中国光学（中英文）2025，Vol.18Issue(4)：794-802,9.DOI:10.37188/CO.2024-0230
基于多尺度小波变换的光谱数据预处理算法
A spectrum signal pre-processing algorithm based on multi-scale wavelet transform
摘要
Abstract
Spectral technology can extract useful characteristic information from a large number of raw sig-nals,which can be directly utilized for analyzing and identitying the material components of the observed samples.It has high application value in fields such as biomedicine,food safety and military reconnaissance.Due to the varying objectives and effects of the pretreatment,there are currently multiple spectral pre-pro-cessing methods available.We propose a spectrum signal pre-processing algorithm based on multi-scale wavelet transform,and the performance of the proposed algorithm and the designed softwere are evaluated through tests using both simulated and experimental spectra.The signal-to-noise ratio(SNR)of the simu-lated signal is 0.5 dB.After processing with the algorithm proposed in this paper,the SNR can reach to 8.978 dB.In the simulation,five different types of baselines are introduced,including linear,Gaussian,poly-nomial,exponential,and sigmoidal function types.Baseline estimation is performed using the algorithm pro-posed in this paper.The root mean square errors(RMSE)of the estimated values are 0.3759,0.2883,0.663 1,0.3489,0.4520,respectively.The spectrum of Polytetrafluoroethylene was measured using a con-focal micro-Raman spectrometer and preprocessed with the algorithm proposed in this paper.The results demonstrate that the algorithm is capable of fast and accurate processing of the spectra.The algorithm can be used to reduce noise and correct baseline.This study put on a set of new ideas on spectrum signal processing.关键词
光谱探测/光谱信号处理/小波变换/去噪声/基线校正Key words
spectral detecion/spectrum signal processing/wavelet transformation/de-noising/baseline cor-rection分类
通用工业技术引用本文复制引用
钱方,许永博,赵伟..基于多尺度小波变换的光谱数据预处理算法[J].中国光学（中英文）,2025,18(4):794-802,9.基金项目
吉林省自然科学基金(No.20210203174SF)Supported by Natural Science Foundation of Jilin Province(No.20210203174SF) （No.20210203174SF）
