TY - GEN
T1 - Fish Quality Recognition using Electrochemical Gas Sensor Array and Neural Network
AU - Rivai, Muhammad
AU - Misbah,
AU - Attamimi, Muhammad
AU - Firdaus, Muhammad Hamka
AU - Tasripan,
AU - Tukadi,
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Identification of the fish quality is needed to determine the level of freshness so that it can be consumed safely. Usually, the recognition of the fish quality through physical and odor examination by humans. This can be dangerous because spoiled fish produces poisonous gas and a pungent odor from the metabolic processes of microorganisms. This study has developed a tool for recognition of the fish quality using an electrochemical gas sensor array and a Neural Network algorithm. The electrochemical gas sensor consists of amperometric and conductometric types. This sensor data is then fed to the Neural Network algorithm which is implemented in the Arduino Due microcontroller. The experimental results show that the fish quality produces a different sensor response. The more fish decay, the greater the sensor response. This system can recognize the fish quality including fresh, half-fresh, and rotten with a success rate of 80%.
AB - Identification of the fish quality is needed to determine the level of freshness so that it can be consumed safely. Usually, the recognition of the fish quality through physical and odor examination by humans. This can be dangerous because spoiled fish produces poisonous gas and a pungent odor from the metabolic processes of microorganisms. This study has developed a tool for recognition of the fish quality using an electrochemical gas sensor array and a Neural Network algorithm. The electrochemical gas sensor consists of amperometric and conductometric types. This sensor data is then fed to the Neural Network algorithm which is implemented in the Arduino Due microcontroller. The experimental results show that the fish quality produces a different sensor response. The more fish decay, the greater the sensor response. This system can recognize the fish quality including fresh, half-fresh, and rotten with a success rate of 80%.
KW - Electrochemical gas sensor array
KW - Fish quality
KW - Neural Network
UR - http://www.scopus.com/inward/record.url?scp=85084445906&partnerID=8YFLogxK
U2 - 10.1109/CENIM48368.2019.8973369
DO - 10.1109/CENIM48368.2019.8973369
M3 - Conference contribution
AN - SCOPUS:85084445906
T3 - 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding
BT - 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019
Y2 - 19 November 2019 through 20 November 2019
ER -