Scientific Reports (Jun 2021)

Determination of quasi-primary odors by endpoint detection

  • Hanxiao Xu,
  • Koki Kitai,
  • Kosuke Minami,
  • Makito Nakatsu,
  • Genki Yoshikawa,
  • Koji Tsuda,
  • Kota Shiba,
  • Ryo Tamura

DOI
https://doi.org/10.1038/s41598-021-91210-6
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 12

Abstract

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Abstract It is known that there are no primary odors that can represent any other odors with their combination. Here, we propose an alternative approach: “quasi” primary odors. This approach comprises the following condition and method: (1) within a collected dataset and (2) by the machine learning-based endpoint detection. The quasi-primary odors are selected from the odors included in a collected odor dataset according to the endpoint score. While it is limited within the given dataset, the combination of such quasi-primary odors with certain ratios can reproduce any other odor in the dataset. To visually demonstrate this approach, the three quasi-primary odors having top three high endpoint scores are assigned to the vertices of a chromaticity triangle with red, green, and blue. Then, the other odors in the dataset are projected onto the chromaticity triangle to have their unique colors. The number of quasi-primary odors is not limited to three but can be set to an arbitrary number. With this approach, one can first find “extreme” odors (i.e., quasi-primary odors) in a given odor dataset, and then, reproduce any other odor in the dataset or even synthesize a new arbitrary odor by combining such quasi-primary odors with certain ratios.