PMID- 30260219
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20191120
IS  - 1520-6882 (Electronic)
IS  - 0003-2700 (Linking)
VI  - 90
IP  - 21
DP  - 2018 Nov 6
TI  - Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using 
      Self-Organizing Maps and a Universal Data Matrix.
PG  - 12475-12484
LID - 10.1021/acs.analchem.8b01951 [doi]
AB  - Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is advancing rapidly, 
      providing instruments with growing capabilities and resolution. The data sets 
      generated by these instruments are likewise increasing dramatically in size and 
      complexity. Paradoxically, methods for efficient analysis of these large, rich 
      data sets have not improved at the same rate. Clearly, more effective 
      computational methods for analysis of ToF-SIMS data are becoming essential. 
      Several research groups are customizing standard multivariate analytical tools to 
      decrease computational demands, provide user-friendly interfaces, and simplify 
      identification of trends and features in large ToF-SIMS data sets. We previously 
      applied mass segmented peak lists to data from PMMA, PTFE, PET, and LDPE. 
      Self-organizing maps (SOMs), a type of artificial neural network (ANN), 
      classified the polymers based on their molecular composition and primary ion 
      probe type more effectively than simple PCA. The effectiveness of this approach 
      led us to question whether it would be useful in distinguishing polymers that 
      were very similar. How sensitive is the technique to changes in polymer chemical 
      structure and composition? To address this question, we generated ToF-SIMS ion 
      peak signatures for seven nylon polymers with similar chemistries and used our 
      up-binning and SOM approach to classify and cluster the polymers. The widely used 
      linear PCA method failed to separate the samples. Supervised and unsupervised 
      training of SOMs using positive or negative ion mass spectra resulted in 
      effective classification and separation of the seven nylon polymers. Our SOM 
      classification method has proven to be tolerant of minor sample irregularities, 
      sample-to-sample variations, and inherent data limitations including spectral 
      resolution and noise. We have demonstrated the potential of machine learning 
      methods to analyze ToF-SIMS data more effectively than traditional methods. Such 
      methods are critically important for future complex data analysis and provide a 
      pipeline for rapid classification and identification of features and similarities 
      in large data sets.
FAU - Madiona, Robert M T
AU  - Madiona RMT
AUID- ORCID: 0000-0002-3422-5756
AD  - Centre for Materials and Surface Science and Department of Chemistry and Physics, 
      School of Molecular Sciences , La Trobe University , Melbourne , VIC 3086 , 
      Australia.
AD  - CSIRO Manufacturing , Clayton , VIC 3168 , Australia.
FAU - Bamford, Sarah E
AU  - Bamford SE
AD  - Centre for Materials and Surface Science and Department of Chemistry and Physics, 
      School of Molecular Sciences , La Trobe University , Melbourne , VIC 3086 , 
      Australia.
FAU - Winkler, David A
AU  - Winkler DA
AUID- ORCID: 0000-0002-7301-6076
AD  - La Trobe Institute for Molecular Sciences, School of Molecular Sciences , La 
      Trobe University , Melbourne , VIC 3086 , Australia.
AD  - CSIRO Manufacturing , Clayton , VIC 3168 , Australia.
AD  - Monash Institute of Pharmaceutical Sciences , Monash University , Parkville 3052 
      , Australia.
AD  - School of Pharmacy , University of Nottingham , Nottingham NG7 2RD , U.K.
FAU - Muir, Benjamin W
AU  - Muir BW
AD  - CSIRO Manufacturing , Clayton , VIC 3168 , Australia.
FAU - Pigram, Paul J
AU  - Pigram PJ
AUID- ORCID: 0000-0002-7972-492X
AD  - Centre for Materials and Surface Science and Department of Chemistry and Physics, 
      School of Molecular Sciences , La Trobe University , Melbourne , VIC 3086 , 
      Australia.
LA  - eng
PT  - Journal Article
DEP - 20181010
PL  - United States
TA  - Anal Chem
JT  - Analytical chemistry
JID - 0370536
EDAT- 2018/09/28 06:00
MHDA- 2018/09/28 06:01
CRDT- 2018/09/28 06:00
PHST- 2018/09/28 06:00 [pubmed]
PHST- 2018/09/28 06:01 [medline]
PHST- 2018/09/28 06:00 [entrez]
AID - 10.1021/acs.analchem.8b01951 [doi]
PST - ppublish
SO  - Anal Chem. 2018 Nov 6;90(21):12475-12484. doi: 10.1021/acs.analchem.8b01951. Epub 
      2018 Oct 10.