Several classification techniques have been developed with varying degrees of success for automated identification of VOCs, however, the problem becomes considerably more challenging when more than one VOC is present. The reason is two-fold: first, the response of the sensors to certain VOCs may be too strong and mask the response of the sensors to other VOCs in the environment; and second the responses of the sensors to VOCs may not have enough separability information if the specificity of the sensors is not adequate. We propose the following procedures for these two issues in identification of binary mixtures of VOCs: a nonlinear cluster transformation technique or nonparametric discriminant analysis to increase pattern separability, followed by a two-tier classification to aid in identification of dominant and secondary VOCs separately. Results demonstrate the feasibility of the combined approach.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Surfaces, Coatings and Films
- Metals and Alloys
- Electrical and Electronic Engineering
- Materials Chemistry