@conference {Alves2022, title = {Classification of Volatile Compounds with Morphological Analysis of e-nose Response}, booktitle = {Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS}, year = {2022}, note = {

n/a

}, month = {Feb}, pages = {31{\textendash}39}, publisher = {Scitepress}, organization = {Scitepress}, abstract = {

Electronic noses (e-noses) mimic human olfaction, by identifying Volatile Organic Compounds (VOCs). This
work presents a novel approach that successfully classifies 11 known VOCs using the signals generated by
sensing gels in an in-house developed e-nose. The proposed signals{\textquoteright} analysis methodology is based on the
generated signals{\textquoteright} morphology for each VOC since different sensing gels produce signals with different shapes
when exposed to the same VOC. For this study, two different gel formulations were considered, and an average
f1-score of 84\% and 71\% was obtained, respectively. Moreover, a standard method in time series classification
was used to compare the performances. Even though this comparison reveals that the morphological approach
is not as good as the 1-nearest neighbour with euclidean distance, it shows the possibility of using descriptive
sentences with text mining techniques to perform VOC classification.

}, isbn = {978-989-758-552-4}, doi = {10.5220/0010827200003123}, url = {https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0010827200003123}, attachments = {https://sites.fct.unl.pt/sites/default/files/biomolecular_eng/files/alves_biosignals_2022.pdf}, author = {Alves, Rita and Jo{\~a}o Rodrigues and Ramou, Efthymia and Susana Palma and Roque, Ana and Gamboa, Hugo} }