基于WPT-IAF-ELM的小麦储存年限电子舌检测分析

Detection and Analysis of Wheat Storage Year Using Electronic Tongue Based on WPT-IAF-ELM

为实现对不同储存年限的陈化小麦进行快速、客观的评价分析,提出基于小波包变换(WPT)、改进鱼群算法(IAF)和极限学习机的小麦储存年限电子舌检测模型(WPT-IAF-ELM)。针对电子舌输出信号复杂、数据量大的特点,采用小波包变换对原始数据进行特征值提取,以降低数据维度,缩减数据规模。在此基础上,采用改进鱼群算法优化极限学习机参数,建立小麦储存年限检测分析模型。应用该模型对5个储存年限的陈化小麦进行定性分析,结果表明:该模型具有较好的分类效果,与分别用遗传算法和粒子群优化ELM算法相比,WPT-IAF-ELM的分类效果更优,其训练集正确率、测试集正确率、总体分类精度和Kappa系数分别为96%、92%、95%和0.91,表明提出的组合模型具有较好的分类效果。

The electronic tongue system based on virtual instrument technology was used to qualitatively analyze the aged wheat with four storage years to achieve rapid and objective evaluation and analysis of aged wheat with different storage years.In view of the complex output signal of the electronic tongue and the large amount of data,the wavelet packet transform was used to extract the eigenvalues of the original data to reduce the data dimension and reduce the data size.On this basis,the improved fish swarm algorithm was used to optimize the parameters of the extreme learning machine,and the analysis model of wheat storage age was established.The model was used to qualitatively analyze the aged wheat with five storage years.The experimental results showed that the model had better classification effect and the classification of WPT-IAF-ELM was compared with genetic algorithm and particle swarm optimization ELM algorithm respectively.The effect was better,and the training set correct rate,test set correct rate,overall classification accuracy and Kappa coefficient were respectively 96%,92%,95% and 0.91,which indicated that the proposed combined model had better classification effect for food.