Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks
The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a n...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Analytical Methods in Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2020/9652470 |
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| author | Lei Zhang Xiangqian Ding Ruichun Hou |
| author_facet | Lei Zhang Xiangqian Ding Ruichun Hou |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification. Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method. The classification is performed by the combination of global and local training features. Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. The multimodal NIR-CNN identification models of tobacco origin were established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries. The classification accuracy of 1-D CNN and 2-D CNN models was 93.15% and 93.05%, respectively, which was better than the traditional PLS-DA method. The experimental results show that the application of 1-D CNN and 2-D CNN can accurately and reliably distinguish the NIRS data, and it can be developed into a new rapid identification method of tobacco origin, which has an important promotion value. |
| format | Article |
| id | doaj-art-5e188224dc2d4de6b66f620caac309e4 |
| institution | OA Journals |
| issn | 2090-8865 2090-8873 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Analytical Methods in Chemistry |
| spelling | doaj-art-5e188224dc2d4de6b66f620caac309e42025-08-20T02:06:00ZengWileyJournal of Analytical Methods in Chemistry2090-88652090-88732020-01-01202010.1155/2020/96524709652470Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural NetworksLei Zhang0Xiangqian Ding1Ruichun Hou2College of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaCollege of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaCollege of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaThe origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification. Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method. The classification is performed by the combination of global and local training features. Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. The multimodal NIR-CNN identification models of tobacco origin were established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries. The classification accuracy of 1-D CNN and 2-D CNN models was 93.15% and 93.05%, respectively, which was better than the traditional PLS-DA method. The experimental results show that the application of 1-D CNN and 2-D CNN can accurately and reliably distinguish the NIRS data, and it can be developed into a new rapid identification method of tobacco origin, which has an important promotion value.http://dx.doi.org/10.1155/2020/9652470 |
| spellingShingle | Lei Zhang Xiangqian Ding Ruichun Hou Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks Journal of Analytical Methods in Chemistry |
| title | Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks |
| title_full | Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks |
| title_fullStr | Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks |
| title_full_unstemmed | Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks |
| title_short | Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks |
| title_sort | classification modeling method for near infrared spectroscopy of tobacco based on multimodal convolution neural networks |
| url | http://dx.doi.org/10.1155/2020/9652470 |
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