<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alves, Rita</style></author><author><style face="normal" font="default" size="100%">João Rodrigues</style></author><author><style face="normal" font="default" size="100%">Ramou, Efthymia</style></author><author><style face="normal" font="default" size="100%">Susana Palma</style></author><author><style face="normal" font="default" size="100%">Roque, Ana</style></author><author><style face="normal" font="default" size="100%">Gamboa, Hugo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Classification of Volatile Compounds with Morphological Analysis of e-nose Response</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Feb</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0010827200003123</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">https://sites.fct.unl.pt/sites/default/files/biomolecular_eng/files/alves_biosignals_2022.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">Scitepress</style></publisher><pages><style face="normal" font="default" size="100%">31–39</style></pages><isbn><style face="normal" font="default" size="100%">978-989-758-552-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Electronic noses (e-noses) mimic human olfaction, by identifying Volatile Organic Compounds (VOCs). This&lt;br /&gt;
work presents a novel approach that successfully classifies 11 known VOCs using the signals generated by&lt;br /&gt;
sensing gels in an in-house developed e-nose. The proposed signals’ analysis methodology is based on the&lt;br /&gt;
generated signals’ morphology for each VOC since different sensing gels produce signals with different shapes&lt;br /&gt;
when exposed to the same VOC. For this study, two different gel formulations were considered, and an average&lt;br /&gt;
f1-score of 84% and 71% was obtained, respectively. Moreover, a standard method in time series classification&lt;br /&gt;
was used to compare the performances. Even though this comparison reveals that the morphological approach&lt;br /&gt;
is not as good as the 1-nearest neighbour with euclidean distance, it shows the possibility of using descriptive&lt;br /&gt;
sentences with text mining techniques to perform VOC classification.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record></records></xml>