Multi-Odor Discrimination by Rat Sniffing for Potential Monitoring of Lung Cancer and Diabetes
- Authors
- Oh, Yunkwang; Kwon, Ohseok; Min, Sun-Seek; Shin, Yong-Beom; Oh, Min-Kyu; Kim, Moonil
- Issue Date
- 6월-2021
- Publisher
- MDPI
- Keywords
- multi-odor discrimination; 2-choice; no-go; animal biosensor; olfactory behavior
- Citation
- SENSORS, v.21, no.11
- Indexed
- SCIE
SCOPUS
- Journal Title
- SENSORS
- Volume
- 21
- Number
- 11
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/127979
- DOI
- 10.3390/s21113696
- ISSN
- 1424-8220
- Abstract
- The discrimination learning of multiple odors, in which multi-odor can be associated with different responses, is important for responding quickly and accurately to changes in the external environment. However, very few studies have been done on multi-odor discrimination by animal sniffing. Herein, we report a novel multi-odor discrimination system by detection rats based on the combination of 2-Choice and Go/No-Go (GNG) tasks into a single paradigm, in which the Go response of GNG was replaced by 2-Choice, for detection of toluene and acetone, which are odor indicators of lung cancer and diabetes, respectively. Three of six trained rats reached performance criterion, in 12 consecutive successful tests within a given set or over 12 sets with a success rate of over 90%. Through a total of 1300 tests, the trained animals (N = 3) showed multi-odor sensing performance with 88% accuracy, 87% sensitivity and 90% specificity. In addition, a dependence of behavior response time on odor concentrations under given concentration conditions was observed, suggesting that the system could be used for quantitative measurements. Furthermore, the animals' multi-odor sensing performance has lasted for 45 days, indicating long-term stability of the learned multi-odor discrimination. These findings demonstrate that multi-odor discrimination can be achieved by rat sniffing, potentially providing insight into the rapid, accurate and cost-effective multi-odor monitoring in the lung cancer and diabetes.
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Collections - College of Engineering > Department of Chemical and Biological Engineering > 1. Journal Articles
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