Failure of Standard Training Sets in the Analysis of Fast-Scan Cyclic Voltammetry Data.

The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows separation of overlapping signal contributions to gain insight into the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the potential window that is gained from analysis of training sets. The ability of the constructed models to resolve analytes critically depends on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection has been reported. This study addresses the source of the deficiencies in the analyte resolution abilities 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 inappropriate assignment of the current-concentration relationships that results inaccurate analyte resolution, which may result in inaccurate conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets used for model construction be obtained under the experimental conditions to allow for accurate data analysis.