Determination of quasi-primary odors by endpoint detection

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-...

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Autores principales:	Hanxiao Xu, Koki Kitai, Kosuke Minami, Makito Nakatsu, Genki Yoshikawa, Koji Tsuda, Kota Shiba, Ryo Tamura
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/af51a11d728048d5940913b65e38e5ec
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spelling	oai:doaj.org-article:af51a11d728048d5940913b65e38e5ec2021-12-02T17:52:12ZDetermination of quasi-primary odors by endpoint detection10.1038/s41598-021-91210-62045-2322https://doaj.org/article/af51a11d728048d5940913b65e38e5ec2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91210-6https://doaj.org/toc/2045-2322Abstract 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.Hanxiao XuKoki KitaiKosuke MinamiMakito NakatsuGenki YoshikawaKoji TsudaKota ShibaRyo TamuraNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hanxiao Xu Koki Kitai Kosuke Minami Makito Nakatsu Genki Yoshikawa Koji Tsuda Kota Shiba Ryo Tamura Determination of quasi-primary odors by endpoint detection
description	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.
format	article
author	Hanxiao Xu Koki Kitai Kosuke Minami Makito Nakatsu Genki Yoshikawa Koji Tsuda Kota Shiba Ryo Tamura
author_facet	Hanxiao Xu Koki Kitai Kosuke Minami Makito Nakatsu Genki Yoshikawa Koji Tsuda Kota Shiba Ryo Tamura
author_sort	Hanxiao Xu
title	Determination of quasi-primary odors by endpoint detection
title_short	Determination of quasi-primary odors by endpoint detection
title_full	Determination of quasi-primary odors by endpoint detection
title_fullStr	Determination of quasi-primary odors by endpoint detection
title_full_unstemmed	Determination of quasi-primary odors by endpoint detection
title_sort	determination of quasi-primary odors by endpoint detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/af51a11d728048d5940913b65e38e5ec
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Determination of quasi-primary odors by endpoint detection

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