POLYMER-BASED SENSOR ARRAYS AND MULTICOMPONENT ANALYSIS FOR THE DETECTION OF HAZARDOUS ORGANIC VAPORS IN THE ENVIRONMENT

An array of piezoelectric quartz crystals was used to detect volatile organic compounds such as hydrocarbons, chlorinated compounds and alcohols. Steady-state frequency shifts have been used as the input parameters for multicomponent analysis. The coating materials chosen were side-chain-modified polysiloxanes. The results show clearly that these polymers provide excellent reproducibility over months. In addition, the performance of the array in the presence of humidity up to 70% r.h. does not decrease compared with dry air. In the multicomponent analysis, we compared commercially available partial least-squares regression (PLS) and artificial neural network (ANN) software. The neural network designed for this application was small in order to avoid overfitting. For low-dimensional problems there is no difference between the two evaluation methods, but for complex ternary mixtures and long-term measurements the ANN offers advantages in predictability. Efforts were made to use a reduced set of calibration points, and here PLS presents the possibility of reducing the calibration time by 90% (use of Factorial and Box-Behnken designs) without loss of resolution, whereas the ANN suffers if a small number of training vectors is chosen.