光谱学与光谱分析, 2018, 38 (5): 1620, 网络出版: 2018-06-01
基于烟叶电子鼻-近红外数据融合的支持向量机分类判别烟叶年份
Using EN-NlR with Support Vector Machine for C1assification of Producing Year of Tobacco
摘要
提出了一种基于烟叶近红外光谱(NIR)和烟叶电子鼻(EN)融合数据的烟叶年份判别的支持向量机分类模型。 在NIR和EN数据融合的基础上, 利用遗传算法来进行变量选择, 再建立烟叶年份判别支持向量机模型, 所得到的烟叶判别模型在训练集和独立测试集上都具有较高的准确率。 建立的烟叶判别NIR-E-SVM模型的建模准确率达到100%, 留一法准确率达到9855%, 对未知样本的预报准确率为90%。
Abstract
Here we proposed a new simulation model constructed by support vector machine based on near infrared spectroscopy(NIR)and electronic nose (EN) data in order to predict tobacco year. After combining the data of NIR and EN, a genetic algorithm was used to analyze and pick the relevant variants to decrease variants in the calculation. The proposed model shows a high accuracy in both the training set and the independent test set. The NIR-EN-SVM model reached the accuracy of 100% and LOOCV’s accuracy reached 9855%. The accuracy of NIR-EN-SVM model to unknown samples is 9000%.
张浩博, 刘太昂, 束茹欣, 杨凯, 叶顺, 尤静林, 葛炯. 基于烟叶电子鼻-近红外数据融合的支持向量机分类判别烟叶年份[J]. 光谱学与光谱分析, 2018, 38(5): 1620. ZHANG Hao-bo, LIU Tai-ang, SHU Ru-xin, YANG Kai, YE Shun, YOU Jing-lin, GE Jiong. Using EN-NlR with Support Vector Machine for C1assification of Producing Year of Tobacco[J]. Spectroscopy and Spectral Analysis, 2018, 38(5): 1620.