The method of noise peaks reduction in high resolution mass spectrum, focusing peak shape.

Abstract

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry has been applied to the analysis of synthetic polymers. MALDI with a high resolution time-of-flight mass spectrometer can be used to determine the identify differences in polymer terminal groups and their molecular weight distributions. Kendrick mass defect (KMD) analysis simplifies the data interpretation, even for complicated mass spectra, then shows an overview of the differences observed within a sample. Usually many noise peaks looking wide and distorted shape are often observed in the low mass region of MALDI mass spectrum. To see the polymer series clearly in the KMD plot, it is important to distinguish between true peaks and noise peaks and then to reduce these noise peaks accurately. We have achieved to classify these peaks by using machine learning. Therefore, we report about the reduction of noise peaks for high resolution mass spectrum by using machine learning technique.