基于深度学习网络的吸收光谱优化自适应 Savitzky-Golay 滤波算法。

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

机构信息

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.

School of Physics and Electronics, Shandong Normal University, 250014, Jinan, China.

出版信息

Spectrochim Acta A Mol Biomol Spectrosc. 2021 Dec 15;263:120187. doi: 10.1016/j.saa.2021.120187. Epub 2021 Jul 16.

DOI:10.1016/j.saa.2021.120187

PMID:34314970

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.

摘要

提出了一种改进的 Savitzky-Golay（S-G）滤波算法，用于对氮氧化物（NO）的吸收光谱进行去噪。将深度学习（DL）网络引入传统的 S-G 滤波算法中，以实时调整窗口大小和多项式阶数。DL 网络的自适应和跟踪动作可以有效解决数字信号处理中输入滤波器参数选择的盲目性。将开发的自适应 S-G 滤波器算法与多信号平均滤波（MAF）算法进行比较，以证明其性能。将优化的 S-G 滤波算法用于基于中量子级联激光（QCL）的气体传感器系统中检测 NO。获得了 5 的灵敏度增强因子，表明新开发的算法可以为大气环境监测和呼气检测等应用生成高质量的气体吸收光谱。