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| Study on One-Dimensional Convolutional Neural Network Model Based on Near-Infrared Spectroscopy Data |
| TANG Jie1, LUO Yan-bo2, LI Xiang-yu2, CHEN Yun-can1, WANG Peng1, LU Tian3, JI Xiao-bo4, PANG Yong-qiang2*, ZHU Li-jun1* |
1. Chongqing Key Laboratory of Scientific Utilization of Tobacco Resources, Chongqing 400060, China
2. China National Tobacco Quality Supervision & Test Center, Zhengzhou 450001, China
3. Shanghai Shuzhiwei Information Technology Co., Ltd., Shanghai 200444, China
4. Department of Chemistry of Shanghai University, Shanghai 200444, China
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Abstract Near-infrared spectroscopy technology has been widely applied for detection in various industries. However, traditional methods struggle to gather key information from the spectral data, leading to significant model prediction errors. This study explores the regression modeling of one-dimensional convolutional neural networks (1DCNN) on near-infrared data, focusing on the chemical composition of 452 plants from the Solanaceae family. Through parameter optimization, the study suggests that the optimal settings for the model include 64 channels in the intermediate convolutional layer, a maximum pooling layer of 1, 6 convolutional layers, and 5 channels in the final convolutional layer. These findings can serve as a reference for future model research. The root mean square error of the model's test set ranges from 0.02 to 0.49, with an average relative error of 0.8%～1.7%, significantly lower than previous literature. Compared to traditional methods, 1DCNN can fully utilize all of the near-infrared spectral data while maintaining a simple model structure and strong predictive capabilities. This work provides new insights for data processing in near-infrared spectroscopy research and promotes the application and development of this technology.
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Received: 2023-05-29
Accepted: 2023-08-04
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Corresponding Authors:
PANG Yong-qiang, ZHU Li-jun
E-mail: pangyq2726@163.com；zhulj7802@163.com
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