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. 2013 Jan;45(1):466-470.
doi: 10.1002/sia.5040. Epub 2012 May 22.

Strategies for MCR image analysis of large hyperspectral data-sets

David J Scurr  1 , Andrew L Hook, Jonathan A Burley, Philip M Williams, Daniel G Anderson, Robert C Langer, Martyn C Davies, Morgan R Alexander
Affiliations

Strategies for MCR image analysis of large hyperspectral data-sets

David J Scurr et al. Surf Interface Anal. 2013 Jan.

Abstract

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.

Keywords: high-performance computing; microarray; multivariate curve resolution; time-of-flight secondary ion mass spectrometry.

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Figures

Figure 1
Figure 1
a) Optical images of the eight individual polymer spots investigated in this study and their monomer composition, b) specific monomer structures and c) table of monomers, where the numbers listed within 1a and b correspond to listed monomer identities.
Figure 2
Figure 2
Scores image and significant loadings data for MCR components a) 1, b) 3 and c) 4, where the sample layout corresponds to that illustrated in Fig. 1.
See this image and copyright information in PMC

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