Spectroscopy and Spectral Analysis, Volume. 38, Issue 5, 1620(2018)
Using EN-NlR with Support Vector Machine for C1assification of Producing Year of Tobacco
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%.
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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
Received: Feb. 17, 2017
Accepted: --
Published Online: Jun. 1, 2018
The Author Email: Hao-bo ZHANG (zhanghb@sh.tobacco.com.cn)