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（英文）  

Using EN-NIR with Support Vector Machine for Classification of Producing Year of Tobacco

作　　者：张浩博[1] 刘太昂[2] 束茹欣 杨凯[1] 叶顺 尤静林[2] 葛炯[1] ZHANG Hao-bo;LIU Tai-ang;SHU Ru-xin;YANG Kai;YE Shun;YOU Jing-lin;GE Jiong(Technology Center of Shanghai Tobacco Group Co.,Ltd.,Shanghai 200082,China;State Key Laboratory of Advanced Special Steel&Shanghai Key Laboratory of Advanced Ferro Metallurgy&School of Materials Science and Engineering,Shanghai University,Shanghai200072,China)

机构地区：[1]上海烟草集团有限责任公司技术中心,上海200082 [2]高品质特殊钢冶金与制备国家重点实验室,上海市钢铁冶金新技术开发应用重点实验室,上海大学材料科学与工程学院,上海200072

出　　处：《光谱学与光谱分析》2018年第5期1620-1625,共6页Spectroscopy and Spectral Analysis

基　　金：National Natural Science Foundation of China(21672141)

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-ESVM模型的建模准确率达到100%,留一法准确率达到98.55%,对未知样本的预报准确率为90%。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 setand the independent test set.The NIR-EN-SVM model reached the accuracy of 100%and LOOCV’s accuracy reached 98.55%.The accuracy of NIR-EN-SVM model tounknown samples is 90.00%.

关 键 词：近红外 支持向量机 烟草 电子鼻 

分 类 号：O657.3[理学—分析化学]