Soon, check quality of milk with your phone
TNN | Nov 21, 2018, 05:26 ISTNEW DELHI: Now your mobile phone could help detect milk adulteration. Researchers from Indian Institute of Technology (IIT), Hyderabad are developing a smart phone-based sensor to detect adulteration and as a first step, has developed a sensor-chip based method for measuring pH, an indicator of the acidity. On testing with milk spiked with various combinations of contaminants, they found near-perfect classification with accuracy of 99.71%
According to the research team, led by Prof Shiv Govind Singh, department of electrical engineering, the researchers have used a process called ‘electrospinning’ to produce paper-like material made of nanosised (~10-9 m diameter) fibres of nylon, loaded with a combination of three dyes. The paper is “halochromic”, that is, it changes colour 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.
The research team also comprises of Soumya Jana and Siva Rama Krishna Vanjari, associate professors, IIT, Hyderabad. The research has been published in the November 2018 issue of Food Analytical Methods journal.
According to the research team, led by Prof Shiv Govind Singh, department of electrical engineering, the researchers have used a process called ‘electrospinning’ to produce paper-like material made of nanosised (~10-9 m diameter) fibres of nylon, loaded with a combination of three dyes. The paper is “halochromic”, that is, it changes colour 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.
The research team also comprises of Soumya Jana and Siva Rama Krishna Vanjari, associate professors, IIT, Hyderabad. The research has been published in the November 2018 issue of Food Analytical Methods journal.
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