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A novel approach to creating tailored odours and fragrances using machine learning

The sense of smell is one of the basic senses of animal species. It is critical to find food, realise attraction, and sense danger. Humans detect smells, or odorants, with olfactory receptors expressed in olfactory nerve cells. These olfactory impressions of odorants on nerve cells are associated with their molecular features and physicochemical properties. This makes it possible to tailor odours to create an intended odour impression. Current methods only predict olfactory impressions from the physicochemical features of odorants. But, that method cannot predict the sensing data, which is indispensable for creating smells.

To tackle this issue, scientists from the Tokyo Institute of Technology (Tokyo Tech) have employed the innovative strategy of solving the inverse problem. Instead of predicting the smell from molecular data, this method predicts molecular features based on the odour impression. This is achieved using standard mass spectrum data and machine learning (ML) models. "We used a machine-learning-based odour predictive model that we had previously developed to obtain the odour impression. Then we predicted the mass spectrum from odour impression inversely based on the previously developed forward model," explains Professor Takamichi Nakamoto, the leader of the research effort by Tokyo Tech. The findings have been published in PLoS One.

The mass spectra of odour mixtures is obtained by a linear combination of the mass spectra of single components. This simple method allows for the quick preparation of the predicted spectra of odour mixtures and can also predict the required mixing ratio, an important part of the recipe for new odour preparation. "For example, we show which molecules give the mass spectrum of apple flavour with enhanced 'fruit' and 'sweet' impressions. Our analysis method shows that combinations of either 59 or 60 molecules give the same mass spectrum as the one obtained from the specified odour impression. With this information, and the correct mixing ratio needed for a certain impression, we could theoretically prepare the desired scent," highlights Prof. Nakamoto.

This novel method described in this study can provide highly accurate predictions of the physicochemical properties of odour mixtures, as well as the mixing ratios required to prepare them, thereby opening the door to endless tailor-made fragrances.

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Story Source:

Materials provided by Tokyo Institute of Technology. Note: Content may be edited for style and length.

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Journal Reference:

1. Daisuke Hasebe, Manuel Alexandre, Takamichi Nakamoto. Exploration of sensing data to realize intended odour impression using mass spectrum of odour mixture. PLOS ONE, 2022; 17 (8): e0273011 DOI: 10.1371/journal.pone.0273011

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