A Versatile Odor Detection System Based on Automatically Trained Rats for Chemical Sensing

[Display omitted] We report a versatile odor detection system that employs rats trained through automated operant conditioning paradigms. While detection animals possess remarkable olfactory capabilities, their practical use has been limited by non-automated training methods, which involve lengthy t...

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Bibliographic Details
Published inJournal of industrial and engineering chemistry (Seoul, Korea) Vol. 131; pp. 400 - 409
Main Authors Oh, Yunkwang, Kim, Miha, Seok Kwon, Oh, Seek Min, Sun, Shin, Yong-Beom, Kim, Keekwang, Oh, Min-Kyu, Kim, Moonil
Format Journal Article
LanguageEnglish
Published Elsevier B.V 25.03.2024
Subjects
automated system
DNT
gas sensor
odor
Olfactory detection
automated system
gas sensor
odor
DNT
Olfactory detection
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Summary:[Display omitted] We report a versatile odor detection system that employs rats trained through automated operant conditioning paradigms. While detection animals possess remarkable olfactory capabilities, their practical use has been limited by non-automated training methods, which involve lengthy training periods, high costs, handler dependency, and low reliability. Our primary research goal was to develop detection animals using a fully automated system. To achieve this, we employed four distinct operant conditioning approaches to train four rats (Numbers 3, 7, 10, and 12) in an automated apparatus for detecting 2,4-dinitrotoluene (DNT). Our system performed exceptionally well, with DNT-trained rats achieving a 95% accuracy rate, 99% sensitivity, 91% specificity, 92% positive predictive value (PPV), and 99% negative predictive value (NPV) across 380 tests. Additionally, we observed a linear decrease in response time as DNT concentration increased from 20 parts per billion (ppb) to 1000 ppb, indicating the system's potential for quantitative odor concentration measurement. Impressively, the rats retained their odor discrimination skills for up to four months after their last training session, underscoring the durability of their olfactory memory. Our study introduces a novel, highly effective system for specific odorant component detection, offering a faster, more reliable, and accurate method for distinguishing between various odors.
ISSN:1226-086X
DOI:10.1016/j.jiec.2023.10.042