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.
The SensMat project has been referenced in the EU Innovation Radar Platform following the analysis of development of 8 innovation […]
We are happy to share and article grouping SensMat and APACHE projects which has been published earlier this month in […]
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Donatella Puglisi and Guillem Domènech-Gil from Linköping University (LiU) are the authors of a new peer-reviewed article in the open-access journal […]