Researchers at BHU have developed a new method for testing milk at home through evaporation
To validate the method, synthetic milk was created in the lab by combining suitable amounts of detergent, regular vegetable oil, urea, and tap water to produce a mixture similar to real milk. This synthetic milk was then blended with real milk in different proportions. The evaporated ring patterns of the synthetic milk were subsequently analyzed.
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 high demand for milk and dairy products has led some traders to produce synthetic milk, which closely resembles real milk. Consumers often 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.
Researchers from Banaras Hindu University and Sikkim University have developed a cost-effective, home-based method to detect milk adulteration using the evaporation process and machine learning. This method ensures milk quality and protects public health. Led by Tapan Parsian under Dr. Archana Tiwari’s supervision, and in collaboration with Dr. Ajay Tripathi, this method analyzes evaporated milk drop patterns to identify adulterants.
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.
In large-scale analyses, the evaporated ring patterns were used to train a machine-learning model. This model accurately identified pure and synthetic milk with 96.7 percent accuracy and water-mixed milk with 95.8 percent accuracy.
Adulterated milk can cause heart disease, diarrhea, central nervous system disorders, and cancer.
Synthetic milk is made from detergent, vegetable oil, urea, and tap water, manipulated to appear creamy and protein-rich.
Researchers developed a cost-effective, home-based method using the evaporation process and machine learning to detect adulterants in milk.
Consumers can place a drop of milk on a glass slide, let it evaporate, and photograph the pattern to check for signs of adulteration.
The machine-learning model detected pure and synthetic milk with 96.7% accuracy and water-mixed milk with 95.8% accuracy.
