An application of wavelet moments to the similarity analysis of three-dimensional fingerprint spectra obtained by high-performance liquid chromatography coupled with diode array detector.

More and more the three-dimensional (3D) fingerprint spectra, which can be obtained by high performance liquid chromatography coupled with diode array detector (HPLC-DAD), are applied to the analysis of drugs and foods. A novel approach to the similarity analysis of traditional Chinese medicines (TCMs) was proposed based on the digital image processing using 3D HPLC-DAD fingerprint spectra. As the one of shape features of digital grayscale image, wavelet moments were employed to extract the shape features from the grayscale images of 3D fingerprint spectra of different Coptis chinensis samples, and used to the similarity analysis of these samples. Compared with the results obtained by traditional features including principal components and spectrum data under single-wavelength, our results represented the more reliable assessment. This work indicates that the better features of fingerprint spectra are more important than similarity evaluation methods. Wavelet moments, which possess multi-resolution specialty and the invariance property in image processing, are more effective than traditional spectral features for the description of the systemic characterisation of mixture sample.