# Automated odor-blending with one-pot Bayesian optimization

https://mdr.nims.go.jp/datasets/13253e9a-cfd2-4f40-999a-03f6ef2bd606

## File

- [d3dd00215b.pdf](https://mdr.nims.go.jp/filesets/67de3765-19bd-4b66-a086-5b38ae2c96c2/download) ([Detail](https://mdr.nims.go.jp/filesets/67de3765-19bd-4b66-a086-5b38ae2c96c2.md))

## Id

13253e9a-cfd2-4f40-999a-03f6ef2bd606

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-06-07T20:21:07.726967Z

## Updated at

2024-06-10T03:30:19.157928Z

## Published at

2024-06-10T03:30:19.491262Z

## Doi



## First published url

https://doi.org/10.1039/d3dd00215b

## Date published

2024-04-16

## Recorded date published

2024-5-15

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Automated odor-blending with one-pot Bayesian optimization
  title_type: original
  lang: en

## Description

- description: The creation of new odors by blending existing ones is usually done
    manually based on the human sense. To enable robots to perform this automatically,
    we developed an automated odor-blending system. In this system, an olfactory sensor
    system composed of an array of Membrane-type Surface stress Sensors (MSSs) performs
    odor measurement of a blended liquid, and Bayesian optimization controls the blending
    concentration. The actual blending of the liquid samples is performed by automated
    syringe pumps. Our system performs odor-blending by injecting liquid samples into
    a pot or by draining some of the liquid from the pot. The one-pot strategy has
    the advantage of reducing the amount of liquid samples used in the entire optimization
    task and reduces the problem of pot replacement. To implement this one-pot strategy
    effectively, a Drainable One-Pot Bayesian Optimization (DOPBO) algorithm was developed
    and applied to our system. The system was tested using a ternary liquid mixture.
  description_type: abstract
  lang: und

## Creator

- name: Yota Fukui
  role: author
- name: Kosuke Minami
  role: author
  orcid: https://orcid.org/0000-0003-4145-1118
  organization: National Institute for Materials Science
- name: Kota Shiba
  role: author
  orcid: https://orcid.org/0000-0001-7775-0318
  organization: National Institute for Materials Science
- name: Genki Yoshikawa
  role: author
  orcid: https://orcid.org/0000-0002-9136-8964
  organization: National Institute for Materials Science
- name: Koji Tsuda
  role: author
  orcid: https://orcid.org/0000-0002-4288-1606
  organization: National Institute for Materials Science
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Royal Society of Chemistry (RSC)

## Managing organization



## Keyword

- subject: Machine learning
  schema: not_defined
- subject: MSS
  schema: not_defined
- subject: Bayesian optimization
  schema: not_defined
- subject: Olfactory sensor
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Digital Discovery
  issn: 2635098X
  volume: '3'
  issue: '5'
  start_page: 969
  end_page: 976

## Conference



## Related item



## Funding

- identifier: 21H01008
  funder_name: Japan Society for the Promotion of Science
- identifier: 21H01971
  funder_name: Japan Society for the Promotion of Science
- identifier: 21K18859
  funder_name: Japan Society for the Promotion of Science
- identifier: 22K05324
  funder_name: Japan Society for the Promotion of Science
- identifier: JP19KK0141
  funder_name: Japan Society for the Promotion of Science

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## Chemical composition



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## Fileset

- id: 67de3765-19bd-4b66-a086-5b38ae2c96c2
  filename: d3dd00215b.pdf
  content_type: application/pdf
  size: 2519040
  md5: 493efb9ed81d409af9a2c3ba0946602d

## Thumbnail

fileset_id: 67de3765-19bd-4b66-a086-5b38ae2c96c2
filename: d3dd00215b.pdf