Monosaccharide Sensing Based on Multivariate Analysis of Voltammetric Data Acquired from a Pt:Ru Electrode Array
Predictive models for concentration of mixed monosaccharide solutions were developed based on combinatorial electrochemistry and chemometric techniques. The columns on a 10x10 array of Pt wires were electrodeposited with 10 different Pt:Ru alloys. Cyclic voltammograms were performed in 1M solutions...
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Published in | ECS transactions Vol. 6; no. 20; pp. 13 - 27 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
08.02.2008
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Online Access | Get full text |
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Summary: | Predictive models for concentration of mixed monosaccharide solutions were developed based on combinatorial electrochemistry and chemometric techniques. The columns on a 10x10 array of Pt wires were electrodeposited with 10 different Pt:Ru alloys. Cyclic voltammograms were performed in 1M solutions of glucose, fructose, and galactose. Principal component analysis was applied to the resulting data sets; a scores plot allowed classification of the pure solutions. Fifteen solutions containing the three sugars in concentrations ranging from 10-1000mM were used to train partial least squares regression models. Twelve independent test solutions were also prepared in similar concentration ranges. For these test samples, root-mean-squared-error-of- predictions (RMSEPs) of 142mM and 120mM were obtained for glucose and galactose. The RMSEP for fructose between 10- 500mM was 128mM, but nonlinearities caused the model to fail at higher concentratio ns. These results demonstrate that with only a few electrode variants it is possible to differentiate monosaccharides in a semi-quantitative fashion. |
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ISSN: | 1938-5862 1938-6737 |
DOI: | 10.1149/1.2831340 |