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Food and Supplement Safety Using Data Science and ML Ensuring Quality and Compliance
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Author(s): Pawan Whig (VIPS, India), Balaji Dhamodharan (Independent Researcher, USA), Vijaya Lakshmi Pavani Molli (Independent Researcher, USA)and Pushan Kumar Dutta (Amity University, Kolkata, India)
Copyright: 2024
Pages: 25
Source title:
Nutrition Controversies and Advances in Autoimmune Disease
Source Author(s)/Editor(s): Srikanta Patnaik (SOA University, India & Interscience Institute of Management and Technology, India), Ahmed M. Hamad (Benha University, Egypt), Debjyoti Paul (Amity University, Kolkata, India), Pushan Kumar Dutta (Amity University, Kolkata, India)and Muhammad Shafiq (Guangzhou University, China & Shenyang Normal University, China)
DOI: 10.4018/979-8-3693-5528-2.ch021
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Abstract
Data science is playing a crucial role in enhancing food and supplement safety, ensuring that products meet regulatory standards and are safe for consumption. This chapter explores the application of data science techniques in monitoring and ensuring the safety and quality of food and dietary supplements. The authors examine the methodologies used for data collection, analysis, and predictive modeling to detect contaminants, adulteration, and compliance with safety regulations. The chapter also covers the integration of big data sources, such as laboratory results, consumer feedback, and supply chain data, to provide comprehensive safety assessments. Case studies and real-world applications illustrate how data science can preemptively identify potential safety issues and improve regulatory compliance. This chapter aims to provide a detailed understanding of how leveraging data science can enhance food and supplement safety, thereby protecting public health.
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