Indian Institute of Technology at Hyderabad is in the process developing Smart Phone-based sensors to detect adulteration in milk. As a first step, researchers have 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 onto a mobile phone to accurately detect the color change.

The research, undertaken by a team led by Prof. Shiv Govind Singh, Department of Electrical Engineering, IIT Hyderabad and comprising Dr. Soumya Jana and Dr. Siva Rama Krishna Vanjari, Associate Professors  and others, has been published in the November 2018 issue of Food Analytical Methods journal.

Speaking about the importance of this research, Prof. Shiv Govind Singh said, ÂWhile techniques such as chromatography and spectroscopy can be used to detect adulteration, such techniques generally require expensive setup and are not amenable to miniaturisation into low-cost easy-to-use devices. Hence, they do not appeal to the vast majority of milk consumers in the developing world.Â

Prof. Shiv Govind Singh further added, ÂWe 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.Â

As a first step, the research team has developed a sensor-chip based method for measuring pH, an indicator of the acidity. The researchers have used a process called Âelectrospinning to produce paper-like material made of nanosized (~10-9 m diameter) fibres of nylon, loaded with a combination of three dyes. The paper is ÂhalochromicÂ, that is, it changes color in response to changes in acidity.

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%.

Adulteration of milk is a serious problem in India. 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.