7 October 2022
Donatella Puglisi and Guillem Domènech-Gil from Linköping University (LiU) are the authors of a new peer-reviewed article in the open-access journal Sensors: “A Virtual Electronic Nose for the Efficient Classification and Quantification of Volatile Organic Compounds”. The paper demonstrates the possibility of efficiently discriminating, classifying, and quantifying short-chain oxygenated VOCs in the parts-per-billion concentration range, by exploiting the synergy between virtual electronic noses and machine learning techniques. The methodology followed and analysis carried out provide an alternative approach to overcoming the issue of gas sensors’ selectivity, and have the potential to be applied across various areas of science and engineering.
Linear discriminant analysis results for dry air, formaldehyde, formic acid, and acetic.
The ellipses, as well as the different colours and shapes, are a visual guide to help differentiate gas.
Donatella Puglisi and Guillem Domènech-Gil from Linköping University (LiU) are the authors of a new peer-reviewed article in the open-access journal […]
In the framework of SensMat activities, UBO, CEA and IC have authored a new peer-reviewed article in the open-access journal […]
As the activities of the SensMat project are about to be completed, CEA and BASSETTI have joined their efforts to […]
The fair is the main meeting place of a world made up of companies, institutions and research that contribute to […]