On reliability of neural network sensitivity analysis applied for sensor array optimization

Sensor arrays are nowadays first choice solution for low-cost and portable gas mixtures analysis systems. The key issue in construction of such systems is selection of sensors. The authors try to apply neural network sensitivity analysis for this task. The algorithm starts from huge set of sensors, which provides satisfying operation of the system, and then detects the most redundant elements, which may be removed without significant decrease of system accuracy. Eventually the small but efficient array of sensors is obtained. Authors present the method, propose some modifications and discuss problems with its application. Results of sensitivity analysis approach are compared with exhaustive search for the best set of sensors. The case study is quantitative analysis of volatile organic compounds mixtures by means of commercial, tin dioxide, TGS 800 series sensors characterized in in-house developed gas chamber.