Optimized adaptive Savitzky-Golay filtering algorithm based on deep learning network for absorption spectroscopy.

Journal: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Published Date: Jul 16, 2021

Abstract

An improved Savitzky-Golay (S-G) filtering algorithm was developed to denoise the absorption spectroscopy of nitrogen oxide (NO). A deep learning (DL) network was introduced to the traditional S-G filtering algorithm to adjust the window size and polynomial order in real time. The self-adjusting and follow-up actions of DL network can effectively solve the blindness of selecting the input filter parameters in digital signal processing. The developed adaptive S-G filter algorithm is compared with the multi-signal averaging filtering (MAF) algorithm to demonstrate its performance. The optimized S-G filtering algorithm is used to detect NO in a mid-quantum-cascade-laser (QCL) based gas sensor system. A sensitivity enhancement factor of 5 is obtained, indicating that the newly developed algorithm can generate a high-quality gas absorption spectrum for applications such as atmospheric environmental monitoring and exhaled breath detection.

Authors

  • Guosheng Zhang
    Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, 230601 Hefei, China; Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China.
  • He Hao
    Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, 230601 Hefei, China; Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China.
  • Yichen Wang
    Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, 230601 Hefei, China; Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China.
  • Ying Jiang
    Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, 230601 Hefei, China; Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China.
  • Jinhui Shi
    School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, China shijinhui10@gmail.com.
  • Jing Yu
    Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Xiaojuan Cui
    Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, 230601 Hefei, China; Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China.
  • Jingsong Li
    Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
  • Sheng Zhou
    Department of The First Clinical Medical College of Gansu, University of Chinese Medicine, Lanzhou, Gansu, China.
  • Benli Yu
    Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China; Laser Spectroscopy and Sensing Laboratory, Anhui University, 230601 Hefei, China.

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