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Force microscopy analysis using chemometric tools

Force microscopy analysis using chemometric tools
Force microscopy analysis using chemometric tools
In this paper we report the first application of multivariate data analysis techniques to force spectrometry measurement sets to enable the physicochemical assignment of spatially ordered multi-component systems. Principal component analysis (PCA) and hierarchical clustering techniques were used to reveal hidden chemical information within force-distance curves generated by high spatial resolution force microscopy. Two experimental samples were analyzed: (i) a two-component system of cytochrome c proteins on a mica surface, and (ii) a three-component system of avidin protein islands positioned on a gold and glass surface. PCA and hierarchical clustering techniques were used to discriminate the different components of the two-component system, whereas hierarchical clustering was found to be superior for the three-component system. Results were in good agreement with the topography and prior knowledge of the surface patterns. This research represents a formative step towards the combination of force spectrometry with chemometric tools for the high resolution physicochemical investigation of complex biochemical systems.
interface, surface analysis, chemometrics, statistics, afm(atomic force microscopy, force spectroscopy, force-distance curves, adhesion forces, soft lithography, spectroscopy, cell, afm proteins, surface
1618-2642
1253-1260
Budich, C.
6a858341-7144-4703-81e2-5b997e1468db
West, J.
f1c2e060-16c3-44c0-af70-242a1c58b968
Lampen, P.
4be26759-967c-4ca3-85d8-189fdd9ddba3
Deckert, V.
53c41855-ef70-45f7-9425-a4ffdaaba20e
Budich, C.
6a858341-7144-4703-81e2-5b997e1468db
West, J.
f1c2e060-16c3-44c0-af70-242a1c58b968
Lampen, P.
4be26759-967c-4ca3-85d8-189fdd9ddba3
Deckert, V.
53c41855-ef70-45f7-9425-a4ffdaaba20e

Budich, C., West, J., Lampen, P. and Deckert, V. (2008) Force microscopy analysis using chemometric tools. Analytical and Bioanalytical Chemistry, 390 (5), 1253-1260. (doi:10.1007/s00216-007-1722-0). (PMID:18157669)

Record type: Article

Abstract

In this paper we report the first application of multivariate data analysis techniques to force spectrometry measurement sets to enable the physicochemical assignment of spatially ordered multi-component systems. Principal component analysis (PCA) and hierarchical clustering techniques were used to reveal hidden chemical information within force-distance curves generated by high spatial resolution force microscopy. Two experimental samples were analyzed: (i) a two-component system of cytochrome c proteins on a mica surface, and (ii) a three-component system of avidin protein islands positioned on a gold and glass surface. PCA and hierarchical clustering techniques were used to discriminate the different components of the two-component system, whereas hierarchical clustering was found to be superior for the three-component system. Results were in good agreement with the topography and prior knowledge of the surface patterns. This research represents a formative step towards the combination of force spectrometry with chemometric tools for the high resolution physicochemical investigation of complex biochemical systems.

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More information

e-pub ahead of print date: 24 December 2007
Published date: 2008
Additional Information: ISI Document Delivery No.: 268HH Times Cited: 1 Cited Reference Count: 32 Budich, Christian West, Jonathan Lampen, Peter Deckert, Volker Springer heidelberg Heidelberg
Keywords: interface, surface analysis, chemometrics, statistics, afm(atomic force microscopy, force spectroscopy, force-distance curves, adhesion forces, soft lithography, spectroscopy, cell, afm proteins, surface
Organisations: Cancer Sciences

Identifiers

Local EPrints ID: 346436
URI: https://eprints.soton.ac.uk/id/eprint/346436
ISSN: 1618-2642
PURE UUID: e2d306d6-8041-46c7-8b5b-7729e681d6f4

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Date deposited: 28 Jan 2013 09:49
Last modified: 18 Jul 2017 05:04

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Contributors

Author: C. Budich
Author: J. West
Author: P. Lampen
Author: V. Deckert

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