  A method for online decorrelation of chemical sensor signals from the effects
of environmental humidity and temperature variations is proposed. The goal is
to improve the accuracy of electronic nose measurements for continuous
monitoring by processing data from simultaneous readings of environmental
humidity and temperature. The electronic nose setup built for this study
included eight metal-oxide sensors, temperature and humidity sensors with a
wireless communication link to external computer. This wireless electronic nose
was used to monitor air for two years in the residence of one of the authors
and it collected data continuously during 537 days with a sampling rate of 1
samples per second. To estimate the effects of variations in air humidity and
temperature on the chemical sensors signals, we used a standard energy band
model for an n-type metal-oxide (MOX) gas sensor. The main assumption of the
model is that variations in sensor conductivity can be expressed as a nonlinear
function of changes in the semiconductor energy bands in the presence of
external humidity and temperature variations. Fitting this model to the
collected data, we confirmed that the most statistically significant factors
are humidity changes and correlated changes of temperature and humidity. This
simple model achieves excellent accuracy with a coefficient of determination
$R^2$ close to 1. To show how the humidity-temperature correction model works
for gas discrimination, we constructed a model for online discrimination among
banana, wine and baseline response. This shows that pattern recognition
algorithms improve performance and reliability by including the filtered signal
of the chemical sensors.
