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Exploration of sensing data to realize intended odor impression using mass spectrum of odor mixture

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  • Daisuke Hasebe
  • Manuel Alexandre
  • Takamichi Nakamoto

Abstract

Recently, olfactory information on odorants has been associated with their corresponding molecular features. Such information has been obtained by predicting the sensory test evaluation scores from the molecular structure parameters or the sensing data. On the other hand, we develop a method of the prediction of molecular features corresponding to the odor impression. We utilize a machine-learning-based odor predictive model introduced in our previous research, and we propose a mathematical model for exploring the sensing data space. By using mass spectrum as sensing data in the predictive model, we can represent predicted mass spectrum as those of an odor mixture, and the mixing ratio can be obtained. We show that the mass spectrum of apple flavor with enhanced ‘fruit’ and ‘sweet’ impressions can be obtained using 59 and 60 molecules respectively by using our analysis method.

Suggested Citation

  • Daisuke Hasebe & Manuel Alexandre & Takamichi Nakamoto, 2022. "Exploration of sensing data to realize intended odor impression using mass spectrum of odor mixture," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-17, August.
    • Handle: RePEc:plo:pone00:0273011
    DOI: 10.1371/journal.pone.0273011
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    References listed on IDEAS

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    1. repec:plo:pone00:0208962 is not listed on IDEAS
    2. Yuji Nozaki & Takamichi Nakamoto, 2018. "Predictive modeling for odor character of a chemical using machine learning combined with natural language processing," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.
    3. Yuji Nozaki & Takamichi Nakamoto, 2016. "Odor Impression Prediction from Mass Spectra," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
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