Authors:
Ana Carolina Pádua
1
;
Jonas Gruber
2
;
Hugo Gamboa
3
and
Ana Cecília Roque
1
Affiliations:
1
UCIBIO, REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia da Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
;
2
Departamento de Química Fundamental, Instituto de Química da Universidade de São Paulo, Av. Prof. Lineu Prestes, 748 CEP 05508-000, São Paulo, SP, Brasil
;
3
Laboratório de Instrumentação Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia da Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
Keyword(s):
Electronic Nose, Volatile Organic Compounds, Spin Coating, Film Coating, Machine Learning.
Abstract:
The 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
spreading methods for sensing film’s production are adequate since the estimated error of classification was inferior to 4 % for all the classification tools applied.
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