Citation:

Dr. P. Ganesh Kumar, A.Alagammai, B.S.Madhumitha, B.Ishwariya, 2022. "Iot Based Milk Monitoring System for the Detection of Milk Adulteration" ESP Journal of Engineering & Technology Advancements  2(2): 6-9.

Abstract:

Adulteration is an intense issue that poses dangerous health problems to numerous in India. In every items there is degradation. Starting from the everyday food, it progresses toward the life saving prescriptions. The reprobates have not saved even infants milk things. The milk that is adulterated is harmful because it is toxic and it can affect the development and proper growth of human being. For perceiving the risky pieces of milk and consumables, the abstract spectroscopic technique is a prevalent decision. Every one of the known spectroscopic strategies for perceiving milk defilements are research center based and need expensive stuff. This lab based identification consumes quite a while and is expensive, which the regular individual will in all likelihood not be able to pay. To resolve this issue, the milk monitoring identification/checking framework utilizes sensors like pH, conductivity/impedance, and CO2 to anticipate bacterial development and milk weakening. In the real-time detection of milk adulterants, this proposed system implemented using Arduino UNO programmed with neural network classifier (Back Propagation Neural Network) for low cost and accurate milk adulteration testing.

References:

[1] N. Sowmya and V. Ponnusamy, Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning, in IEEE Access, 9 (2021) 53979-53995, doi: 10.1109/ACCESS.2021.3070558.
[2] T. C. Trigoso, J. O. Cruz, H. A. Miñano and W. C. Silupu, Application of Machine Learning in the Discrimination of Citrus Fruit Juices: Uses of Dielectric Spectroscopy, 2020 IEEE Engineering International Research Conference (EIRCON), (2020) 1-4. doi: 10.1109/EIRCON51178.2020.9253756.
[3] S. P. S. Brighty, G. S. Harini and N. Vishal, Detection of Adulteration in Fruits Using Machine Learning, 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), (2021) 37-40. doi: 10.1109/WiSPNET51692.2021.9419402.
[4] M. Chakraborty and K. Biswas, Milk Tester: Simultaneous Detection of Fat Content and Adulteration, in IEEE Transactions on Instrumentation and Measurement, 69(5) (2020) 2468-2476, doi:10.1109/TIM.2019.2955796.
[5] K. A. Ghodinde and U. M. Chaskar, Quantification of Urea Adulteration with Impedance Spectroscopy in Cow Milk, 2021 6th International Conference for Convergence in Technology (I2CT), (2021) 1-5. doi: 10.1109/I2CT51068.2021.9418154.
[6] S. Saravanan, K. M, K. N S, K. N S and N. V I, Smart Milk Quality Analysis and Grading Using IoT, 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), (2021) 378-383. doi: 10.1109/ICICCS51141.2021.9432298.
[7] A. A. Khairurrahman, B. M. Wibawa and D. Hidayat, The Evaluation of Adulteration in Milk Using The Propagation of Ultrasonic Waves, 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), (2021) 1-4, doi: 10.1109/AIMS52415.2021.9466070.
[8] K. Ramakrishnan, S. Singaram, D. Thiyagarajan and S. V. Srinivasan, Design and Development of Electro-Optic System for Quantification of Melamine Concentration in Milk, in IEEE Transactions on Nanotechnology, 20(2021) 810-817, doi: 10.1109/TNANO.2021.3123175.
[9] S. Natarajan and V. Ponusamy, A Review on Quantitative Adulteration Detection in Milk, 2021 Smart Technologies, Communication and Robotics (STCR),2021,pp.14,doi:10.1109/STCR51658.2021.9588800.P. Del Carpio-Concha, A. Nuñez-Flores, R. Acosta- Araníbar and P. Castillo-Araníbar, Microwave Sensor Based on Split Ring Resonators for Dielectric Characterization of Density and Viscosity of Milk, 2020 IEEE MTT-S Latin America Microwave Conference (LAMC 2020), (2021) 1-4. doi: 10.1109/LAMC50424.2021.9601580.
[10] T. M. Amado et al., Development of Predictive Models using Machine Learning Algorithms for Food Adulterants Bacteria Detection, 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2019)1-6. doi: 10.1109/HNICEM48295.2019.9072907.

Keywords:

Back Propagation Neural Network, Iot, Milk Adulteration.