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Generative Diffusion Network for Creating Scents
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AI
DOI：10.1109/ACCESS.2025.3555273.png)
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Abstract
En 中文
This paper introduces a novel application of generative diffusion networks for creating scents. The research presents a generative diffusion network designed to create new aromas with specified, required odor descriptors using essential oils as the basic components. The model uses mass spectrometry data of essential oils as latent embedding space of the essential oils. The generative network outputs mass spectrometry data as the primary output. These generated mass spectrometry profiles are then processed by non-negative least squares to create essential oils recipes that have the required odor descriptors. The results demonstrate the model's ability to produce diverse and new aroma profiles, which are validated by sensory tests. The method can create new scents by mixing essential oils, making automated aroma design possible. This approach shows major progress in aroma design. These results suggest many uses in industries like perfumery and food and beverage, improving efficiency and creativity in making many different fragrances.
Keywords:
Oils
Noise
Mass spectroscopy
Olfactory
Noise measurement
Machine learning
Noise reduction
Temperature measurement
Chemicals
Industries
Essential oils
fragrance synthesis
generative diffusion networks
mass spectrometry
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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