伏安法中离散数据的傅里叶最小二乘处理方法

Discrete Data Processing Method Based on Fourier Least Square in Voltammetry

提出了傅里叶（Ｆｏｕｒｉｅｒ）最小二乘法对含噪声伏安数据的处理，并讨论其原理。通过对理论模拟数据和甲醛实验体系中高噪声溶出伏安数据的处理，证明了Ｆｏｕｒｉｅｒ 最小二乘法的可行性。

Fourier least square method (FLSM) was introduced into voltammetry to process discrete data containing noise. The principle was described in detail. Stripping voltammetric analysis, traditionally regarded as the most sensitive electrochemical techniques, usually suffers from poor signal-to-noise ratio(S/N). The method proposed was assessed by different simulated voltammetric techniques. Then it was utilized to process experimental data obtained from determination of formaldehyde by differential pulse stripping voltammetry. Although the curve has poor resolution, after FLSM peak height as the measuring parameter gave a good linear relationship. It was also found that FISM provided a powerful and accessible tool for discriminating against noise even in case where the S/N ratio were very unfavorable.