Odor or malodor, which refers to unpleasant smells, is nowadays considered an important contributor to environmental
pollution. The public is keenly aware of odor pollution events and they often act as first indicators of air quality problems in their community. Resolution of odor pollution is often difficult because the behavioral responses to odor vary from person to person, and also because it is generally difficult to quantify the presence of odor-causing chemicals. Although the human nose is used as a way to quantify odor impacts, the quantification of odor-causing compounds responsible for causing odor complaints is required to solve odor problems. Typically, these odorous compounds are present in small
quantities and hence are difficult to quantify. Conventional technologies require large sample collection over a long period of time and extensive sample preconcentration before any analysis is done using instruments such as gas chromatographs and mass spectrometers. Fortunately, there have been recent advances in measurement technologies such as the invention of electronic noses. Conventional electronic noses (eNoses) produce a recognizable response pattern using an array of dissimilar but not specific chemical sensors. Electronic noses have interested developers of neural networks and artificial intelligence algorithms for some time, yet physical sensors have limited performance because of overlapping responses and
physical instability. eNoses using physical sensor arrays cannot separate the chemicals within vapors and hence cannot quantify the chemistry of aromas. This paper describes a new type of electronic nose, based upon high-speed gas chromatography with surface acoustic wave sensor (GC/SAW). This simulates an almost unlimited number of specific virtual chemical sensors and produces olfactory images based upon aroma chemistry. This research work describes analytical measurements of odors conducted in near real time with partsper- billion sensitivity.