TY - GEN
T1 - Classification and Gas Concentration Measurements of Human Axillary Odor using Electronic Nose
AU - Sabilla, Shoffi Izza
AU - Malikhah,
AU - Sarno, Riyanarto
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Human axillary odor produces gas from sweat which concentration will change depends on the activities and metabolism in the body. Sweat concentration can be used as information to determine body health. Nowadays, e-nose is widely used in medicine, food industry, agriculture, and biotechnology. An electronic nose (e-nose) is a device that mimics how the human nose works. This paper will build an e-nose with seven sensors from Figaro Taguchi series (TGS) sensors and one sensor from humidity and temperature sensors (SHT-15 series). The e-nose was used to obtain the human axillary odor in the morning, afternoon, and evening. Several classifiers are used in the classification process and the result showed that Random Forest with tuned hyperparameter produced the best result with an accuracy of 87.43%. By using the ANOVA f-test, it is showed that methane and ethanol from sensor TGS 2612 are the most significant gas in the classification process. The experimental result showed that human axillary odor produced different ethanol and methane gas concentration in the morning, afternoon, and evening.
AB - Human axillary odor produces gas from sweat which concentration will change depends on the activities and metabolism in the body. Sweat concentration can be used as information to determine body health. Nowadays, e-nose is widely used in medicine, food industry, agriculture, and biotechnology. An electronic nose (e-nose) is a device that mimics how the human nose works. This paper will build an e-nose with seven sensors from Figaro Taguchi series (TGS) sensors and one sensor from humidity and temperature sensors (SHT-15 series). The e-nose was used to obtain the human axillary odor in the morning, afternoon, and evening. Several classifiers are used in the classification process and the result showed that Random Forest with tuned hyperparameter produced the best result with an accuracy of 87.43%. By using the ANOVA f-test, it is showed that methane and ethanol from sensor TGS 2612 are the most significant gas in the classification process. The experimental result showed that human axillary odor produced different ethanol and methane gas concentration in the morning, afternoon, and evening.
KW - ANOVA
KW - Axillary odor
KW - Electronic nose
KW - Ethanol
KW - Methane
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85123297259&partnerID=8YFLogxK
U2 - 10.1109/ICTS52701.2021.9608597
DO - 10.1109/ICTS52701.2021.9608597
M3 - Conference contribution
AN - SCOPUS:85123297259
T3 - Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
SP - 161
EP - 166
BT - Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Information and Communication Technology and System, ICTS 2021
Y2 - 20 October 2021 through 21 October 2021
ER -
