IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach
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 contaminati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01074.pdf |
| _version_ | 1797658693426216960 |
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| author | Gundavarapu Mallikarjuna Rao Bhavita Mandapati Sahithi Meesal Varsha Naidu Kumar Rakesh Prasanna Y. Lakshmi |
| author_facet | Gundavarapu Mallikarjuna Rao Bhavita Mandapati Sahithi Meesal Varsha Naidu Kumar Rakesh Prasanna Y. Lakshmi |
| author_sort | Gundavarapu Mallikarjuna Rao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T18:03:02Z |
| format | Article |
| id | doaj.art-3a6e663d095f42dba4e47c5e5323cc6a |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-11T18:03:02Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-3a6e663d095f42dba4e47c5e5323cc6a2023-10-17T08:47:38ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014300107410.1051/e3sconf/202343001074e3sconf_icmpc2023_01074IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart ApproachGundavarapu Mallikarjuna Rao0Bhavita Mandapati1Sahithi Meesal2Varsha Naidu3Kumar Rakesh4Prasanna Y. Lakshmi5Gokaraju Rangaraju Institute of Engineering and Technology, CSE DepartmentGokaraju Rangaraju Institute of Engineering and Technology, CSE DepartmentGokaraju Rangaraju Institute of Engineering and Technology, CSE DepartmentGokaraju Rangaraju Institute of Engineering and Technology, CSE DepartmentUttaranchal Institute of Management, Uttaranchal UniversityGokaraju Rangaraju Institute of Engineering and Technology, CSE DepartmentFood 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01074.pdf |
| spellingShingle | Gundavarapu Mallikarjuna Rao Bhavita Mandapati Sahithi Meesal Varsha Naidu Kumar Rakesh Prasanna Y. Lakshmi IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach E3S Web of Conferences |
| title | IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach |
| title_full | IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach |
| title_fullStr | IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach |
| title_full_unstemmed | IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach |
| title_short | IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach |
| title_sort | iot powered intelligent framework for detecting food adulteration a smart approach |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01074.pdf |
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