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Rank Estimation and the Multivariate Analysis of in Vivo Fast-Scan Cyclic Voltammetric Data
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    Rank Estimation and the Multivariate Analysis of in Vivo Fast-Scan Cyclic Voltammetric Data
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    Departments of Chemistry and Psychology, and Neuroscience Center and Neurobiology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
    * To whom correspondence should be addressed. E-mail: [email protected]
    †Department of Chemistry.
    ‡Department of Psychology.
    §Neuroscience Center and Neurobiology Curriculum.
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    Analytical Chemistry

    Cite this: Anal. Chem. 2010, 82, 13, 5541–5551
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    https://doi.org/10.1021/ac100413t
    Published June 8, 2010
    Copyright © 2010 American Chemical Society

    Abstract

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    Principal component regression has been used in the past to separate current contributions from different neuromodulators measured with in vivo fast-scan cyclic voltammetry. Traditionally, a percent cumulative variance approach has been used to determine the rank of the training set voltammetric matrix during model development; however, this approach suffers from several disadvantages including the use of arbitrary percentages and the requirement of extreme precision of training sets. Here, we propose that Malinowski’s F-test, a method based on a statistical analysis of the variance contained within the training set, can be used to improve factor selection for the analysis of in vivo fast-scan cyclic voltammetric data. These two methods of rank estimation were compared at all steps in the calibration protocol including the number of principal components retained, overall noise levels, model validation as determined using a residual analysis procedure, and predicted concentration information. By analyzing 119 training sets from two different laboratories amassed over several years, we were able to gain insight into the heterogeneity of in vivo fast-scan cyclic voltammetric data and study how differences in factor selection propagate throughout the entire principal component regression analysis procedure. Visualizing cyclic voltammetric representations of the data contained in the retained and discarded principal components showed that using Malinowski’s F-test for rank estimation of in vivo training sets allowed for noise to be more accurately removed. Malinowski’s F-test also improved the robustness of our criterion for judging multivariate model validity, even though signal-to-noise ratios of the data varied. In addition, pH change was the majority noise carrier of in vivo training sets while dopamine prediction was more sensitive to noise.

    Copyright © 2010 American Chemical Society

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    Cited By

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    This article is cited by 32 publications.

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    Analytical Chemistry

    Cite this: Anal. Chem. 2010, 82, 13, 5541–5551
    Click to copy citationCitation copied!
    https://doi.org/10.1021/ac100413t
    Published June 8, 2010
    Copyright © 2010 American Chemical Society

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