@article {Palma2022, title = {Learning to see VOCs with Liquid Crystal Droplets}, journal = {2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)}, year = {2022}, note = {

n/a

}, month = {may}, pages = {1{\textendash}4}, publisher = {IEEE}, abstract = {

In hybrid gels with immobilized liquid crystal
(LC) droplets, fast and unique optical texture variations are
generated when distinct volatile organic compounds (VOCs)
interact with the LC and disturb its molecular order. The
optical texture variations can be observed under a polarized
optical microscope or transduced into a signal representing the
variations of light transmitted through the LC. We show how
hybrid gels can accurately identify 11 distinct VOCs by using
deep learning to analyze optical texture variations of individual
droplets (0.93 average F1-score) and by using machine learning
to analyze 1D optical signals from multiple droplets in hybrid
gels (0.88 average F1-score)

}, isbn = {978-1-6654-5860-3}, doi = {10.1109/ISOEN54820.2022.9789647}, url = {https://ieeexplore.ieee.org/document/9789647/}, attachments = {https://sites.fct.unl.pt/sites/default/files/biomolecular_eng/files/palma_isoen2022.pdf}, author = {Susana I C J Palma and Frazao, Jose and Alves, Rita and Henrique M.A. Costa and Claudia Alves and Gamboa, Hugo and Silveira, Margarida and Roque, Ana C A} }