An entropy-based method for noise reduction of liquid chromatography-mass spectrometry data

Entropy-based methods have been extensively used to measure the uncertainty information in a variety of fields. In this article, a novel information theory-based method for reducing noise of liquid chromatography-mass spectrometry (LC/MS) data was developed. The uncertainty existed in the LC/MS chromatograms was captured and evaluated by information entropy. By comparing the information entropy computationally derived from mass chromatograms, the good quality chromatograms and the noisy chromatograms can be distinguished. The proposed method was applied in processing LC/MS data of “Jing-Zhi-Guan-Xin” troche which is a well-known preparation of traditional Chinese medicine (TCM). The obtained result indicated that this method is beneficial to reduce noise of LC/MS data of complicated chemical samples, such as TCM.