Multivariate curve resolution of co-eluting vapors from a gas chromatograph with microsensor array detector

Abstract

The application of a hybrid multivariate curve resolution method, which combines evolving factor analysis (EFA) with alternating least squares (ALS), to the analysis of partially overlapping peaks from vapors measured by a microsensor-array gas chromatograph detector is described. The detector comprised an array of four chemiresistors coated with different sorptive thiolate-monolayer-protected gold nanoparticle (MPN) films. Three pairs of vapors, the members of which had array response pattern correlation coefficients, ρ, ranging from −0.57 to 0.85, were tested at different values of chromatographic resolution, Rs, and relative response ratio, RRR. Composite responses were equivalent to the sums of the responses to the individual components, but differences in peak asymmetry among the sensors in the array led to pattern distortions across the spans of all peaks. With data pre-processing to account for the latter, EFA correctly determined the chemical ranks of the binary composite peaks in 57 of the 63 cases (90%), with most errors observed for the most highly correlated pair. By using calibrated response patterns as inputs for the ALS refinements of EFA-extracted responses, the fidelity of recovered response patterns and elution profiles was sufficiently high to differentiate the composite peak components in 124 of 126 cases (98%) and to quantify them to within ±30% of actual values in 95 of 126 (75%) cases. Without such inputs, the corresponding rates were 112 of 126 (89%) and 68 of 126 (54%), respectively. In general, the RRR value was a more important determinant of performance than was the Rs value. The methodology and performance of EFA-ALS in this application are critically assessed.