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IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach

Authors :
Gundavarapu Mallikarjuna Rao
Bhavita Mandapati
Sahithi Meesal
Varsha Naidu
Kumar Rakesh
Prasanna Y. Lakshmi
Source :
E3S Web of Conferences, Vol 430, p 01074 (2023)
Publication Year :
2023
Publisher :
EDP Sciences, 2023.

Abstract

Food adulteration refers to the practice of deliberately adding substances to food to increase its volume, weight, or to improve its appearance, texture, or flavor; it is a significant issue that affects the health and safety of consumers. With the increasing demand for food, the risk of contamination and the intentional addition of harmful substances has increased. There are several existing methods for detecting food adulteration, including chemical analysis, microscopy, sensory analysis, etc. While these methods are helpful, they can be time-consuming, labor-intensive, and may not provide Real-time results. Using the Internet of Things (IoT), Machine Learning (ML) can significantly enhance the ability to identify food adulteration.Within this Framework, we are propose a solution to detect food adulteration using IoT and machine learning. The system comprises IoT sensors and devices to gather data on various parameters such as color, pH, gas content, etc. The collected data is fed into machine learning algorithms for preprocessing, analysis, and testing. Any anomalies or deviations from the standard patterns are flagged for further investigation. ML algorithms can continuously learn from the collected data, enabling them to enhance their accuracy and effectiveness over time. By implementing this system, we aim to create a Real-time, data- driven approach to detecting food adulteration, ensuring food safety and quality for consumers by creating a warning system.

Subjects

Subjects :
Environmental sciences
GE1-350

Details

Language :
English, French
ISSN :
22671242
Volume :
430
Database :
Directory of Open Access Journals
Journal :
E3S Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.3a6e663d095f42dba4e47c5e5323cc6a
Document Type :
article
Full Text :
https://doi.org/10.1051/e3sconf/202343001074