A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopy
The content of nicotine, a critical component of tobacco, significantly influences the quality of tobacco leaves. Near-infrared (NIR) spectroscopy is a widely used technique for rapid, non-destructive, and environmentally friendly analysis of nicotine levels in tobacco. In this paper, we propose a n...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1138693/full |
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| author | Di Wang Fengyuan Zhao Rui Wang Junwei Guo Cihai Zhang Huimin Liu Yongsheng Wang Guohao Zong Le Zhao Weihua Feng |
| author_facet | Di Wang Fengyuan Zhao Rui Wang Junwei Guo Cihai Zhang Huimin Liu Yongsheng Wang Guohao Zong Le Zhao Weihua Feng |
| author_sort | Di Wang |
| collection | DOAJ |
| description | The content of nicotine, a critical component of tobacco, significantly influences the quality of tobacco leaves. Near-infrared (NIR) spectroscopy is a widely used technique for rapid, non-destructive, and environmentally friendly analysis of nicotine levels in tobacco. In this paper, we propose a novel regression model, Lightweight one-dimensional convolutional neural network (1D-CNN), for predicting nicotine content in tobacco leaves using one-dimensional (1D) NIR spectral data and a deep learning approach with convolutional neural network (CNN). This study employed Savitzky–Golay (SG) smoothing to preprocess NIR spectra and randomly generate representative training and test datasets. Batch normalization was used in network regularization to reduce overfitting and improve the generalization performance of the Lightweight 1D-CNN model under a limited training dataset. The network structure of this CNN model consists of four convolutional layers to extract high-level features from the input data. The output of these layers is then fed into a fully connected layer, which uses a linear activation function to output the predicted numerical value of nicotine. After the comparison of the performance of multiple regression models, including support vector regression (SVR), partial least squares regression (PLSR), 1D-CNN, and Lightweight 1D-CNN, under the preprocessing method of SG smoothing, we found that the Lightweight 1D-CNN regression model with batch normalization achieved root mean square error (RMSE) of 0.14, coefficient of determination (R2) of 0.95, and residual prediction deviation (RPD) of 5.09. These results demonstrate that the Lightweight 1D-CNN model is objective and robust and outperforms existing methods in terms of accuracy, which has the potential to significantly improve quality control processes in the tobacco industry by accurately and rapidly analyzing the nicotine content. |
| first_indexed | 2024-04-09T13:13:35Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-09T13:13:35Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-89106d515e9e4e419e5b6fe59e1656712023-05-12T05:54:18ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-05-011410.3389/fpls.2023.11386931138693A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopyDi Wang0Fengyuan Zhao1Rui Wang2Junwei Guo3Cihai Zhang4Huimin Liu5Yongsheng Wang6Guohao Zong7Le Zhao8Weihua Feng9Zhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, ChinaSchool of Mathematics and Statistics, Zhengzhou University, Zhengzhou, ChinaZhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, ChinaZhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, ChinaTechnology Center of China Tobacco Guizhou Industrial Co., Ltd., Guiyang, ChinaZhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, ChinaZhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, ChinaZhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, ChinaZhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, ChinaZhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, ChinaThe content of nicotine, a critical component of tobacco, significantly influences the quality of tobacco leaves. Near-infrared (NIR) spectroscopy is a widely used technique for rapid, non-destructive, and environmentally friendly analysis of nicotine levels in tobacco. In this paper, we propose a novel regression model, Lightweight one-dimensional convolutional neural network (1D-CNN), for predicting nicotine content in tobacco leaves using one-dimensional (1D) NIR spectral data and a deep learning approach with convolutional neural network (CNN). This study employed Savitzky–Golay (SG) smoothing to preprocess NIR spectra and randomly generate representative training and test datasets. Batch normalization was used in network regularization to reduce overfitting and improve the generalization performance of the Lightweight 1D-CNN model under a limited training dataset. The network structure of this CNN model consists of four convolutional layers to extract high-level features from the input data. The output of these layers is then fed into a fully connected layer, which uses a linear activation function to output the predicted numerical value of nicotine. After the comparison of the performance of multiple regression models, including support vector regression (SVR), partial least squares regression (PLSR), 1D-CNN, and Lightweight 1D-CNN, under the preprocessing method of SG smoothing, we found that the Lightweight 1D-CNN regression model with batch normalization achieved root mean square error (RMSE) of 0.14, coefficient of determination (R2) of 0.95, and residual prediction deviation (RPD) of 5.09. These results demonstrate that the Lightweight 1D-CNN model is objective and robust and outperforms existing methods in terms of accuracy, which has the potential to significantly improve quality control processes in the tobacco industry by accurately and rapidly analyzing the nicotine content.https://www.frontiersin.org/articles/10.3389/fpls.2023.1138693/fulltobacconear-infrared spectroscopynicotinelightweightconvolutional neural network |
| spellingShingle | Di Wang Fengyuan Zhao Rui Wang Junwei Guo Cihai Zhang Huimin Liu Yongsheng Wang Guohao Zong Le Zhao Weihua Feng A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopy Frontiers in Plant Science tobacco near-infrared spectroscopy nicotine lightweight convolutional neural network |
| title | A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopy |
| title_full | A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopy |
| title_fullStr | A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopy |
| title_full_unstemmed | A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopy |
| title_short | A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopy |
| title_sort | lightweight convolutional neural network for nicotine prediction in tobacco by near infrared spectroscopy |
| topic | tobacco near-infrared spectroscopy nicotine lightweight convolutional neural network |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1138693/full |
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