江西农业学报2011，Vol.23Issue(5)：22-24,3.
样本集选择对稻谷千粒重NIR模型预测精度的影响
Effect of Selecting Sample Sets on Predictive Ability of NIR Model for 1000-grain Weight of Paddy
摘要
Abstract
The effect of selecting calibration sample sets on the NIR predictive model for 1000- grain weight of paddy was investigated. NIR models were developed by using partial least square regression in the wavelength region from 600 nm to 1100 nm under the conditions of different calibration sets with various paddy quantities, different ratios of the calibration set to the validation set, and different methods for selecting the calibration sets. The developed NIR models were evaluated according to determination coefficients for cress- validation (Rv2) and for prediction (Rp2), and standard errors for cross- validation (SECV) and for prediction (SEP). The results showed that the quantity of paddy sample, the ratio of the calibration set to the validation set and the method for selecting calibration set all had significant influences upon the NIR model for 1000 - grain weight of paddy. The 7:3 was the optimal ratio of the calibration set to the validation set for the development of NIR model. The NIR model developed based on the calibration set which was selected with K - S algorithm had better predictive ability for 1000 - grain weight of paddy than that developed based on the calibration sets which were selected with the gradient and the random methods.关键词
近红外光谱/样本集/定标集/NIR模型/稻谷分类
农业科技引用本文复制引用
党文新,卢晓宇,龚红菊..样本集选择对稻谷千粒重NIR模型预测精度的影响[J].江西农业学报,2011,23(5):22-24,3.基金项目
江苏省农机局基金项目资助(gxz09007). （gxz09007）
