Steroid Level Detection in Milk Production

Steroids are now frequently used to increase an animal's productivity in producing milk and meat. Yet these steroids end up in food products and when ingested present themselves as major health hazards to human beings. The misuse of anabolic and corticosteroids in animals causes residues to be above safe levels causing consumers to question the safety of their foods. Following a review of prior studies regarding the identification of steroid residues in animal products, there is a lack of research employing machine learning to generate predictive findings for the safe administration of these substances. This research therefore fills this gap by developing a machine learning model that can predict safe steroid levels in milk. This makes it possible to predict permissible steroid content for animals of a particular type, class, and name by factoring age, weight, and type of steroid on use, to meet required food safety levels. This framework makes scientifically proven recommendations on the use of steroids in animals to ensure that legal steroids are allowed while keeping the health of the public safe. This study also describes the procedures for designing a system which could be installed at a website to avoid risky levels of steroids in milk and provide its consumers with sufficient and harmless levels of steroids.