The focus of this study is the quantification of multianalyte mixtures in water by the use of sensor arrays based on polymer layers. Reflectometric interference spectroscopy is used as a optical sensor system for temporal-resolved measurements of the interaction kinetics of analytes in water with the polymer layers. The principles and widespread possibilities of this approach are demonstrated using the quantification of quaternary aqueous mixtures of low alcohols from methanol up to 1-butanol. The sensitive layers consist of two hyperbranched polyesters and one microporous polyimide. Different time-dependent sensor signals are evaluated by artificial neural networks. Because the kinetics of sorption and desorption of the analytes differ significantly, the number of sensors needed for a quantification of analytes in mixtures can be reduced. A feature extraction allows identification of the most important differences of kinetic patterns of the analytes and allows improvement of the multivariate calibration. It is shown that a quantification of quaternary mixtures of methanol, ethanol, 1-propanol, and 1-butanol is possible on the basis of only two polymer sensors.

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In this study, different approaches to the multivariate calibration of the vapors of two refrigerants are reported. As the relationships between the time-resolved sensor signals and the concentrations of the analytes are nonlinear, the widely used partial least-squares regression (PLS) fails. Therefore, different methods are used, which are known to be able to deal with nonlinearities present in data. First, the Box-Cox transformation, which transforms the dependent variables nonlinearly, was applied. The second approach, the implicit nonlinear PLS regression, tries to account for nonlinearities by introducing squared terms of the independent variables to the original independent variables. The third approach, quadratic PLS (QPLS), uses a nonlinear quadratic inner relationship for the model instead of a linear relationship such as PLS. Tree algorithms are also used, which split a nonlinear problem into smaller subproblems, which are modeled using linear methods or discrete values. Finally, neural networks are applied, which are able to model any relationship. Different special implementations, like genetic algorithms with neural networks and growing neural networks, are also used to prevent an overfitting. Among the fast and simpler algorithms, QPLS shows good results. Different implementations of neural networks show excellent results. Among the different implementations, the most sophisticated and computing-intensive algorithms (growing neural networks) show the best results. Thus, the optimal method for the data set presented is a compromise between quality of calibration and complexity of the algorithm.

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