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 signatures of individual chemical components for analyte-profile determination. However, due to increased model freedom, proper deployment of MCR–ALS requires careful consideration of the model parameters and the imposition of constraints on possible model solutions. As such, approaches to achieve meaningful MCR–ALS models are characterized. It is shown, through use of previously reported techniques, that MCR–ALS can produce similar results to PCA–ILS and may serve as a useful supplement or replacement to PCA–ILS for signal isolation from FSCV data.