A method for characterizing chromatographic fingerprint by integrating wavelet transform with fractal dimension

A method for characterizing chromatographic fingerprint by integrating wavelet transform with fractal dimension was proposed. The chromatographic profile is decomposed into the components of different resolution levels by wavelet transform, and the fractal dimensions of these components are then computed. As a result, chromatographic signal data are replaced by the wavelet-based fractal parameters for characterizing. chromatographic fingerprint. It has been substantiated by the simulation experiment that, for the disturbance caused by variation of retention times of peaks, the anti-disturbing performance of the wavelet-based fractal parameters is better than that of chromatographic signal data. A typical example of identifying the species and habitats of two herbs, i. e. Ligusticum Chuangxiong and Angelica Sinensis (Oliv.) Diels, was used for comparative analysis between the wavelet-based fractal parameters and chromatographic signal data. The cross-validation results of k Nearest Neighbor showed that the classification accuracy obtained with the wavelet-based fractal parameters was greater than that obtained with chromatographic signal data.