神经网络模型在LED便携式近红外整粒小麦成分测量仪上的建立

采用三层反向传播人工神经网络，在LED便携式近红外整粒小麦成分测量仪上，建立了测定整粒小麦中粗蛋白含量的定量分析模型。给出了LED便携式近红外整粒小麦成分测量仪的结构框图、光路结构图及所采集的整粒小麦样品光谱图。简介了人工神经网络的原理并给出了建模的结果。用人工神经网络建立模型，得到校准集和预测集的相关系数分别为0.90和0.96，相对标准偏差分别为3.77%和4.46%。在所使用的有限个分立波长的测定仪上，由于仪器光路、电路及整粒样品状态的影响，存在着一定的非线性。采用人工神经网络建立的模型，其结果优于线性模型的结果。

Building Artificial Neural Networks Model on Portable NIR Integrity Wheat Component Measuring Apparatus

The quantitative analysis model of protein in integrity wheat was built by three layers back propagation artificial neural networks for portable near infrared (NIR) integrity wheat component measuring apparatus. The structure diagram of integrity wheat component measuring apparatus, light route structure of apparatus and the spectrum of integrity wheat were given in the present paper. The theory of artificial neural network was briefly introduced and the results of quantitative analysis model of protein were given. For calibration set and prediction set, the correlation coefficient was 0.90 and 0.96 respectively; the relative standard deviation is 3.77% and 4.46% respectively. Because of the influence of light route structure, electrical circuit, and integrity sample forms on the measuring apparatus, some nonlinearity exists between the spectral parameters and chemical values. The results of artificial neural networks nonlinear model were superior to linear model.