Evidence map›Paper›PMID 35986395›Full record

ReviewBiomaterials research2022

Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review.

Chuntae Kim, Kyung Kwan Lee, Moon Sung Kang, Dong-Myeong Shin, Jin-Woo Oh, Chang-Soo Lee, Dong-Wook Han

Abstract readReview
In one paragraph

Review in Biomaterials research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed.

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  7. Adaptive Machine Learning for Electronic Nose-Based Forensic VOC Classification.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Chuntae KimBionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, South Korea.
Kyung Kwan LeeBionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, South Korea.
Moon Sung KangDepartment of Cogno-Mechatronics Engineering, Pusan National University, Busan, 46241, South Korea.
Dong-Myeong ShinDepartment of Mechanical Engineering, The University of Hong Kong, Pokfulam, 999077, Hong Kong, China.
Jin-Woo OhDepartment of Nanoenergy Engineering, Pusan National University, Busan, 46241, South Korea. ojw@pusan.ac.kr.
Chang-Soo LeeBionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, South Korea. cslee@kribb.re.kr.
Dong-Wook HanDepartment of Cogno-Mechatronics Engineering, Pusan National University, Busan, 46241, South Korea. nanohan@pusan.ac.kr.ORCID http://orcid.org/0000-0001-8314-1981

Funding

Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry 421042-04Korea Medical Device Development Fund 20200901_0108Korea Research Institute of Bioscience and Biotechnology KGM5472221National Research Foundation of Korea 2019R1A2C1084687National Research Foundation of Korea 2021R1A2C2006013
6 · The paper itself

Abstract

Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.

Identifiers

PMID35986395
PMCPMC9392354

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.