By analyzing the near-infrared spectrum, we can determine the quantitative relationship model between the spectral data of different cocoa beans and the target components. This paper proposes a predictive regression model based on 1D-CNN. Based on the traditional convolutional neural network, gating mechanisms and dilated convolutions are combined. The particle swarm optimization method is used to optimize the hyper-parameters of one-dimensional convolution. The end-to-end near-infrared predictive regression model does not require wavelength selection. It is convenient to use and has a strong promotional value. Taking the public cocoa beans near-infrared data set as an example, the method can predict the water and fat content in cocoa beans, and the effectiveness of the method is verified. Comparing the improved one-dimensional convolution with traditional one-dimensional convolution results and partial least squares regression, it shows better prediction accuracy and robustness.
|Title of host publication||Recent Featured Applications of Artificial Intelligence Methods. LSMS 2020 and ICSEE 2020 Workshops - Workshops of the 6th International Conference on Life System Modeling and Simulation, LSMS 2020, and 6th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2020, Proceedings|
|Editors||Minrui Fei, Kang Li, Zhile Yang, Qun Niu, Xin Li|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||13|
|Publication status||Published - 12 Jan 2020|
|Event||6th International Conference on Life System Modeling and Simulation, LSMS 2020, and 6th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2020 - Shanghai, China|
Duration: 25 Oct 2020 → 25 Oct 2020
|Name||Communications in Computer and Information Science|
|Conference||6th International Conference on Life System Modeling and Simulation, LSMS 2020, and 6th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2020|
|Period||25/10/2020 → 25/10/2020|
Bibliographical noteFunding Information:
This research is financially supported by Natural Science Foundation of China (61877065), the National Key Research and Development Program of China (No. 2019YFB1405500) and Key Project of Science and Technology Commission of Shanghai Municipality under Grant (No. 16010500300).
© 2020, Springer Nature Singapore Pte Ltd.
Copyright 2021 Elsevier B.V., All rights reserved.
- Convolutional neural network
- Dilated convolution
- Gating mechanisms
- Infrared spectroscopic data
ASJC Scopus subject areas
- Computer Science(all)