Strategies for MCR image analysis of large hyperspectral data-sets
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 individua...
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| Format: | Article |
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Wiley
2012
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| Online Access: | https://eprints.nottingham.ac.uk/3134/ |
| _version_ | 1848790961177493504 |
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| author | Scurr, David J. Hook, Andrew L. Burley, Jonathan Williams, Philip M. Anderson, Daniel G. Langer, Robert Davies, Martyn C. Alexander, Morgan R. |
| author_facet | Scurr, David J. Hook, Andrew L. Burley, Jonathan Williams, Philip M. Anderson, Daniel G. Langer, Robert Davies, Martyn C. Alexander, Morgan R. |
| author_sort | Scurr, David J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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’) |
| first_indexed | 2025-11-14T18:20:56Z |
| format | Article |
| id | nottingham-3134 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:20:56Z |
| publishDate | 2012 |
| publisher | Wiley |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-31342020-05-04T16:33:14Z https://eprints.nottingham.ac.uk/3134/ Strategies for MCR image analysis of large hyperspectral data-sets Scurr, David J. Hook, Andrew L. Burley, Jonathan Williams, Philip M. Anderson, Daniel G. Langer, Robert Davies, Martyn C. Alexander, Morgan R. 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’) Wiley 2012-05-22 Article PeerReviewed Scurr, David J., Hook, Andrew L., Burley, Jonathan, Williams, Philip M., Anderson, Daniel G., Langer, Robert, Davies, Martyn C. and Alexander, Morgan R. (2012) Strategies for MCR image analysis of large hyperspectral data-sets. Surface and Interface Analysis, 45 (1). pp. 466-470. ISSN 0142-2421 Time-of-flight secondary ion mass spectrometry Multivariate curve resolution Microarray High-performance computing http://onlinelibrary.wiley.com/doi/10.1002/sia.5040/full doi:10.1002/sia.5040 doi:10.1002/sia.5040 |
| spellingShingle | Time-of-flight secondary ion mass spectrometry Multivariate curve resolution Microarray High-performance computing Scurr, David J. Hook, Andrew L. Burley, Jonathan Williams, Philip M. Anderson, Daniel G. Langer, Robert Davies, Martyn C. Alexander, Morgan R. Strategies for MCR image analysis of large hyperspectral data-sets |
| title | Strategies for MCR image analysis of large hyperspectral data-sets |
| title_full | Strategies for MCR image analysis of large hyperspectral data-sets |
| title_fullStr | Strategies for MCR image analysis of large hyperspectral data-sets |
| title_full_unstemmed | Strategies for MCR image analysis of large hyperspectral data-sets |
| title_short | Strategies for MCR image analysis of large hyperspectral data-sets |
| title_sort | strategies for mcr image analysis of large hyperspectral data-sets |
| topic | Time-of-flight secondary ion mass spectrometry Multivariate curve resolution Microarray High-performance computing |
| url | https://eprints.nottingham.ac.uk/3134/ https://eprints.nottingham.ac.uk/3134/ https://eprints.nottingham.ac.uk/3134/ |