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Strategies for MCR image analysis of large hyperspectral data‐sets
Author(s) -
Scurr David J.,
Hook Andrew L.,
Burley Jonathan,
Williams Philip M.,
Anderson Daniel G.,
Langer Robert,
Davies Martyn C.,
Alexander Morgan R.
Publication year - 2013
Publication title -
surface and interface analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 90
eISSN - 1096-9918
pISSN - 0142-2421
DOI - 10.1002/sia.5040
Subject(s) - hyperspectral imaging , computer science , data set , throughput , data mining , set (abstract data type) , multivariate statistics , overhead (engineering) , pattern recognition (psychology) , artificial intelligence , machine learning , wireless , programming language , operating system , telecommunications
Polymer microarrays are a key enabling technology for high throughput materials discovery. In this study, multivariate image analysis, specifically multivariate curve resolution (MCR), is applied to the hyperspectral time of flight secondary ion mass spectroscopy (ToF‐SIMS) data from eight individual microarray spots. Rather than analysing the data individually, the data‐sets are collated and analysed as a single large data‐set. Desktop computing is not a practical method for undertaking MCR analysis of such large data‐sets due to the constraints of memory and computational overhead. Here, a distributed memory High‐Performance Computing facility (HPC) is used. Similar to what is achieved using MCR analysis of individual samples, the results from this consolidated data‐set allow clear identification of the substrate material; furthermore, specific chemistries common to different spots are also identified. The application of the HPC facility to the MCR analysis of ToF‐SIMS hyperspectral data‐sets demonstrates a potential methodology for the analysis of macro‐scale data without compromising spatial resolution (data ‘binning’). Copyright © 2012 John Wiley & Sons, Ltd.