Processing and Prospect of Electronic Nose

Recently, a Novel Coronavirus Pneumonia (NCP) swept the globe. This kind of new virus has extremely high infection efficiency, which has brought great disaster to human's production and daily life. However, it is reported that lung cancer and other diseases with a high incidence will lead to a very high mortality rate if they are not diagnosed and treated in time. Therefore, it is urgent to design an accurate and convenient new diagnostic equipment. Due to the increasing innovation of sensor module in the field of Information and Communication Technology (ICT), electronic nose emerges as the times require. The device can use sensors to analyze Volatile Organic Compounds exhaled by people with certain diseases and identify chemical components of various odor and flavor. In addition, many other types of sensors and classification methods such as machine learning (ML) algorithms are also mentioned in this paper. At the end of the paper, the author attached a related experiment and some shortcomings and prospects of electronic nose as well

Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses