘要： Visible and near infrared (Vis-NIR) spectroscopy was used to fast and non-destructively classify the disease levels of rice particle blast. Reflectance, spectra between 325 and 1 075 rim were measured. Kennard-Stone algorithm was operated to separate sample, into calibration and prediction sets. Different spectral pretreatment methods. including standard normal variate (SNV) and multiplicative scatter correction (MSC), were used for the spectral pretreatment before further spectral analysis. A hybrid wavelength variable selection method which is combined with uninformative variable elimination (UVE) and successive projections algorithm (SPA) was operated to select effective wavelength variables from original spectra, SNV pretreated spectra and MSC pretreated spectra respectively. UVE was firstly operated to remove uninformative wavelength variables from the full spectrum. Then SPA selected the effective wavelength variables with less colinearity after LIVE. Least square-support vector machine (LS-SVM) was used as the calibration method for the spectral analysis in this study. The selected effective wavelengths were set a as input variables of LS-SVM model. The LS-SVM model established based on SNV-UVE SPA obtained the best results. Only six effective wavelengths (459, 546, 569 590, 775 and 981 run) were selected from the full spectrum which has 600 wavelength variables by UVE SPA, and their LS-SVM model's performance was further improved. For SNV-UVE-SPA-LS-SVM SVM model, coefficient of determination for prediction set (R-p(2)), root mean square error for prediction (RMSEP) and residual predictive deviation (RPD) were 0. 979, 0. 507 and 6.580, respectively. The overall results indicate that Vis-NIR spectroscopy is a feasible way to classify disease levels of rice panicle blast fast and non-destructively. UVE-SPA is an efficient variable selection method for the spectral analysis. and their selected effective wavelengths can represent the useful information of the full-spectrum and have higher signal/noise ratio and less colinearity.