title: Failure of Standard Training Sets in the Analysis of Fast-Scan Cyclic Voltammetry Data.
abstract: The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.
authors.affiliation: Department of Chemistry and ‡Neuroscience Center and Neurobiology Curriculum, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina 27599-3290, United States.
authors.forename: Justin A
authors.initials: JA
authors.lastname: Johnson
authors.affiliation: Department of Chemistry and ‡Neuroscience Center and Neurobiology Curriculum, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina 27599-3290, United States.
authors.forename: Nathan T
authors.initials: NT
authors.lastname: Rodeberg
authors.affiliation: Department of Chemistry and ‡Neuroscience Center and Neurobiology Curriculum, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina 27599-3290, United States.
authors.forename: R Mark
authors.initials: RM
authors.lastname: Wightman
mesh_terms: D000818:Animals; D001921:Brain / Q000378:metabolism; D001923:Brain Chemistry / Q000502:physiology; D002138:Calibration; D004298:Dopamine / Q000032:analysis*; D055664:Electrochemical Techniques / Q000592:standards*; D006863:Hydrogen-Ion Concentration; D025341:Principal Component Analysis / Q000592:standards*
chemical_list: D004298:Dopamine
keywords: Fast-scan cyclic voltammetry; calibration; chemometrics; data analysis; multivariate data analysis; principal component regression; training sets