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Non-negative Matrix Factorisation of Large Mass Spectrometry Datasets

Trindade, GF, Abel, Marie-Laure and Watts, John (2017) Non-negative Matrix Factorisation of Large Mass Spectrometry Datasets Chemometrics and Intelligent Laboratory Systems, 163. pp. 76-85.

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Abstract

The development of state-of-art time-of-flight secondary ion mass spectrometry (ToF-SIMS) results in extremely large datasets. In order to perform multivariate analysis of such datasets without loss of mass and spatial resolution, appropriate data handling methods must be developed. The work in this paper presents an approach that can be taken to perform non-negative matrix factorisation (NMF) of large ToF-SIMS datasets. A large area stage raster scan of a chemically contaminated fingerprint is used as an example and the results show that the fingerprint signal was successfully separated from the substrate signal. Pre-processing challenges and artefacts that arises from the results are also discussed and an alternative approach, using the MapReduce programming model, is suggested for even larger datasets.

Item Type: Article
Subjects : Mechanical Engineering Science
Divisions : Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Authors :
NameEmailORCID
Trindade, GFUNSPECIFIEDUNSPECIFIED
Abel, Marie-LaureM.Abel@surrey.ac.ukUNSPECIFIED
Watts, JohnJ.Watts@surrey.ac.ukUNSPECIFIED
Date : 1 March 2017
Identification Number : 10.1016/j.chemolab.2017.02.012
Copyright Disclaimer : © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : time-of-flight secondary ion mass spectrometry; non-negative matrix factorization; multivariate analysis; hyperspectral imaging; fingerprints; MapReduce; large datasets; big data
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 14 Mar 2017 15:54
Last Modified : 19 Jul 2017 12:49
URI: http://epubs.surrey.ac.uk/id/eprint/813772

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