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A Virtual Electronic Nose for the Efficient Classification and Quantification of Volatile Organic Compounds.

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اقرأ أكثر
  • المؤلفون: Domènech-Gil G;Domènech-Gil G; Puglisi D; Puglisi D
  • المصدر:
    Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Sep 27; Vol. 22 (19). Date of Electronic Publication: 2022 Sep 27.
  • نوع النشر :
    Journal Article
  • اللغة:
    English
  • معلومة اضافية
    • المصدر:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: Basel, Switzerland : MDPI, c2000-
    • الموضوع:
      Electronic Nose* ; Volatile Organic Compounds*/analysis; Gases ; Least-Squares Analysis ; Support Vector Machine
    • نبذة مختصرة :
      Although many chemical gas sensors report high sensitivity towards volatile organic compounds (VOCs), finding selective gas sensing technologies that can classify different VOCs is an ongoing and highly important challenge. By exploiting the synergy between virtual electronic noses and machine learning techniques, we demonstrate the possibility of efficiently discriminating, classifying, and quantifying short-chain oxygenated VOCs in the parts-per-billion concentration range. Several experimental results show a reproducible correlation between the predicted and measured values. A 10-fold cross-validated quadratic support vector machine classifier reports a validation accuracy of 91% for the different gases and concentrations studied. Additionally, a 10-fold cross-validated partial least square regression quantifier can predict their concentrations with coefficients of determination, R 2 , up to 0.99. Our methodology and analysis provide an alternative approach to overcoming the issue of gas sensors' selectivity, and have the potential to be applied across various areas of science and engineering where it is important to measure gases with high accuracy.
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    • Grant Information:
      814596 EU's H2020 research and innovation program; 2019-02095 VINNOVA
    • Contributed Indexing:
      Keywords: gas sensors; indoor air quality; machine learning; quantification; selectivity; virtual arrays; volatile organic compounds
    • الرقم المعرف:
      0 (Gases)
      0 (Volatile Organic Compounds)
    • الموضوع:
      Date Created: 20221014 Date Completed: 20221017 Latest Revision: 20221019
    • الموضوع:
      20250114
    • الرقم المعرف:
      PMC9571808
    • الرقم المعرف:
      10.3390/s22197340
    • الرقم المعرف:
      36236439