近红外和电子鼻数据融合识别不同香型风格

分别基于近红外和电子鼻融合数据、近红外数据以及电子鼻数据建立判别烟叶清香型、中间香型和浓香型三种香型风格的定性判别模型,结果表明虽然三种模型的建模准确率差异不大,都超过了89.00%,但基于融合数据建立的模型对中间香型和浓香型的预报准确率分别为82.67%和80.00%,比仅仅利用近红外数据建立模型的72.41%和73.33,也比仅仅基于电子鼻数据建立模型的68.97%和53.33%都有明显的提高。融合后预报准确率提高的可能原因是：电子鼻风味分析仪对于影响中间香型和浓香型的烟叶致香成分感应更加灵敏,捕获的信息也更多,这些新的信息可以作为NIR数据信息的有利补充,可用于建立烟叶香型分类判别准确率更高的模型。同时本研究还基于相同的融合数据,对比不同数据挖掘算法建模和预报结果差异性。实验结果表明：人工神经网络的建模结果高于支持向量机建模,人工神经网络模型的预报结果准确率只有65.00%,远低于支持向量机模型的预报结果的83.75%。这也验证了支持向量机算法可以在建模过程中减少过拟合。该研究可以为快速鉴别烟叶香型风格提供支撑,而且随着研究的深入可以争取为烟草系统的专业评吸人员提供辅助的鉴别方法。

Discriminating Flavor Styles via Data Fusion of NIR and EN

In this study, a qualitative discrimination model was established based on the combined technology of near-infrared (NIR) and electronic nose (EN) to distinguish the light, intermediate and strong flavors of tobacco leaves. The results showed little difference in the accuracy of the three models, all of which were more than 89.00%. However, the prediction accuracy of the combined model for intermediate flavor and strong flavor was 82.67% and 80.00%, respectively, which were significantly higher than those by NIR (72.41% and 73.33%) and EN (68.97% and 53.33%). The reason may be that EN was more sensitive to aroma components affecting intermediate flavor and strong flavor, and captured more information. The new information as a beneficial supplement to NIR data and can be used to establish a model with higher accuracy for tobacco flavor classification. In addition, based on the same fusion data, this study compared the modeling and prediction accuracy of different data mining algorithms. The results showed that the modeling accuracy of the artificial neural network (99.07%) was higher than that of the support vector machine (96.26%). However, the prediction accuracy of the artificial neural network (65.00%) was significantly lower than that of the support vector machine (83.75%), which verified that the support vector machine could reduce overfitting in the modeling process. This study can support the rapid identification of tobacco flavor style, and the further development of this technology will strive to provide an auxiliary identification method for professional tobacco evaluators.