Multivariate Curve Resolution for Signal Isolation from Fast-Scan Cyclic Voltammetric Data.

ABSTRACT: The use of multivariate analysis techniques, such as principal component analysis-inverse least-squares (PCA-ILS), has become standard for signal isolation from in vivo fast-scan cyclic voltammetric (FSCV) data due to its superior noise removal and interferent-detection capabilities. However, the requirement of collecting separate training data for PCA-ILS model construction increases experimental complexity and, as such, has been the source of recent controversy. Here, we explore an alternative method, multivariate curve resolution-alternating least-squares (MCR-ALS), to circumvent this issue while retaining the advantages of multivariate analysis. As compared to PCA-ILS, which relies on explicit user definition of component number and profiles, MCR-ALS relies on the unique temporal signa

SUBMITTER: Johnson JA 

PROVIDER: S-EPMC6470876 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature