Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using Self-Organizing Maps and a Universal Data Matrix-Reference-Cited by-同舟云学术

Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using Self-Organizing Maps and a Universal Data Matrix

Published:2018-09-27 Issue:21 Volume:90 Page:12475-12484
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Madiona Robert M. T.¹²^ORCID,Bamford Sarah E.¹,Winkler David A.³²⁴⁵^ORCID,Muir Benjamin W.²,Pigram Paul J.¹^ORCID

Affiliation:

1. Centre for Materials and Surface Science and Department of Chemistry and Physics, School of Molecular Sciences, La Trobe University, Melbourne, VIC 3086, Australia

2. CSIRO Manufacturing, Clayton, VIC 3168, Australia

3. La Trobe Institute for Molecular Sciences, School of Molecular Sciences, La Trobe University, Melbourne, VIC 3086, Australia

4. Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia

5. School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, U.K.

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.8b01951

Reference28 articles.

1. Secondary Ion Mass Spectrometry: Characterizing Complex Samples in Two and Three Dimensions

2. The Probability of Chance Correlation Using Partial Least Squares (PLS)

3. Non-negative matrix factorisation of large mass spectrometry datasets

4. Multivariate analysis of extremely large ToFSIMS imaging datasets by a rapid PCA method

5. Rapid multivariate analysis of 3D ToF-SIMS data: graphical processor units (GPUs) and low-discrepancy subsampling for large-scale principal component analysis

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