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| Using EN-NIR with Support Vector Machine for Classification of Producing Year of Tobacco |
| ZHANG Hao-bo1, LIU Tai-ang2, SHU Ru-xin1, YANG Kai1, YE Shun1, YOU Jing-lin2, GE Jiong1* |
1. Technology Center of Shanghai Tobacco Group Co., Ltd., Shanghai 200082, China
2. State Key Laboratory of Advanced Special Steel & Shanghai Key Laboratory of Advanced Ferro Metallurgy & School of Materials Science and Engineering, Shanghai University, Shanghai 200072, China |
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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 98.55%. The accuracy of NIR-EN-SVM model to unknown samples is 90.00%.
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Received: 2017-02-17
Accepted: 2017-07-28
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Corresponding Authors:
GE Jiong
E-mail: gej@sh.tobacco.com.cn
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