Indian Institute of Technology (IIT) Hyderabad researchers are developing a Smart Phone-based sensoring system to detect adulteration in milk. As a first step, the IIT Hyderabad team has developed a detector system to measure the acidity of milk through design of an indicator paper that changes color according to the acidity of the milk. They have also developed algorithms that can be incorporated on to a mobile phone to accurately detect the color change.
The study results have been published in the November 2018 issue of Food Analytical Methods journal.
The research was undertaken by a team led by Prof Shiv Govind Singh, Department of Electrical Engineering, IIT Hyderabad.
The team is also comprised of Dr Soumya Jana and Dr Siva Rama Krishna Vanjari, Associate Professors in the Department of Electrical Engineering, IIT Hyderabad and others.
“While techniques such as chromatography and spectroscopy can be used to detect adulteration, such techniques generally require expensive setup 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,” Prof Singh talked about the study.
Further, Prof Singh added, “We need to develop simple devices that the consumer can use to detect milk contamination. It should be possible to make milk adulteration detection failsafe by monitoring all of these parameters at the same time, without the need for expensive equipment.”
The researchers have used a process called ‘electrospinning’ to produce paper-like material made of nanosized fibres of nylon, loaded with a combination of three dyes, said a statement from the Institute.
The paper is “halochromic”, that is, it changes color in response to changes in acidity, the statement added.
The Researchers have developed a prototype smart phone-based algorithm, in which, the colours of the sensor strips after dipping in milk are captured using the camera of the phone, and the data is transformed into pH (acidity) ranges.
They have used three machine-learning algorithms and compared 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 99.71%.
A recent report by the Animal Welfare Board shows that 68.7 % 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.