Authors:
Gonçalo Santos
1
;
Cláudia Alves
1
;
Ana Carolina Pádua
1
;
Susana Palma
1
;
Hugo Gamboa
2
and
Ana Cecília Roque
1
Affiliations:
1
UCIBIO, Departamento de Química, Faculdade de Ciências e Tecnologia da Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
;
2
Laboratório de Instrumentaç ão Engenharia Biomédica e Física da Radiaç ão (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia da Universidade NOVA de Lisboa, Monte da Caparica, 2829-516 Caparica, Portugal
Keyword(s):
Electronic Nose, Volatile Organic Compounds, Machine Learning, Biomaterials.
Abstract:
Electronic noses (E-noses), are usually composed by an array of sensors with different selectivities towards classes of VOCs (Volatile Organic Compounds). These devices have been applied to a variety of fields, including environmental protection, public safety, food and beverage industries, cosmetics, and clinical diagnostics. This work demonstrates that it is possible to classify eleven VOCs from different chemical classes using a single gas sensing biomaterial that changes its optical properties in the presence of VOCs. To accomplish this, an in-house built E-nose, tailor-made for the novel class of gas sensing biomaterials, was improved and combined with powerful machine learning techniques. The device comprises a delivery system, a detection system and a data acquisition and control system. It was designed to be stable, miniaturized and easy-to-handle. The data collected was pre-processed and features and curve fitting parameters were extracted from the original response. A recur
sive feature selection method was applied to select the best features, and then a Support Vector Machine classifier was implemented to distinguish the eleven distinct VOCs. The results show that the followed methodology allowed the classification of all the VOCs tested with 94.6% (± 0.9%) accuracy.
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