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 10800246475?profile=RESIZE_710xA three-dimensional paper-based microfluidic device has been designed and fabricated to simultaneously detect multiple chemical adulterants in milk using a visual colourimetric indicator. 

It is intended as a quick and cheap screening test for use in developing countries.  

The authors propose that it could be used by consumers to check milk before consumption.

It was shown to detect urea, detergents, soap, starch, hydrogen peroxide, sodium-hydrogen-carbonate, and salt which had been added to milk at concentrations between 0.05% and 0.2% v/v.

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Photo by Eiliv-Sonas Aceron on Unsplash





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4503064731?profile=RESIZE_400xMilk adulteration normally involves dilution with water or whey and adding other nitrogen sources such as ammonium salts, urea, melamine or non-dairy proteins. The established method for detecting added water in milk is to determine its freezing point depression, however, this method would not be effective to detect most milk adulterations. Brazilian researchers have developed a rapid and simple method to screen milk for adulteration, which involves precipitation of the milk proteins with copper sulphate and measuring the intensity of remaining copper salt after complexing with EDTA with a smartphone  and a colorimetric app. The method was tested by adulterating milk with ammonium chloride, urea and melamine, and was able to detect the addition of 1% added water to the milk.

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Urea is added as an adulterant to give milk whiteness and increase its consistency by improving the non-fat solids content, but excessive amounts of urea in milk causes overload and kidney damage. A sensitive method for detecting and quantifying urea adulteration of milk has been developed using FT-NIRS (Fourier Transformed Near Infra Red Spectroscopy) coupled with multivariate analysis. The model was developed using 162 fresh milk samples, consisting of 20 non-adulterated samples (without urea), and 142 samples with the urea adulterant at 8 different concentrations (0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%), each prepared in triplicate. The NIR data coupled with the PLS‐DA (Partial Least Squares -Discriminant Analysis) model can be used to discriminate between the unadulterated fresh milk samples and those adulterated with urea.  Furthermore, the NIR data coupled with PLSR (Partial Least Squares Regression) models may be used to quantify the level of the urea in milk samples. 

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