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| 基于变量选择的蚕茧茧层量可见-近红外光谱无损检测 | |
| 引用本文： | 黄凌霞,吴 迪,金航峰,赵丽华,何 勇,金佩华,楼程富.基于变量选择的蚕茧茧层量可见-近红外光谱无损检测[J].农业工程学报,2010,26(2):231-236. |
| 作者姓名： | 黄凌霞 吴 迪 金航峰 赵丽华 何 勇 金佩华 楼程富 |
| 作者单位： | 1. 浙江大学动物科学学院,杭州,310029 2. 浙江大学生物系统工程与食品科学学院,杭州,310029 3. 浙江省湖州市农业科学研究院,湖州,313000 |
| 基金项目： | 国家基金科学基金项目（30671213）；中国博士后科学基金项目（20080431301）；浙江省教育厅科研基金项目（20070125）；浙江大学2009年青年教师交叉研究种子基金；湖州市科技计划项目（2008YN24） |
| 摘 要： | 以蚕茧茧层量为研究对象，研究了基于可见-近红外光谱技术的蚕茧茧层量无损检测方法。采用最小二乘支持向量机（least square-support vector machine，LS-SVM）建立可见-近红外光谱模型。采用无信息变量消除算法（uninformative variable elimination, UVE）与连续投影算法（successive projections algorithm, SPA）相结合选取光谱有效波长。结果表明，基于UVE-SPA法进行变量选择，最终将原始光谱的600个光谱变量减少到了8个（673，937，963，982，989，992，995和1 008 nm）。基于此8个变量建立的LS-SVM模型得到了预测集的确定系数（Rp2）为0.5354，误差均方根（RMSEP）为0.0373的预测结果。表明可见-近红外光谱可以用于对蚕茧的茧层量进行无损检测，同时UVE-SPA是一种有效的光谱变量选择方法。 |
| 关 键 词： | 近红外光谱，无损检测，模型分析，蚕茧，茧层量，无信息变量消除算法（UVE），连续投影算法（SPA） |
| 收稿时间： | 2009/4/11 0:00:00 |
| 修稿时间： | 2009/6/17 0:00:00 |
Non-destructive detection of cocoon shell weight based on variable selection by visible and near infrared spectroscopy |
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| Huang Lingxia,Wu Di,Jin Hangfeng,Zhao Lihua,He Yong,Jin Peihua,Lou Chengfu.Non-destructive detection of cocoon shell weight based on variable selection by visible and near infrared spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering,2010,26(2):231-236. | |
| Authors: | Huang Lingxia Wu Di Jin Hangfeng Zhao Lihua He Yong Jin Peihua Lou Chengfu |
| Institution: | 1.College of Animal Sciences/a>;Zhejiang University/a>;Hangzhou 310029/a>;China/a>;2.College of Biosystems Engineering and Food Science/a>;3.Huzhou Agricuhural Academy of Zhejiang Province/a>;Huzhou 313000/a>;China |
| Abstract: | Visible and near-infrared reflectance spectroscopy (Vis-NIRS) was applied to measure cocoon shell weight. Least square-support vector machine (LS-SVM) was used to establish the Vis-NIR model. Uninformative variable elimination and successive projections algorithm were combined to select wavelength from Vis-NIR spectroscopy. Eight wavelength variables, namely 673, 937, 963, 982, 989, 992, 995 and 1 008 nm, were selected. The UVE-SPA-LS-SVM model was established based on these eight wavelength variables. The results showed that the determination coefficient for prediction set (Rp2) was 0.5354, and the root mean square error for prediction (RMSEP) was 0.0373. It is concluded that Vis-NIRS can be used in the cocoon shell weight measurement, and UVE-SPA is a feasible and efficient algorithm for the spectral variable selection. |
| Keywords: | near infrared spectroscopy nondestructive examination model analysis cocoon shell weight uninformative variable elimination (UVE) successive projections algorithm (SPA) |
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