基于SPA-RBF神经网络的小麦蛋白质含量无损检测

Non-destructive Quality Analysis of Wheat Protein Based on SPA-RBF Neural Network

传统半微量凯氏法测量小麦蛋白质含量繁琐费时,应用近红外光谱分析技术结合SPA-RBF神经网络对小麦蛋白质含量进行快速、无损检测。采用SPXY算法划分校正集和预测集样本,运用连续投影算法(SPA)对一阶微分和SNV预处理后的光谱数据提取敏感波点作为RBF神经网络的输入,建立小麦蛋白质含量的SPA-RBF神经网络校正模型。模型的预测均方根误差和预测相关系数可达到0.26576和0.975,预测效果较好,基本上可以完成粮食储备和食品加工行业对小麦及其制品品质的划分以及育种上的前期世代筛选。研究表明:近红外光谱技术结合SPA-RBF神经网络可实现对小麦蛋白质含量的检测,满足现代农业发展对小麦无损、实时、大量检测的需要。

The traditional detection method of the protein content of wheat was tedious and time-consuming. NIRS （Near Infrared Reflectance Spectroscopy） and SPA-RBF artificial neural network were used to non-destructively measure the protein content of wheat in this paper. A representative set of correction samples was selected by SPXY algorithm, and then the spectral data was pretreated with first derivative and SNV methods to enhance spectral features, on the basis of which, making use of SPA to extract sensitive wave points which are used to establish SPA-RBF neural network model of wheat grain protein. Root-Mean-Square Error of Prediction （RMSEP） and prediction correlation coefficient （R） were 0.26576 and 0.975 respectively, which could basically complete the division that was used in grain reserves and food processing profession and breeding preliminary generation. The study showed that： NIRS combing with SPA-RBF neural network could achieve the detection of the protein content of wheat, which could satisfy the need of non-destructive and real-time detection of wheat to meet the development of modern agriculture.