Wavelet transform and its applications in high performance liquid chromatography (HPLC) analysis

Wavelet transform has been proven to be a high performance signal processing technique. In this paper, a novel algorithm which is more suitable to process analytical signals is proposed, and application of the algorithm in denoising, baseline correction, determination of component number in overlapping chromatograms, and preprocessing the data matrix for window factor analysis (WFA) in resolving multi-component overlapping chromatograms is reported. The main characteristic of the wavelet transform is that it decomposes a signal into localized contributions, and each of the contributions represents the information of different frequency contained in the original signal. Therefore, the noise can be filtered by separating the high frequency contributions from the chromatogram, the baseline correction can be obtained by removing the low frequency contributions from the chromatogram, and the resolved chromatogram can be retrieved from the overlapping signal by choosing a discrete detail at certain scales from the decomposed contributions. The determination of component number in overlapping chromatograms can be obtained by simply counting the number of peaks in the resolved chromatogram.