Dynamic multiple spectral similarity measures for compound identification

Abstract

Gas chromatography-mass spectrometry (GC-MS) is one of the most important and powerful tools to identify compounds in both chemical and biological samples. In this work, a novel compound identification method based on the dynamic multiple spectral similarity measures is proposed. The proposed method uses seven spectral similarity measures. To reduce the computational time, DFTR measure is used a filter layer in proposed method. 22457 mass spectra for 15793 unique compounds are used as query data and NIST05 main spectral library is used as reference library. The experimental results showed that the identification accuracy of the dynamic multiple similarity measures is increased 8.97% and 18.46% comparing with DFTR and Correlation measure, respectively.