基于GA-BP神经网络的茶叶蔗糖量检测模型研究

Research on Detecting Model of Sucrose Content in Tea Based on GA-BP Neural Network

摘要:

采用近红外光谱技术结合反向传播人工神经网络算法建立了茶叶中蔗糖含量的检测模型,并通过引入遗传算法改进了模型预测质量。预测模型采用120个茶叶掺蔗糖样品的傅里叶变换漫反射光谱数据建立。对另外42个样品的预测结果表明,基于传统的反向传播人工神经网络算法模型的相关系数为0.7380,预测均方根误差为3.0754,正确识别率为83.3%；增加遗传算法后相关系数提高到0.9419, 预测均方根误差为1.3176,正确率为88.1%,训练误差减小一个量级以上。实验结果表明,反向传播人工神经网络模型可用来检测茶叶中的蔗糖含量,同时,引入遗传算法优化了神经网络的初始权值和阈值,使预测误差更小。

Abstract:

The detection model of sucrose content in tea was established by using near-infrared spectroscopy technology and a back propagation neural network algorithm. The quality of model prediction is improved by introducing a genetic algorithm. The prediction model was established by using Fourier transform diffuse reflectance spectroscopy data of 120 tea samples mixed with sucrose. The prediction results of another 42 samples show that the correlation coefficient based on the traditional back-propagation neural network algorithm model is 0.7380, the root mean square error of prediction is 3.0754, and the prediction accuracy is 83.3%. The correlation coefficient increases to 0.9419 after the introduction of the genetic algorithm. The root mean square error of prediction is 1.3176, and the prediction accuracy is 88.1%, thus the training error is reduced by more than one order of magnitude. The experimental results show that the back-propagation neural network model can be used to detect the sucrose content in tea. Simultaneously, the introduction of the genetic algorithm can optimize the initial weights and thresholds of the neural network, so as to diminish the prediction error.