ADAP-GC 4.0: Application of Clustering-Assisted Multivariate Curve Resolution to Spectral Deconvolution of Gas Chromatography-Mass Spectrometry Metabolomics Data

• Type of Material: Article
• Authors: Aleksandr Smirnov, University of North Carolina; Yunping Qiu, Albert Einstein College of Medicine; Wei Jia, University of Hawaii; Douglas Walker, Emory University; Dean Jones, Emory University; Xiuxia Du, University of North Carolina
• Language: English
• Date: 2019-07-16
• Publisher: American Chemical Society
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2019 American Chemical Society.
• Final Published Version (URL): http://dx.doi.org/10.1021/acs.analchem.9b01424
• Title of Journal or Parent Work: Analytical Chemistry
• Volume: 91
• Issue: 14
• Start Page: 9069
• End Page: 9077
• Grant/Funding Information: This work was financially supported by the United States National Science Foundation grant award 1262416 and National Institutes of Health grant award U01CA235507.
• Keywords: QUANTIFICATION; Physical Sciences; Science & Technology; TOOL; Chemistry, Analytical; EXTRACTION; ALGORITHMS; METABOLITES; Chemistry; PEAK DETECTION; SEPARATION; GC/TOF-MS DATA; NONNEGATIVE MATRIX FACTORIZATION; COMPOUND IDENTIFICATION
• Research Categories: Chemistry, Analytical

Abstract

We report a multivariate curve resolution (MCR)-based spectral deconvolution workflow for untargeted gas chromatography-mass spectrometry metabolomics. As an essential step in preprocessing such data, spectral deconvolution computationally separates ions that are in the same mass spectrum but belong to coeluting compounds that are not resolved completely by chromatography. As a result of this computational separation, spectral deconvolution produces pure fragmentation mass spectra. Traditionally, spectral deconvolution has been achieved by using a model peak approach. We describe the fundamental differences between the model peak-based and the MCR-based spectral deconvolution and report ADAP-GC 4.0 that employs the latter approach while overcoming the associated computational complexity. ADAP-GC 4.0 has been evaluated using GC-TOF data sets from a 27-standards mixture at different dilutions and urine with the mixture spiked in, and GC Orbitrap data sets from mixtures of different standards. It produced the average matching scores 960, 959, and 926 respectively. Moreover, its performance has been compared against MS-DIAL, eRah, and ADAP-GC 3.2, and ADAP-GC 4.0 demonstrated a higher number of matched compounds and up to 6% increase of the average matching score.