title: A large scale test dataset to determine optimal retention index threshold based on three mass spectral similarity measures.
abstract: Retention index (RI) is useful for metabolite identification. However, when RI is integrated with mass spectral similarity for metabolite identification, many controversial RI threshold setup are reported in literatures. In this study, a large scale test dataset of 5844 compounds with both mass spectra and RI information were created from National Institute of Standards and Technology (NIST) repetitive mass spectra (MS) and RI library. Three MS similarity measures: NIST composite measure, the real part of Discrete Fourier Transform (DFT.R) and the detail of Discrete Wavelet Transform (DWT.D) were used to investigate the accuracy of compound identification using the test dataset. To imitate real identification experiments, NIST MS main library was employed as reference library and the test dataset was used as search data. Our study shows that the optimal RI thresholds are 22, 15, and 15 i.u. for the NIST composite, DFT.R and DWT.D measures, respectively, when the RI and mass spectral similarity are integrated for compound identification. Compared to the mass spectrum matching, using both RI and mass spectral matching can improve the identification accuracy by 1.7%, 3.5%, and 3.5% for the three mass spectral similarity measures, respectively. It is concluded that the improvement of RI matching for compound identification heavily depends on the method of MS spectral similarity measure and the accuracy of RI data.
authors: School of Electronic Engineering & Automation, Anhui University, Hefei, Anhui 230601, China.
authors: Jun
authors: J
authors: Zhang
authors: Department of Chemistry, University of Louisville, Louisville, KY 40292, USA; Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA.
authors: Imhoi
authors: I
authors: Koo
authors: School of Electrical Engineering & Information, Anhui University of Technology, Maanshan, Anhui 243002, China.
authors: Bing
authors: B
authors: Wang
authors: School of Electronic Engineering & Automation, Anhui University, Hefei, Anhui 230601, China.
authors: Qing-Wei
authors: QW
authors: Gao
authors: School of Electronic Engineering & Automation, Anhui University, Hefei, Anhui 230601, China. Electronic address: zhengch99@126.com.
authors: Chun-Hou
authors: CH
authors: Zheng
authors: Department of Chemistry, University of Louisville, Louisville, KY 40292, USA.
authors: Xiang
authors: X
authors: Zhang
mesh_terms: D000465:Algorithms; D000473:Alkanes / Q000737:chemistry; D016208:Databases, Factual; D005583:Fourier Analysis; D013058:Mass Spectrometry / Q000379:methods*; D008956:Models, Chemical
chemical_list: D000473:Alkanes