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Comparing Different Dictionary-Based Classifiers for the Classification of Volatile Compounds Measured with an E-nose

  • Conference paper
  • First Online:
Biomedical Engineering Systems and Technologies (BIOSTEC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1814))

  • 226 Accesses

Abstract

Electronic noses (e-noses) are devices that mimic the biological sense of olfaction to recognize gaseous samples in a very fast and accurate manner, being applicable in a multitude of scenarios. E-noses are composed of an array of gas sensors, a signal acquisition unit and a pattern recognition unit including automatic classifiers based on machine learning. In a previous work, a text-based approach was developed to classify volatile organic compounds (VOCs) using as input signals from an in-house developed e-nose. This text-based algorithm was compared with a 1-nearest neighbor classifier with euclidean distance (1-NN ED). In this work we studied other text-based approaches that relied in the Bag of Words model and compared it with the previous approach that relied in the term frequency-inverse document frequency (TF-IDF) model and other traditional text-mining classifiers, namely the naive bayes and linear Support Vector Machines (SVM). The results show that the TF-IDF model is more robust overall when compared with the Bag of Words (BoW) model. An average F1-score of 0.84 and 0.70 was achieved for the TF-IDF model with a linear SVM for two distinct gas sensor formulations (5CB and 8CB, respectively), while an F1-score of 0.66 and 0.71 was achieved for the BoW model for the same formulations. The text-based approaches appeared to be less reliable than the traditional 1-NN ED method.

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Acknowledgements

This project has received funding from the European Research Council (ERC) under the EU Horizon 2020 research and innovation programme [grant reference SCENT-ERC-2014-STG-639123, (2015-2022)] and by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences - UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy - i4HB, which is financed by national funds from financed by FCT/MEC (UID/Multi/04378/2019). This work was also partly supported by Fundação para a Ciência e Tecnologia, under PhD grant PD/BDE/142816/2018.

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Authors and Affiliations

  1. LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal

    Rita Alves, Joao Rodrigues & Hugo Gamboa

  2. Associate Laboratory i4HB- Institute for Health and Bioeconomy, School of Science and Technology, NOVA University Lisbon, 2829-516, Caparica, Portugal

    Rita Alves, Efthymia Ramou, Susana I. C. J. Palma & Ana C. A. Roque

  3. UCIBIO - Applied Molecular Biosciences Unit, Department of Chemistry, School of Science and Technology, NOVA University Lisbon, 2829-516, Caparica, Portugal

    Rita Alves, Efthymia Ramou, Susana I. C. J. Palma & Ana C. A. Roque

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Editors and Affiliations

  1. Universidade Nova de Lisboa, Caparica, Portugal

    Ana Cecília A. Roque

  2. Virginia Tech, Blacksburg, VA, USA

    Denis Gracanin

  3. University of Vienna, Vienna, Austria

    Ronny Lorenz

  4. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  5. Université de Montréal, Montréal, QC, Canada

    Nathalie Bier

  6. Instituto de Telecomunicações and Instituto Superior Técnico - Lisbon University, Lisbon, Portugal

    Ana Fred

  7. Nova School of Science and Technology and University of Lisbon, Caparica, Portugal

    Hugo Gamboa

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Alves, R., Rodrigues, J., Ramou, E., Palma, S.I.C.J., Roque, A.C.A., Gamboa, H. (2023). Comparing Different Dictionary-Based Classifiers for the Classification of Volatile Compounds Measured with an E-nose. In: Roque, A.C.A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2022. Communications in Computer and Information Science, vol 1814. Springer, Cham. https://doi.org/10.1007/978-3-031-38854-5_7

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