Alves, R, Rodrigues J, Ramou E, Palma S, Roque A, Gamboa H.
2022.
Classification of Volatile Compounds with Morphological Analysis of e-nose Response, Feb. Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS. :31–39.: Scitepress
AbstractElectronic 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’ analysis methodology is based on the
generated signals’ 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.
Palma, SICJ, Esteves C, Pádua AC, Alves CM, Santos GMC, Costa HMA, Dionisio M, Gamboa H, Gruber J, Roque ACA.
2019.
Enhanced gas sensing with soft functional materials, May 2019. ISOEN 2019 - 18th International Symposium on Olfaction and Electronic Nose, Proceedings. , Fukuoka, Japan: Institute of Electrical and Electronics Engineers Inc.
AbstractThe materials described in this work result from the selfassembly of liquid crystals and ionic liquids into droplets,
stabilized within a biopolymeric matrix. These systems are
extremely versatile gels, in terms of composition, and offer
potential for fine tuning of both structure and function, as
each individual component can be varied. Here, the
characterization and application of these gels as sensing thin
films in gas sensor devices is presented. The unique
supramolecular structure of the gels is explored for molecular
recognition of volatile organic compounds (VOCs) by
employing gels with distinct formulations to yield
combinatorial optical and electrical responses used in the
distinction and identification of VOCs.
Roque, ACA, Fred A, Gamboa H.
2019.
Foreword, January 2019. BIODEVICES 2019 - 12th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019. , Prague: SciTePress
Padua, A, Gruber J, Gamboa H, Roque ACA.
2019.
Impact of Sensing Film’s Production Method on Classification Accuracy by Electronic Nose. Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES. , Prague, Czech Republic
AbstractThe development of gas sensing materials is relevant in the field of non-invasive biodevices. In this work, we used an electronic nose (E-nose) developed by our research group, which possess versatile and unique sensing materials. These are gels that can be spread over the substrate by Film Coating or Spin Coating. This study aims to evaluate the influence of the sensing film spreading method selected on the classification capabilities of the E-nose. The methodology followed consisted of performing an experiment where the E-nose was exposed to 13 different pure volatile organic compounds. The sensor array had two sensing films produced by Film Coating, and other two produced by Spin Coating. After data collection, a set of features was extracted from the original signal curves, and the best were selected by Recursive Feature Elimination. Then, the classification performance of Multinomial Logistic regression, Decision Tree, and Naíve Bayes was evaluated. The results showed that both s preading methods for sensing film’s production are adequate since the estimated error of classification was inferior to 4 % for all the classification tools applied.