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 set and 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 to unknown samples is 9000%.