ARTIFICIAL NEURAL NETWORK BASED ULTRASONIC SENSOR SYSTEM FOR DETECTION OF ADULTERATION IN EDIBLE OIL
Tony George1*, Zachariah C Alex2
1Adi Shankara Institute of Engineering and Technology, Kalady, Kerala, India.
2Vellore Institute of Technology, Vellore, Tamilnadu, India.
This paper presents the design, development and experimental validation of an ultrasonic sensor
system for the detection of adulteration in edible oil. Variation of ultrasonic wave propagation
characteristics like attenuation coefficient, reflection coefficient and velocity of propagation in pure and
adulterated oil were used for developing the algorithm to detect the adulteration. Measurement cell was
designed for operating ultrasonic transducer at 1 MHz using COMSOL 4.4. Artificial Neural Network
(ANN) based algorithm was also developed for improving the efficiency of the sensor system. It is found
that this system can detect adulteration with an accuracy of 99.53% for sunflower oil added in pure
coconut oil, whereas 98.82% for palm oil added in pure coconut oil.
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