Detection of Adulteration in Clarified Butter by Using Machine Learning

Adulterating clarified butter involves adding impurities and subpar substances to pure clarified butter with the intention of increasing the quantity and maximizing profits. Such adulteration in food and other consumable products has a direct impact on human health, compromising the nutritional value of the substance. This study aims to provide a comprehensive analysis of various techniques employed in detecting adulteration in clarified butter. Leveraging the advancements in machine learning technology, the study explores the analysis of existing data collected from different products and laboratories to identify patterns indicative of clarified butter adulteration. The research focuses on quantifiable measures used to determine the level of adulteration in various products, ultimately contributing to a better understanding of machine learning algorithms suitable for detecting adulteration in clarified butter. The findings of this study serve as a foundation for enhancing the existing framework and guiding future research endeavors in the field of machine learning-based detection systems for clarified butter adulteration.