Rapid detection and quantification of milk adulterants using a nanoclusters-based fluorescent optical tongue

Abstract

A paper-based sensor array consisting of eight nanoclusters (NCs) combined with multivariate analysis was used as a rapid method for the determination of animal sources of milk; goat, camel, sheep and cow. It was also used to detect and quantify three adulterants including sodium hypochlorite, hydrogen peroxide and formaldehyde in milk. The changes in fluorescence intensity of the NCs were quantified using a smartphone when the sensor array was immersed in the milk samples. The device generated a specific colorimetric signature for milk samples from different animals and for different adulterants. This allowed simultaneous identification of animal and adulterant sources with 100% accuracy. The device was found to be capable of accurately measuring the level of contaminants with a detection limit as low as 0.01% using partial least squares regression. In conclusion, a paper-based optical tongue device has been developed for the detection of adulterants in milk with point-of-need capability.