BHU researchers discovered a new method for testing milk at home using evaporation

Milk is a crucial part of Indian cuisine, providing essential micronutrients. However, concerns about milk purity and the presence of harmful chemicals are rising due to frequent reports of adulteration across the country.

Contaminated milk can cause health issues like heart disease, diarrhea, central nervous system disorders, and cancer.

The demand for milk and dairy products is high today. Taking advantage of this, some milk traders produce fake milk (synthetic milk) that looks exactly like real milk, and everyday consumers unknowingly purchase this fake milk, which is made from detergent, vegetable oil, urea, and tap water.

This mixture is manipulated to appear creamy and protein-rich, mimicking high-quality milk.

A team of researchers from Sikkim University and Banaras Hindu University have created a novel, low-cost, and DIY method for identifying adulterants in milk by employing a machine learning algorithm and the evaporation process.

The technique helps protect the public’s health as well as the strength and purity of milk. The technique was created by research scholar Tapan Parsian, working with Dr. Ajay Tripathi of Sikkim University, and overseen by Dr. Archana Tiwari of the Department of Physics.

When a drop of milk evaporates, suspended particles move to the edges, forming unique ring patterns. Synthetic milk, created by mixing detergent, vegetable oil, urea, and tap water in the lab, leaves distinct evaporated ring patterns. These patterns, which include multiple rings, visible micro-droplets, and varying transparency, indicate the level of adulteration.

To test milk at home, place a drop on a glass slide, let it evaporate, and photograph the pattern with a smartphone. If the pattern shows signs of synthetic adulteration, such as multiple rings and micro-droplets, it confirms contamination.

The evaporating ring patterns were employed in large-scale investigations to build a machine learning model. With 96.7 percent accuracy, our model correctly identified both pure and synthetic milk, and with 95.8 percent accuracy, it correctly identified water-mixed milk.

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