CWBLS network and its application in portable spectral measurement.

Journal: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Published Date: May 3, 2025

Abstract

The research presents a novel approach called the D-CWBLS network to address the challenges of poor accuracy and stability in regression models caused by low-signal-to-noise-ratio and low reproducibility data in portable near-infrared spectroscopy. The D-CWBLS network improves upon the BLS network in three key aspects. Firstly, it expands the network structure by incorporating Near-Infrared characteristic spectral band data, thereby emphasizing important information and enhancing accuracy. Secondly, it deepens the network by adding a Dropout layer vertically, optimizing the structure, eliminating redundant information, and improving robustness. Lastly, it combines optimized feature node weight matrices and enhanced node weight matrices to eliminate uncertainty resulting from randomness during network training, subsequently improving robustness. In tests examining model reproducibility, accuracy, and robustness, the D-CWBLS model demonstrated superior performance compared to traditional machine learning models (PLSR, BP-ANN, and ELM), as well as deep learning models (MLP, CNN, and RNN), and even basic BLS and CWBLS models. This highlights the significant progress made by the D-CWBLS model in addressing the challenges associated with using portable near-infrared spectroscopy devices in outdoor settings, exhibiting higher reliability and applicability.

Authors

  • Yutong Sui
    Heilongjiang Bayi Agricultural University, College of Information and Electrical Engineering, Daqing, Heilongjiang 163319, China.
  • Xiaoyu Zhao
    Department of Science and Technology, Hebei Agricultural University, Huanghua, China.
  • Cheng Liu
    Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Anhui Province Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei 230026, China. Electronic address: chliu81@ustc.edu.cn.
  • Yue Zhao
    The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China.
  • Lijing Cai
    Heilongjiang Bayi Agricultural University, College of Information and Electrical Engineering, Daqing, Heilongjiang 163319, China.
  • Yuchen Tong
    Qiqihar University, Institute of Life Science and Agriculture and Forestry, Qiqihar 161006, China.

Keywords

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