Predicting food adulterants in milk using Support Vector Machine (SVM)

Prakash R *, Shantha kumar S kapse, Umar Ameen and Chandan Hegde

Department of MCA Surana College (Autonomous) Bangalore, Karnataka, India.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(01), 2257–2268.
Article DOI: 10.30574/ijsra.2024.13.1.1848
Publication history: 
Received on 19 August 2024; revised on 02 October 2024; accepted on 05 October 2024
 
Abstract: 
Milk adulteration is a significant global issue, particularly in emerging nations, where inadequate monitoring and unhygienic conditions prevail. The adulteration of milk with various chemicals, such as urea, water, skimmed milk powder, sugar, and detergent, poses serious health risks, including heart problems, diarrhea, CNS disorders, irritation, and gastrointestinal disorders. Traditional detection methods are labor-intensive and require sophisticated equipment, which limits their practical application. This study aims to develop a machine learning-based approach to detect milk adulteration using attributes like Solids- Not-Fat (SNF), fat, Corrected Lactometer Reading (CLR), Total Solids (TS), temperature, and protein content. Various machine learning models were employed and evaluated for their performance, including Logistic Regression, Decision Trees, SVM, and Random Forests. The findings demonstrate that machine learning can effectively identify adulteration types, providing a foundation for the dairy industry’s practical and automated detection systems. This research comprehensively reviews common milk adulterants and highlights advanced detection methods to ensure milk quality and safety.
 
Keywords: 
SVM; Solids-Not-Fat (SNF);  Corrected Lactometer Reading (CLR); Total Solids (TS); Temperature; Protein content
 
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