An electronic nose (e-nose), or artificial olfactometer, is a device that analyzes the air to quantify odor concentration using an array of gas sensors. This equipment is a monitoring tool for industrial firms. There is a shortage of an algorithm to address odor prediction challenges in an automatic fashion. Challenges like unreliable data transfer due to sensor malfunction or missing data due to anomalies in mobile data connection. Providing such an algorithm avoids human intervention in data handling, and elevates electronic nose towards smart nose. We address these challenges by proposing a data validation and data imputation method combined with a robust odor prediction machinery. Our proposed algorithm is successfully implemented on the e-nose equipment and is serving the industry currently.
Paru en novembre 2016 , 18 pages