A Comparative Study on Electronic Nose Data Analysis Tools

Abstract

In the last decades, the electronic nose technology has been providing considerable advantages in practical applications including food and beverage quality assessment, medical diagnosis, security systems and air monitoring. Electronic nose systems include both hardware and software components. Sensors allow the system to collect gas/odor samples and the software carries out the classification process. While choosing robust, sensitive and compact elements is significant for the hardware requirements, the key point in the software part is selecting the appropriate algorithm, which is typically a challenging, time consuming and laborious process. Therefore in this study, an extensive comparison of the most commonly employed unsupervised data analysis algorithms in practical electronic nose applications is carried out. These approaches are also compared with supervised methods. Frequently used four dimensionality reduction techniques and four distinct clustering and classification algorithms are employed aiming at choosing the most suitable algorithms for further research in this domain. © 2020 IEEE.