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

SUBMITTER: Johnson JA 

PROVIDER: S-EPMC5136453 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature