Multivariate curve resolution of nonlinear ion mobility spectra followed by multivariate nonlinear calibration for quantitative prediction

Abstract In this work, a new methodology to analyze spectra time-series obtained from ion mobility spectrometry (IMS) has been investigated. The proposed method combines the advantages of multivariate curve resolution-alternating least squares (MCR-ALS) for an optimal physical and chemical interpretation of the system (qualitative information) and a multivariate calibration technique such as polynomial partial least squares (poly-PLS) for an improved quantification (quantitative information) of new samples. Ten different concentrations of 2-butanone and ethanol were generated using a volatile generator based on permeation tubes. The different concentrations were measured with IMS. These data present a non-linear behavior as substance concentration increases. Although MCR-ALS is based on a bilinear decomposition, non-linear behavior can be modeled by adding new components to the model. After spectral pre-processing, MCR-ALS was applied aiming to get information about the ionic species that appear in the drift tube and their evolution with the analyte concentration. By resolving the IMS data matrix, concentration profiles and pure spectra of the different ionic species have been obtained for both analytes. Finally, poly-PLS was used in order to build a calibration model using concentration profiles obtained from MCR-ALS for ethanol and 2-butanone. The results, with more than 99% of explained variance for both substances, show the feasibility of using MCR-ALS to resolve IMS datasets. Furthermore, similar or better prediction accuracy is achieved when concentration profiles from MCR-ALS are used to build a calibration model (using poly-PLS) compared to other standard univariate and multivariate calibration methodologies.