光谱信号的小波去噪新技术

光谱分析中，噪声的存在常影响分析的准确度和检测限。现有滤波方法在光谱信号除噪方面有种种缺陷。文章充分利用小波在信号处理方面的优良特性，提出了Mexican Hat小波滤波算法，它选用Mexican Hat小波函数构造滤波项，利用滤波项与原始信号作用，从而实现信号与噪音的分离。该方法无论对低频或高频信号均适用，除噪完全，即使对信噪比为1的高噪声信号也能获取满意的处理结果。运用这一技术处理光谱信号，简单快速、结果可靠，处理后峰位置、峰高、峰面积误差分别小于0.2%，3.2%，1.1%。大量实验表明，本方法能有效提高光谱分析的准确度。

A New De-Noising Technique for Spectra Based on Mexican Hat Wavelet

Signals in spectral analysis often have random noise, which has negative influence on the accuracy and detection limit of analysis. A new chemometrics method named Mexican Hat Wavelet De-noising Arithmetic (MWDA) is presented, which can be used to remove noise in analytical chemical signals. In this method, Mexican Hat wavelet is chosen to construct de-noising function because of its excellent properties, then the de-noising function is used to extract useful information from noisy signals. MWDA is effective for signals with either wide peaks or very sharp peaks. Many processing results of simulated and experimental signals indicate that MWDA is a simple and powerful de-noising method, even when the signal has very high noise (whose signal to noise ratio is 1). After processed, the relative errors of peak position, peak height and peak area are less than 0.2%, 3.2% and 1.1% respectively. When it is applied to experimental spectra, the results are also satisfactory. This new method can increase the accuracy of spectral analysis, and the result is credible and satisfying.