Di Wu, Cao Fang, Zhang Hao, Sun Guang-Ming, Feng Lei, He Yong
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Dec;29(12):3295-9.
Visible and near infrared (Vis-NIR) spectroscopy was used to fast and non-destructively classify the disease levels of rice panicle blast. Reflectance spectra between 325 and 1 075 nm were measured. Kennard-Stone algorithm was operated to separate samples 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 UVE. 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 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 nm) 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 model, coefficient of determination for prediction set (R2(p)), 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.
可见近红外（Vis-NIR）光谱法被用于快速、无损地对水稻穗颈瘟的病害等级进行分类。测量了325至1075nm之间的反射光谱。采用肯纳德-斯通算法将样本分为校正集和预测集。在进一步进行光谱分析之前，使用了包括标准正态变量变换（SNV）和多元散射校正（MSC）在内的不同光谱预处理方法对光谱进行预处理。采用一种将无信息变量消除（UVE）和连续投影算法（SPA）相结合的混合波长变量选择方法，分别从原始光谱、SNV预处理光谱和MSC预处理光谱中选择有效波长变量。首先运用UVE从全光谱中去除无信息波长变量。然后，SPA在UVE之后选择共线性较低的有效波长变量。在本研究中，采用最小二乘支持向量机（LS-SVM）作为光谱分析的校正方法。将所选的有效波长设置为LS-SVM模型的输入变量。基于SNV-UVE-SPA建立的LS-SVM模型取得了最佳结果。通过UVE-SPA从具有600个波长变量的全光谱中仅选择了六个有效波长（459、546、569、590、775和981nm），并且其LS-SVM模型的性能得到了进一步提升。对于SNV-UVE-SPA-LS-SVM模型，预测集的决定系数（R2(p)）、预测均方根误差（RMSEP）和剩余预测偏差（RPD）分别为0.979、0.507和6.580。总体结果表明，可见近红外光谱法是一种快速、无损地对水稻穗颈瘟病害等级进行分类的可行方法。UVE-SPA是一种用于光谱分析的有效变量选择方法，其所选的有效波长能够代表全光谱的有用信息，具有较高的信噪比和较低的共线性。
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