Using EN-NlR with Support Vector Machine for C1assification of Producing Year of Tobacco

ZHANG Hao-bo; LIU Tai-ang; SHU Ru-xin; YANG Kai; YE Shun; YOU Jing-lin; GE Jiong

doi:10.3964/j.issn.1000-0593(2018)05-1620-06

Journals >Spectroscopy and Spectral Analysis >Volume 38 >Issue 5 >Page 1620 > Article

Spectroscopy and Spectral Analysis
Vol. 38, Issue 5, 1620 (2018)

Using EN-NlR with Support Vector Machine for C1assification of Producing Year of Tobacco

ZHANG Hao-bo^1、*, LIU Tai-ang², SHU Ru-xin¹, YANG Kai¹, YE Shun¹, YOU Jing-lin², and GE Jiong¹

Author Affiliations

¹[in Chinese]

²[in Chinese]

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DOI: 10.3964/j.issn.1000-0593(2018)05-1620-06 Cite this Article

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 Copy Citation Text

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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%.

Keywords

Electronic nose Near infrared spectroscopy Support vector machine Tobacco

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