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

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Biomedical Engineering Systems and Technologies (BIOSTEC 2022)

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

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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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References

  1. A study of an electronic nose for detection of lung cancer based on a virtual saw gas sensors array and imaging recognition method, author = Chen, Xing and Cao, Mingfu and Li, Yi and Hu, Weijun and Wang, Ping and Ying, Kejing and Pan, Hongming, year = 2005, journal = Measurement Science and Technology, volume = 16, number = 8, pages = 1535–1546, doi = https://doi.org/10.1088/0957-0233/16/8/001, issn = 0957-0233, url = https://iopscience.iop.org/article/10.1088/0957-0233/16/8/001, keywords = Breath detection, Electronic nose, Lung cancer, Non-invasive detection, Virtual sensors array

  2. Alves., R., Rodrigues., J., Ramou., E., Palma., S., Roque., A., Gamboa., H.: Classification of volatile compounds with morphological analysis of e-nose response. In: Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, pp. 31–39. INSTICC, SciTePress (2022). https://doi.org/10.5220/0010827200003123

  3. Bos, L.D.J., Sterk, P.J., Schultz, M.J.: Volatile metabolites of pathogens: a systematic review. PLoS Pathog. 9(5), e1003311 (2013). https://doi.org/10.1371/journal.ppat.1003311, https://dx.plos.org/10.1371/journal.ppat.1003311

  4. Bruins, M., Rahim, Z., Bos, A., van de Sande, W.W., Endtz, H.P., van Belkum, A.: Diagnosis of active tuberculosis by e-nose analysis of exhaled air. Tuberculosis 93(2), 232–238 (2013). https://doi.org/10.1016/j.tube.2012.10.002, https://linkinghub.elsevier.com/retrieve/pii/S1472979212001898

  5. Capelli, L., et al.: Application and uses of electronic noses for clinical diagnosis on urine samples: a review. Sensors 16(10), 1708 (2016). https://doi.org/10.3390/s16101708, http://www.mdpi.com/1424-8220/16/10/1708

  6. Chandler, R., Das, A., Gibson, T., Dutta, R.: Detection of oil pollution in seawater: biosecurity prevention using electronic nose technology. In: 2015 31st IEEE International Conference on Data Engineering Workshops. vol. 2015-June, pp. 98–100. IEEE (2015). https://doi.org/10.1109/ICDEW.2015.7129554, http://ieeexplore.ieee.org/document/7129554/

  7. Chen, L.Y., et al.: Development of an electronic-nose system for fruit maturity and quality monitoring. In: 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 1129–1130. IEEE (2018). https://doi.org/10.1109/ICASI.2018.8394481, https://ieeexplore.ieee.org/document/8394481/

  8. Coronel Teixeira, R., et al.: The potential of a portable, point-of-care electronic nose to diagnose tuberculosis. J. Infect. 75(5), 441–447 (2017). https://doi.org/10.1016/j.jinf.2017.08.003, https://linkinghub.elsevier.com/retrieve/pii/S0163445317302608

  9. D’Amico, A., et al.: An investigation on electronic nose diagnosis of lung cancer. Lung Cancer 68(2), 170–176 (2010). https://doi.org/10.1016/j.lungcan.2009.11.003, https://linkinghub.elsevier.com/retrieve/pii/S0169500209005807

  10. Di Natale, C., Macagnano, A., Martinelli, E., Paolesse, R., D’Arcangelo, G., Roscioni, C., Finazzi-Agrò, A., D’Amico, A.: Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. Biosens. Bioelectron. 18(10), 1209–1218 (2003). https://doi.org/10.1016/S0956-5663(03)00086-1, https://linkinghub.elsevier.com/retrieve/pii/S0956566303000861

  11. Dragonieri, S., Pennazza, G., Carratu, P., Resta, O.: Electronic nose technology in respiratory diseases. Lung 195(2), 157–165 (2017). https://doi.org/10.1007/s00408-017-9987-3

    Article  Google Scholar 

  12. Dragonieri, S., et al.: An electronic nose in the discrimination of patients with asthma and controls. J. Allergy Clin. Immunol. 120(4), 856–862 (2007). https://doi.org/10.1016/j.jaci.2007.05.043, https://linkinghub.elsevier.com/retrieve/pii/S009167490701038X

  13. Dutta, R., Hines, E.L., Gardner, J.W., Boilot, P.: Bacteria classification using Cyranose 320 electronic nose. BioMed. Eng. OnLine 1(1), 4 (2002). https://doi.org/10.1186/1475-925X-1-4, https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/1475-925X-1-4

  14. Esteves, C., et al.: Effect of film thickness in gelatin hybrid gels for artificial olfaction. Mater. Today Bio 1(December 2018), 100002 (2019). https://doi.org/10.1016/j.mtbio.2019.100002, https://linkinghub.elsevier.com/retrieve/pii/S2590006418300401

  15. Faouzi, J., Janati, H.: pyts: a python package for time series classification. J. Mach. Learn. Res. 21(46), 1–6 (2020). http://jmlr.org/papers/v21/19-763.html

  16. Fens, N., et al.: Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma. Am. J. Respir. Critic. Care Med. 180(11), 1076–1082 (2009). https://doi.org/10.1164/rccm.200906-0939OC, http://www.atsjournals.org/doi/abs/10.1164/rccm.200906-0939OC

  17. Frazão, J., Palma, S.I.C.J., Costa, H.M.A., Alves, C., Roque, A.C.A., Silveira, M.: Optical gas sensing with liquid crystal droplets and convolutional neural networks. Sensors 21(8), 2854 (2021). https://doi.org/10.3390/s21082854, https://www.mdpi.com/1424-8220/21/8/2854/htm

  18. HaCohen-Kerner, Y., Miller, D., Yigal, Y.: The influence of preprocessing on text classification using a bag-of-words representation. PLOS ONE 15(5), 1–22 (2020). https://doi.org/10.1371/journal.pone.0232525, https://doi.org/10.1371/journal.pone.0232525

  19. He, Q., et al.: Classification of electronic nose data in wound infection detection based on PSO-SVM combined with wavelet transform. Intell. Autom. Soft Comput. 18(7), 967–979 (2012). https://doi.org/10.1080/10798587.2012.10643302, http://autosoftjournal.net/paperShow.php?paper=10643302

  20. Hockstein, N.G., Thaler, E.R., Lin, Y., Lee, D.D., Hanson, C.W.: Correlation of pneumonia score with electronic nose signature: a prospective study. Ann. Otol. Rhinol. Laryngol. 114(7), 504–508 (2005). https://doi.org/10.1177/000348940511400702, http://journals.sagepub.com/doi/10.1177/000348940511400702

  21. Hockstein, N.G., Thaler, E.R., Torigian, D., Miller, W.T., Deffenderfer, O., Hanson, C.W.: Diagnosis of pneumonia with an electronic nose: correlation of vapor signature with chest computed tomography scan findings. Laryngoscope 114(10), 1701–1705 (2004). https://doi.org/10.1097/00005537-200410000-00005, http://doi.wiley.com/10.1097/00005537-200410000-00005

  22. Hu, W., et al.: Electronic noses: from advanced materials to sensors aided with data processing. Adv. Mater. Technol. 4(2), 1–38 (2018). https://doi.org/10.1002/admt.201800488, https://onlinelibrary.wiley.com/doi/abs/10.1002/admt.201800488

  23. Hussain, A., et al.: Tunable gas sensing gels by cooperative assembly. Adv. Funct. Mater. 27(27), 1700803 (2017). https://doi.org/10.1002/adfm.201700803, http://doi.wiley.com/10.1002/adfm.201700803

  24. Jian, Y., et al.: Artificially intelligent olfaction for fast and noninvasive diagnosis of bladder cancer from urine. ACS Sens. 7(6), 1720–1731 (2022). https://doi.org/10.1021/acssensors.2c00467, https://doi.org/10.1021/acssensors.2c00467, pMID: 35613367

  25. Karakaya, D., Ulucan, O., Turkan, M.: Electronic nose and its applications: a survey. Int. J. Autom. Comput. 17(2), 179–209 (2020). https://doi.org/10.1007/s11633-019-1212-9, http://link.springer.com/10.1007/s11633-019-1212-9

  26. Keogh, E., Lonardi, S., Ratanamahatana, C.A.: Towards parameter-free data mining. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 206–215. KDD 2004, Association for Computing Machinery, New York, NY, USA (2004). https://doi.org/10.1145/1014052.1014077

  27. van Keulen, K.E., Jansen, M.E., Schrauwen, R.W.M., Kolkman, J.J., Siersema, P.D.: Volatile organic compounds in breath can serve as a non-invasive diagnostic biomarker for the detection of advanced adenomas and colorectal cancer. Aliment. Pharmacol. Ther. 51(3), 334–346 (2020). https://doi.org/10.1111/apt.15622, http://doi.wiley.com/10.1111/apt.15622

  28. Kodogiannis, V., Lygouras, J., Tarczynski, A., Chowdrey, H.: Artificial odor discrimination system using electronic nose and neural networks for the identification of urinary tract infection. IEEE Trans. Inf. Technol. Biomed. 12(6), 707–713 (2008). https://doi.org/10.1109/TITB.2008.917928, http://ieeexplore.ieee.org/document/4526692/

  29. Lee, Y.S., Joo, B.S., Choi, N.J., Lim, J.O., Huh, J.S., Lee, D.D.: Visible optical sensing of ammonia based on polyaniline film. Sens. Actuators B: Chem. 93(1–3), 148–152 (2003). https://doi.org/10.1016/S0925-4005(03)00207-7, https://linkinghub.elsevier.com/retrieve/pii/S0925400503002077

  30. Liang, Z., Tian, F., Zhang, C., Sun, H., Liu, X., Yang, S.X.: A correlated information removing based interference suppression technique in electronic nose for detection of bacteria. Analytica Chimica Acta 986, 145–152 (2017). https://doi.org/10.1016/j.aca.2017.07.028, http://dx.doi.org/10.1016/j.aca.2017.07.028

  31. Liddell, K.: Smell as a diagnostic marker. Postgrad. Med. J. 52(605), 136–138 (1976). https://doi.org/10.1136/pgmj.52.605.136, https://pmj.bmj.com/lookup/doi/10.1136/pgmj.52.605.136

  32. Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15, 107–144 (2007). https://doi.org/10.1007/s10618-007-0064-z

    Article  MathSciNet  Google Scholar 

  33. Lin, J., Khade, R., Li, Y.: Rotation-invariant similarity in time series using bag-of-patterns representation. J. Intell. Inf. Syst. 39(2), 287–315 (2012). https://doi.org/10.1007/s10844-012-0196-5

    Article  Google Scholar 

  34. Moens, M., et al.: Fast identification of ten clinically important micro-organisms using an electronic nose. Lett. Appl. Microbiol. 42(2), 121–126 (2006). https://doi.org/10.1111/j.1472-765X.2005.01822.x, http://doi.wiley.com/10.1111/j.1472-765X.2005.01822.x

  35. Pádua, A.C., Palma, S., Gruber, J., Gamboa, H., Roque, A.C.: Design and evolution of an opto-electronic device for VOCs detection. In: BIODEVICES 2018–11th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018 1(Biostec), pp. 48–55 (2018). https://doi.org/10.5220/0006558100480055

  36. Pavlou, A.K., Magan, N., Jones, J.M., Brown, J., Klatser, P., Turner, A.P.: Detection of Mycobacterium tuberculosis (TB) in vitro and in situ using an electronic nose in combination with a neural network system. Biosens. Bioelectron. 20(3), 538–544 (2004). https://doi.org/10.1016/j.bios.2004.03.002, https://linkinghub.elsevier.com/retrieve/pii/S0956566304001204

  37. Persaud, K., Dodd, G.: Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 299(5881), 352–355 (1982). https://doi.org/10.1038/299352a0, http://www.nature.com/articles/299352a0

  38. Pinto, J., et al.: Urinary volatilomics unveils a candidate biomarker panel for noninvasive detection of clear cell renal cell carcinoma. J. Proteome Res. 20(6), 3068–3077 (2021). https://doi.org/10.1021/acs.jproteome.0c00936, https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00936

  39. Raspagliesi, F., Bogani, G., Benedetti, S., Grassi, S., Ferla, S., Buratti, S.: Detection of ovarian cancer through exhaled breath by electronic nose: a prospective study. Cancers 12(9), 1–13 (2020). https://doi.org/10.3390/cancers12092408

    Article  Google Scholar 

  40. Röck, F., Barsan, N., Weimar, U.: Electronic nose: current status and future trends. Chem. Rev. 108(2), 705–725 (2008). https://doi.org/10.1021/cr068121q, https://pubs.acs.org/doi/10.1021/cr068121q

  41. Rodrigues, J., Folgado, D., Belo, D., Gamboa, H.: SSTS: a syntactic tool for pattern search on time series. Inf. Process. Manage. 56(1), 61–76 (2019). https://doi.org/10.1016/j.ipm.2018.09.001, https://www.sciencedirect.com/science/article/pii/S0306457318302577

  42. Saidi, T., Zaim, O., Moufid, M., El Bari, N., Ionescu, R., Bouchikhi, B.: Exhaled breath analysis using electronic nose and gas chromatography–mass spectrometry for non-invasive diagnosis of chronic kidney disease, diabetes mellitus and healthy subjects. Sens. Actuat. B: Chem. 257, 178–188 (2018). https://doi.org/10.1016/j.snb.2017.10.178, http://dx.doi.org/10.1016/j.snb.2017.10.178

  43. Santonico, M., et al.: In situ detection of lung cancer volatile fingerprints using bronchoscopic air-sampling. Lung Cancer 77(1), 46–50 (2012). https://doi.org/10.1016/j.lungcan.2011.12.010, https://linkinghub.elsevier.com/retrieve/pii/S016950021100674X

  44. Santos, G., Alves, C., Pádua, A., Palma, S., Gamboa, H., Roque, A.: An optimized e-nose for efficient volatile sensing and discrimination. In: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 36–46. SCITEPRESS - Science and Technology Publications (2019). https://doi.org/10.5220/0007390700360046, http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0007390700360046

  45. Santos, J., et al.: Electronic nose for the identification of pig feeding and ripening time in Iberian hams. Meat Sci. 66(3), 727–732 (2004). https://doi.org/10.1016/j.meatsci.2003.07.005, https://linkinghub.elsevier.com/retrieve/pii/S0309174003001955

  46. Schäfer, P.: The BOSS is concerned with time series classification in the presence of noise. Data Min. Knowl. Disc. 29(6), 1505–1530 (2014). https://doi.org/10.1007/s10618-014-0377-7

    Article  MathSciNet  MATH  Google Scholar 

  47. Schäfer, P., Leser, U.: Fast and accurate time series classification with weasel, pp. 637–646. Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3132847.3132980

  48. van der Schee, M.P., Fens, N., Buijze, H., Top, R., van der Poll, T., Sterk, P.J.: Diagnostic value of exhaled breath analysis in tuberculosis. In: D96. WHAT’S NEW IN TUBERCULOSIS DIAGNOSTICS. pp. A6510–A6510. American Thoracic Society (2012). https://doi.org/10.1164/ajrccm-conference.2012.185.1_MeetingAbstracts.A6510, http://www.atsjournals.org/doi/abs/10.1164/ajrccm-conference.2012.185.1_MeetingAbstracts.A6510

  49. Schnabel, R., et al.: Analysis of volatile organic compounds in exhaled breath to diagnose ventilator-associated pneumonia. Sci. Rep. 5(1), 17179 (2015). https://doi.org/10.1038/srep17179, http://www.nature.com/articles/srep17179

  50. Senin, P., Malinchik, S.: Sax-vsm: interpretable time series classification using sax and vector space model (2013). https://doi.org/10.1109/ICDM.2013.52

  51. de Vries, R., et al.: Integration of electronic nose technology with spirometry: validation of a new approach for exhaled breath analysis. J. Breath Res. 9(4), 046001 (2015). https://doi.org/10.1088/1752-7155/9/4/046001, https://iopscience.iop.org/article/10.1088/1752-7155/9/4/046001

  52. Wilson, A.D., Baietto, M.: Advances in electronic-nose technologies developed for biomedical applications. Sensors 11(1), 1105–1176 (2011). https://doi.org/10.3390/s110101105, http://www.mdpi.com/1424-8220/11/1/1105

  53. Wong, D.M., et al.: Development of a breath detection method based e-nose system for lung cancer identification. In: 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 1119–1120. IEEE (2018). https://doi.org/10.1109/ICASI.2018.8394477, https://ieeexplore.ieee.org/document/8394477/

  54. Yang, H.Y., Wang, Y.C., Peng, H.Y., Huang, C.H.: Breath biopsy of breast cancer using sensor array signals and machine learning analysis. Sci. Rep. 11(1), 103 (2021). https://doi.org/10.1038/s41598-020-80570-0, http://www.nature.com/articles/s41598-020-80570-0

  55. Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956. KDD 2009, Association for Computing Machinery, New York, NY, USA (2009). https://doi.org/10.1145/1557019.1557122

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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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  1. Rita Alves
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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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