Food and Supplement Safety Using Data Science and ML Ensuring Quality and Compliance

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.