Related Experiment Video
Updated: Dec 1, 2025

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data
Alexander A Aksenov1,2, Ivan Laponogov3, Zheng Zhang1
1Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA.
Abstract:
We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.
More Related Videos
Related Concept Videos
Gas Chromatography–Mass Spectrometry (GC–MS)
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall....
Mass Spectrometry: Complex Analysis
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Gas Chromatography: Introduction
In GC, a sample is vaporized and mixed with an inert carrier gas (the mobile phase), which transports it through a...
MALDI-TOF Mass Spectrometry
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
Gas Chromatography: Overview of Detectors
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...
Mass Spectrometry: Overview

