基于连续投影算法的小麦湿面筋近红外校正模型优化  

Near Infrared Spectroscopy Calibration Model Optimizing of Wet Gluten Based on Successive Projections Algorithm

作　　者：钱海波[1] 孙来军[1] 王乐凯[2] 徐璐璐[1] 戴常军[2] 

机构地区：[1]黑龙江省电子工程省高校重点实验室/黑龙江大学,哈尔滨150080 [2]农业部谷物及制品质量监督检验测试中心,哈尔滨150086

出　　处：《中国农学通报》2011年第18期51-56,共6页Chinese Agricultural Science Bulletin

基　　金：小麦现代农业产业技术体系建设专项基金资助项目(nycytx-03)

摘　　要：为减少建模过程中的计算量、提高模型的稳健性及预测精度,将连续投影算法用于小麦湿面筋近红外校正模型的建立。首先采用SPXY算法选择具有代表性的校正集样本,然后对光谱数据作不同预处理,增强光谱特征;运用连续投影算法对原始光谱和预处理后的光谱进行敏感波点提取,进而分别建立多元线性回归校正模型。测试结果表明,对光谱标准正态变量变换后利用连续投影算法提取敏感波点所建多元线性回归模型预测效果最好,预测均方根误差和预测相关系数分别为1.3332和0.94319,优于同等条件下建立的偏最小二乘回归模型。In order to reduce computational complexity of modeling and improve the model＇s robustness and prediction accuracy,successive projections algorithm （SPA） was used in the near infrared spectrum calibration modeling of wheat gluten.Firstly,a representative set of correction samples were selected by SPXY algorithm.Secondly,the spectral data was pretreated with several different methods to enhance spectral features.Thirdly,making use of SPA to extract sensitive wave points of the original spectrum and the spectrum after preprocessing and then multiple linear regression （MLR） calibration models were established.The results showed that the calibration model established with the data extracted from the spectrum after standard normal variate transformation （SNV） obtained the best results.The root mean square error of prediction （RMSEP） and the prediction correlation coefficient （r） were 1.3332 and 0.94319,respectively,which was better than the model established by partial least square regression （PLSR） under the same conditions.

关 键 词：小麦 湿面筋 近红外光谱 连续投影算法 波长选择 

分 类 号：S512[农业科学—作物学] S123