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Assessment of the complementarity of data from multiple analytical techniques

McKenzie, James (2013) Assessment of the complementarity of data from multiple analytical techniques. PhD thesis, University of York.

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Whilst the capabilities of analytical techniques are ever-increasing, individual methods can provide only a limited quantity of information about the composition of a complex mixture. Interrogation of samples by multiple techniques may permit for complementary information to be acquired, and suitable data fusion strategies are required in order to optimally exploit such complementary information. A novel mid-level data fusion strategy has been implemented which uses two-stage genetic programming for feature selection and canonical correlation analysis such that highly discriminatory variables can be related together in a multivariate fashion. The approach offers an intuitive way to visualise variable interaction and their contributions to experimental trends.

Item Type: Thesis (PhD)
Keywords: Data, fusion, NMR, LC, MS, chemometrics, multivariate, analysis
Academic Units: The University of York > Chemistry (York)
Identification Number/EthosID: uk.bl.ethos.581656
Depositing User: Mr James McKenzie
Date Deposited: 07 Oct 2013 09:19
Last Modified: 08 Sep 2016 13:29
URI: http://etheses.whiterose.ac.uk/id/eprint/4471

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