Detection of Adulteration in Food Using Recurrent Neural Network with Internet of Things

Abstract

Food is an essential need for human survival. Throughout history, food has been recognised as a crucial need for people in order to maintain good health as well as to treat illness. As with all living things, it is one of the most basic necessities that man has as well as those of all other living creatures. In a recent publication, it was said that an extremely affordable, robust, and biocompatible impedance sensor that serves as a fractional-order element has been created and may be used to distinguish milk and tainted milk. A complete study on milk adulteration includes more than 160 academic articles on the topic. A comprehensive study on milk adulteration is available online. Specifically, the goal of this research is to discover various types of milk adulterants, different approaches for detecting each kind of adulterant, as well as the health hazards associated with milk product adulteration. In the proposed project, the fractional-order element would be investigated for its potential use in the detection of milk adulteration. With this fractional-order element-based impedance sensor, you can distinguish between different types of contaminated milk and different types of faking it, which is quite useful in the detection and differentiation of fake and real milk. According to the researchers, they have created a low-cost, user-friendly instrumentation system for detecting milk adulteration. They hope to commercialise it soon. An automated sensing system for the detection of synthetic milk, based on a microcontroller, has been created in order to reduce the reliance on specialised labour and to improve efficiency. In order to model the sensor, the dipole layer capacitance at the interface of the impedance sensor immersed in milk and the contaminated milk must be taken into account throughout the modelling process. In this study, an electrical equivalent circuit is built, and the correctness of the circuit is shown by both theoretical and experimental investigation. The detection of milk adulteration is classified with the use of Recurrent Neural Networks, and the status is updated in the cloud server with the help of the Internet of Things and Recurrent Neural Networks. It is estimated that the proposed work will have an accuracy rate of 92.31 percent, a sensitivity rate of 75.23 percent, and a specificity rate of 90.12 percent, all of which are higher than the present rate.