TY - GEN
T1 - Indoor Beef Quality Identification Using Gas Sensor Array and Probabilistic Neural Network Method
AU - Amalia, Aslikha
AU - Rivai, Muhammad
AU - Purwanto, Djoko
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Beef is one of the foods most consumed by humans. However, rotten beef is often found in markets. This indicates the omission of the rotten beef, which is still stored in the warehouse. Rotten beef can release metabolic products such as ammonia (NH3), hydrogen sulfide (H2S), and volatile organic compounds (VOC). This study has developed an electronic nose system that can identify the quality of beef indoors. This system uses the MQ-137, MQ-136, and TGS2602 gas sensors. However, the airflow in the room can cause a disturbance in the concentration of the gas, making the sensor's response unstable. Therefore, a probabilistic neural network (PNN) is employed to identify beef quality. The experimental results show that this method can identify the quality of fresh, spoiled, and rotten beef with a success rate of 94.9%.
AB - Beef is one of the foods most consumed by humans. However, rotten beef is often found in markets. This indicates the omission of the rotten beef, which is still stored in the warehouse. Rotten beef can release metabolic products such as ammonia (NH3), hydrogen sulfide (H2S), and volatile organic compounds (VOC). This study has developed an electronic nose system that can identify the quality of beef indoors. This system uses the MQ-137, MQ-136, and TGS2602 gas sensors. However, the airflow in the room can cause a disturbance in the concentration of the gas, making the sensor's response unstable. Therefore, a probabilistic neural network (PNN) is employed to identify beef quality. The experimental results show that this method can identify the quality of fresh, spoiled, and rotten beef with a success rate of 94.9%.
KW - PNN
KW - beef quality
KW - food
KW - gas sensors
UR - http://www.scopus.com/inward/record.url?scp=85193852831&partnerID=8YFLogxK
U2 - 10.1109/AIMS61812.2024.10513153
DO - 10.1109/AIMS61812.2024.10513153
M3 - Conference contribution
AN - SCOPUS:85193852831
T3 - International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024
BT - International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024
Y2 - 22 February 2024 through 23 February 2024
ER -