基于小波变换的循环伏安弱信号特征信息提取  

Method for Extracting Weak Cyclic Voltammetric SignalCharacteristic Information Based on Wavelet Transformation

作　　者：张水锋[1] 王立世[1] 张永清[1] 费新平[1] 党志[1] 莫金垣[2] 

机构地区：[1]华南理工大学环境科学与工程学院,广州510640 [2]中山大学化学与化学工程学院,广州510275

出　　处：《分析化学》2005年第4期487-490,共4页Chinese Journal of Analytical Chemistry

基　　金：国家自然科学基金(No. 20005003; 20475018);华南理工大学高水平大学建设苗子项目基金资助

摘　　要：本研究采用连续小波变换(continuouswavelettransform,CWT)技术处理循环伏安弱信号,通过研究苯酚、对苯二酚和对硝基苯酚的共存体系循环伏安弱信号,表明CWT可以成功地识别出极弱信号中的指纹特征信息,并且经处理得到的对应的小波系数峰比原始信号更窄,更高,由此可以成功地确定弱峰的数目和位置。由循环伏安信号所得的结果,有力地证实CWT是循环伏安弱信号识别的有力工具,对于理解弱响应体系电化学机理具有重要作用。In this paper, we paid our attention to process the weak cyclic voltammetric signals of a complex system adopting the skill of continuous wavelet transform ( CWT). By studying the weak cyclic voltammetric signals of the concomitant system of three hydroxybenzenes, namely phenol, hydroquinone and nitrophenol, it was shown that this skill could successfully distinguish the fingerprint-characteristic information from the very weak signal, and it was found also that the processed corresponding wavelet coefficient peaks were narrower and higher than the original signals, which in turn, helped to successfully identify both the number and location of the weak peaks. The results obtained from the weak cyclic voltammetric signals strongly confirmed that proposed CWT was a powerful tool of identifying processing and could greatly help to understand the weakly-responded system's electrochemical mechanism.

关 键 词：循环伏安 弱信号 特征信息提取 连续小波变换 对硝基苯酚 电化学机理 技术处理 共存体系 对苯二酚 小波系数 信号识别 CWT 

分 类 号：O657.1[理学—分析化学]