基于多尺度小波变换的光谱数据预处理算法

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关键词：光谱探测;光谱信号处理；小波变换;去噪声；基线校正中图分类号：Tb96 文献标志码：A doi：10.37188/CO.2024-0230 CSTR：32171.14.CO.2024-0230

Abstract： Spectral technology can extract useful characteristic information from a large number of raw signals， 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-processing 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 simulated signal is 0.5dB . 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， polynomial，exponential， and sigmoidal function types. Baseline estimation is performed using the algorithm proposed in this paper. The root mean square errors （RMSE） of the estimated values are 0.3759，0.2883， 0.6631，0.3489， 0.4520，respectively. The spectrum of Polytetrafluoroethylene was measured using a confocal 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.

Key words： spectral detecion; spectrum signal processing; wavelet transformation; de-noising; baseline correction

1引言

20世纪80年代初，光谱探测技术逐渐兴起，90年代后形成研发热潮，由于其兼有探测和光谱分析的优点，已经广泛应用于生物医药、食品安全、军事目标侦察等方面。（剩余11326字）