Simple test to detect adulteration in milk devised

IIT-H researchers have developed a nano-sized paper that gives an accurate reading of the acidity in milk by changing colour.

The simple test is a big step in the quest to develop smartphone based sensors to detect adulteration in milk, say the team from the Indian Institute of Technology, Hyderabad (IIT-H).

The nano-sized (as thin as human hair) indicator paper is a chip based detector system. It is made of fibres of nylon loaded with a combination of three types of dyes. Called halochromatic, it is designed to change colour with the acidity of milk.

Supplementing this, the team also developed algorithms that can be incorporated on to a mobile phone to accurately detect and interpret the colour change.

The research has been done by a team led by Shiv Govind Singh, Department of Electrical Engineering, with Soumya Jana and Siva Rama Krishna Vanjari and others.

It has been published in the November 2018 issue of Food Analytical Methods journal.

Adulteration of milk is a serious problem in India. A recent report by the Animal Welfare Board shows that 68.7 per cent of milk and milk by-products in the country are adulterated with products such as detergent, glucose, urea, caustic soda, white paint and oil. Chemicals such as formalin, hydrogen peroxide, boric acid and antibiotics could also be added to milk to increase shelf life.

How the system works

According to Govind Singh, “While techniques such as chromatography and spectroscopy can be used to detect adulteration, they generally require expensive set-up and are not amenable to miniaturization into low-cost easy-to-use devices. Hence, they do not appeal to the vast majority of milk consumers in the developing world.” Therefore, there is a need to develop simple and cost effective devices.

In the prototype, the sensor strips are dipped in milk and the colour change is captured using a smartphone’s camera. This data is transformed into pH (acidity) ranges using algorithms.

The scientists used three machine-learning algorithms to analyse their detection efficiencies in classifying the colour of the indicator strips. On testing with milk spiked with various combinations of contaminants, they found near-perfect classification with accuracy of 99.71per cent.

The next step is to extend the above research to study the effects of mobile phone cameras and lighting on detection efficiency.

Just the start

In the long run, Govind Singh’s research group seeks to detect contamination by sensing changes in the biophysical properties of milk. Some common biophysical properties that change because of adulterants are acidity, electrical conductivity and refractive index (passage of light through material).

For example, the addition of detergent, caustic soda or boric acid can make the milk more or less acidic than it should be. The addition of urea can change the electrical conductivity of milk. The addition of sugar, water and urea has been shown to alter the refractive index of milk.

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