Electronic noses, or e-noses, are systems that combine chemical gas sensors, signal processing, and machine learning algorithms to mimic the sense of smell. E-noses can be used for checking food quality, monitoring air pollution, diagnosing diseases, and detecting explosives. Researchers from Northwestern Polytechnical University in Xi’an, China, and Weiwei Wu of Xidian University in Xi’an, China, conducted a comprehensive review of the methods and algorithms developed for e-noses. The review discusses the limitations of current gas sensors and provides an outlook on algorithm design. Findings were published on Jan. 20 in Intelligent Computing, a Science Partner Journal.
Gas sensors of an e-nose correspond to biological olfactory receptor neurons. When you sniff something, tiny molecules float through the air and enter your nose. Similarly, a gas sensor captures airborne molecules through an air intake system, reacts to the molecules, and changes in a way that electronic signals can measure. The signals are converted from an analog to a digital format so that computers can use algorithms to analyze and interpret the data.
They identified the following limitations of gas sensors:
- Limitations on selectivity. Gas sensors respond to all stimuli in a mixture, making it difficult to distinguish different odors
- Limitations on sensitivity. Every gas sensor operates within a range defined by the minimum and maximum quantity it can detect
- Limitations on stability. Gas sensors often do not produce a stable and reproducible response to the same chemical stimuli
- Limitations on reproducibility. Two gas sensors of the same type may have different responses to the same gas under the same conditions
- Limitations on noise. Gas sensors are affected by external and internal noise
Large-scale deployment of e-noses in practical applications still has a long way to go.
