关键词: 仪器分析, 小波理论, 分析信号处理, 自适应滤波

Abstract: In this paper, a new type of adaptive filtering algorithm, which can adaptively remove all kinds of noises from signals of analyticaLINstruments under a variety of complex conditions, is proposed. At present, the popular filtering algorithms which are widely applied to the data processing equipment for analyticaLINstrument are lowpass filter or bandpass filter. The fundamental of those filters depends on the fact that the frequency characteristics of real signals are different from those of noises. These filtering algorithms based on the different frequency distribution characteristics between signals and noises have an obvious defect, that is, users have to preset properly initial filter factors according to the width of peaks, which greatly influences the objectivity and veracity of computational results in analytical procedures. In the light of the wavelet transform modulus maximum theory proposed by Mallat, the characteristics of wavelet transform modulus maxima of real signals are distinctively different from those of noises in the practical signals of analyticaLINstruments, such as chromatography. It is easy to identify them. Taking advantage of the different characteristics between real signals and noises on different scales in wavelet transformation domain, noises can be removed from the practical signals of analyticaLINstruments while avoiding to distort the real signals. The adaptive filtering algorithm designed by this principle breaches the popular patterns of current filtering algorithms, and radically improves the filtering effects. Alot of tests using chromatography data prove that this algorithm has a serial of virtues, such as no requirement on artificially presetting filter factors, excellent separation of signals and noises, holding the position and height of peaks, and so on. Its performance in the robustness, adaptability and fidelity of peak completely satisfy the needs of signal processing for analyticaLINstruments.

Key words: Instrument analysis, Wavelet theory, Analytical signal processing, Adaptive filtering