TY - GEN
T1 - Improvement of E-Nose Sensor Signal Using MVA, FFT, DWT Methods on Pineapple Fruit Maturity
AU - Hasan, Mhd Arief
AU - Sarno, Riyanarto
AU - Ardani, M. Syauqi Hanif
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this study, we built an Electronic Nose to detect pineapple ripeness. We designed a prototype E-Nose using a wooden crate. Inside the box, we built the E-Nose using 9 MQ sensor circuits connected to the Arduino Microcontroller. In the crate, we put an object, namely a pineapple with three levels of ripeness (ripe, half ripe, and unripe), each weighing 1kg placed in three different positions (5cm, 20cm, and 35cm) from the position of the sensor array (E-nose). We converted the signal output results into ppm units. We compared the value of each fruit signal based on the level of ripeness and distance using E-Nose. We used several signal processing methods (wavelets) for signal processing. Then, we improved the signal generated using wavelet. The wavelet methods used are Moving Average, Fast Fourier Transform (FFT), and Discrete Wavelet Transform (DWT). The contribution of this research is to see the influence of the signal valuegenerated from the sensor (MQ Sensor) to the position (distance) of the object (fruit) and its influence on the level of maturity (fruit). Then signalgenerated from the sensor we make improvements with the mva, fft, and dwt methods.
AB - In this study, we built an Electronic Nose to detect pineapple ripeness. We designed a prototype E-Nose using a wooden crate. Inside the box, we built the E-Nose using 9 MQ sensor circuits connected to the Arduino Microcontroller. In the crate, we put an object, namely a pineapple with three levels of ripeness (ripe, half ripe, and unripe), each weighing 1kg placed in three different positions (5cm, 20cm, and 35cm) from the position of the sensor array (E-nose). We converted the signal output results into ppm units. We compared the value of each fruit signal based on the level of ripeness and distance using E-Nose. We used several signal processing methods (wavelets) for signal processing. Then, we improved the signal generated using wavelet. The wavelet methods used are Moving Average, Fast Fourier Transform (FFT), and Discrete Wavelet Transform (DWT). The contribution of this research is to see the influence of the signal valuegenerated from the sensor (MQ Sensor) to the position (distance) of the object (fruit) and its influence on the level of maturity (fruit). Then signalgenerated from the sensor we make improvements with the mva, fft, and dwt methods.
KW - DWT
KW - E-Nose
KW - FFT
KW - MVA
KW - Pineapple
UR - http://www.scopus.com/inward/record.url?scp=85150436169&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE57756.2022.10057787
DO - 10.1109/ICITISEE57756.2022.10057787
M3 - Conference contribution
AN - SCOPUS:85150436169
T3 - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
SP - 766
EP - 771
BT - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Y2 - 13 December 2022 through 14 December 2022
ER -