Classical Least Squares Transformations of Sensor Array Pattern Vectors into Vapor Descriptors Simulation of Arrays of Polymer-Coated Surface Acoustic Wave Sensors with Mass-Plus-Volume Transduction Mechanisms

DISCLAIMER This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor Battelle Memorial Institute, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Summary A new method of processing multivariate response data to extract chemical information has been developed. Sensor array response patterns are transformed into a vector containing values for solvation parameter descriptors of the detected vapor's properties. These results can be obtained by using a method similar to classical least squares, and equations have been derived for mass-transducing sensors or volume-transducing sensors. Polymer-coated acoustic wave devices are an example of mass-transducing sensors. However, some acoustic wave sensors, such as polymer-coated surface acoustic wave (SAW) devices give responses resulting from both mass-loading and decreases in modulus. The latter effect can be modeled as a volume effect. In this paper we derive solutions for obtaining descriptor values from arrays of mass-plus-volume-transducing sensors. Simulations were performed to investigate the effectiveness of these solutions and to compare them with solutions for purely mass-transducing sensor arrays. It is concluded that this new method of processing sensor array data can be applied to SAW sensor arrays even when the modulus changes contribute to the responses. The simulations show that good estimations of vapor descriptors can be obtained by using a closed form estimation approach that is similar to the closed form solution for purely mass-transducing sensor arrays, Estimations can be improved using a nonlinear least squares optimization method. The results also suggest ways to design SAW arrays to obtain the best results, either by minimizing the volume sensitivity or matching the volume sensitivities in the array.