Chemometric analysis of gas chromatographic peaks measured with a microsensor array: Methodology and performance assessment

A hybrid multivariate curve resolution method that combines evolving factor analysis (EFA) with alternating least squares (ALS) is applied to simulated partially overlapping binary gas chromatographic (GC) peaks from a microsensor array detector. Extended disjoint principal component regression is then used to relate the results of EFA-ALS to vapor recognition probabilities. The application of this methodology to such data is illustrated and the performance is evaluated. Responses to a set of organic vapors obtained from a portable CC with a detector consisting of an array of four nanoparticle-coated chemiresistors (CR) are used to derive the absolute and relative sensitivity values for the modeling and simulations performed. From these, seven vapor pairs spanning a range of pattern similarity are selected and modeled as Gaussian peaks whose magnitudes and degrees of overlap are varied by simulation. Performance is assessed as a function of the response pattern similarity, chromatographic resolution, signal-to-noise ratio, and the relative response ratio of the composite peak constituents. Overall, despite the low dimensionality of the array data, EFA-ALS provides an effective means of extracting information about co-eluting components from the GC-microsensor array system, and the array provides sufficient diversity of responses to identify those components in most cases, provided that the relative response ratio is <20:1. (C) 2009 Elsevier B.V. All rights reserved.