农机化研究Issue(7)：56-60,5.
基于高光谱的油茶籽内部品质检测最优预测模型研究
Research on Optimal Predicting Model for the Detection of Internal Quality by Hyperspectral Technology
摘要
Abstract
To construct optimal predicting models based on the content of aliphatic acid of camellia seed, the research was concerned with oleic acid, linoleic acid, and palmitic acid.This approach was composed of four major procedures:line-by-line reflection spectrum scanning to select the region of interest ( ROI) by hyperspectral imaging system;smoot-hing the original spectrum and analyzing by multiplicative scatter correction ( MSC ); identifying the sensitive optimized waveband, which can reflect the variation of the content of oleic acid, linoleic acid, palmitic acid by correlation analysis and stepwise regression analysis;and then to build the optional waveband model by using partial least squares regression ( PLS) , principle component regression ( PCR) and radial basis function( RBF) neural network.Based on the external authentication, the RBF can achieve the best effects:the cross-validation correlation coefficient ( R) of oleic acid, lino-leic acid, and palmitic acid are 0.9403, 0.8935 and 0.9122;the correction error of root mean square are 0.441, 0. 1749 and 0.0664;the prediction error of root mean square are 0.3518, 0.184 and 0.162, respectively.关键词
高光谱/油茶籽/脂肪酸/预测模型Key words
hyper spectral/camellia/fatty acid/predicting model分类
农业科技引用本文复制引用
蒋蘋,罗亚辉,胡文武,廖敦军..基于高光谱的油茶籽内部品质检测最优预测模型研究[J].农机化研究,2015,(7):56-60,5.基金项目
湖南省科技厅科技计划项目 （）
