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简述了小波变换的基本理论及其在分析化学中的应用。小波变换可同时提供时域与频域的信息,它把一个信号分解为不同尺度和位置的分量,每一分量代表原信号不同频率成分的信息。由于具有诸如正交性、方向选择性及可变的时频分辨率等特性,小波变换已被成功地用于FIA、HPLC、UV、IR、NIR、NMR、MS、电分析化学及其它化学计量学方法如模式识别及人工神经网络等领域之中。小波变换及小波包变换被用来压缩分析数据、去噪音、检出信号峰、扣除色谱基线等。在处理分析化学信息方面,它具有过程简单、耗时短、分辨率高、检测限低、数据存储量小、结果准确度高、重现性好等优点,在化学计量学方面的应用倍受人们关注。
The basic theory of wavelet transform and its application in analytical chemistry are briefly described. Wavelet transform can provide both time domain and frequency domain information. It decomposes a signal into components of different scales and locations. Each component represents the information of different frequency components of the original signal. Wavelet transforms have been successfully used for FIA, HPLC, UV, IR, NIR, NMR, MS, electroanalytical chemistry, and other stoichiometry due to such properties as orthogonality, direction selectivity and variable time-frequency resolution Learning methods such as pattern recognition and artificial neural networks and other fields. Wavelet transform and wavelet packet transform are used to compress the analysis data, remove noise, detect the signal peak, deduct the chromatogram baseline and so on. It has the advantages of simple process, short time-consuming, high resolution, low detection limit, small data storage, high accuracy and good reproducibility in the processing of analytical chemistry information, and its application in stoichiometry People are concerned.