Signal Processing using Wavelets for Enhancing Electronic Nose Performance

By Ekachai Phaisangittisagul

Abstract:

In recent years, many new technologies of electronic devices that mimic the
mammalian olfactory system, electronic noses (e-noses), have been developed in many
research institutions and commercial organizations around the world. These devices have
been used in a wide range of applications such as food and beverage quality, environmental
monitoring, medical diagnosis. Over the past decade, many researchers have spent a great
deal of effort improving e-nose performance and also extended the use of the e-nose devices,
not only for discriminating or classifying different odor samples, but also for quantifying an
ingredient of a given odor sample.
This dissertation focuses on two technical areas. First, an implementation of an e-nose
signal processing system is developed to improve classification performance for small
portable devices with fast response times and reduced cost. Second, the signal processing
system is extended to odor mixture analysis. The advances made this research are based on a
modern signal processing technique, specifically wavelet analysis. Ultimately, the
performance of e-nose devices is highly dependent on the quality of features from the
sensors’ response. Therefore, a new transient feature extraction method using wavelet
decomposition to capture the transient sensor’s response has been developed. The evaluation
of these transient features shows promising results in terms of classification performance,
number of sensors employed in the e-nose device, and simplification of the classifier. For
handling different types of odor samples, a simplified multiple classifier system is developed
based on an “odor type signature.” Analyzing mixtures of odors is a challenge for e-nose
systems. Herein a new method is developed for predicting a sensor’s response to mixtures of
odors. The combination of wavelet decomposition and reconstruction is adopted to
implement the mixed odor sensor-response predictor.

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