Data processing in multivariable RFID vapor sensors

Sensors for selective monitoring of gases and volatiles are needed for numerous applications including medical diagnostics, food safety, environmental, industrial, homeland protection, and many others. For these and other applications, we have developed passive radio frequency identification (RFID) sensors for vapor sensing where we apply a sensing film onto the resonant antenna of the RFID sensor, simultaneously measure several parameters of antenna impedance, and process these parameters using multivariate analysis tools. In this work, we critically analyze techniques of processing the impedance response of individual sensors coated with different sensing materials and the ability of these techniques to increase selectivity of developed sensors upon exposure to model vapors. Four types of investigated data processing techniques are based on unsupervised and supervised pattern recognition algorithms. Two evaluation criteria for these techniques involved their ability (1) to correctly identify types of vapors and (2) to provide the smallest error of prediction of concentrations of vapors.