Objective- To develop nanosensors using silver nanoparticles to detect urea in milk and predict its amount using neural networks. Methodology- Synthesis of silver nanoparticles (AgNps) and the development of silver nanoparticles (AgNps) have been done using the green synthesis method. Tea extract(Camelia sinensis) has been used as the reducing agent (bioreductor) due to its presence of antioxidant property(Catechin compounds). Accordingly, AgNps and also citrate-capped AgNps were prepared. These two types of silver nanoparticles were then characterized with the help of UV-Vis Spectroscopy and added to stock samples containing whey protein from milk with and without urea as a contaminant. The results of both citrate-capped AgNps and AgNps were compared and out of these the one that displays better results was chosen to develop the neural network model. Results and Conclusion- The result of this study determined that citrate-capped AgNps are better than AgNps for the detection of urea in milk. For the observation of the optical changes were as such -the peak of absorbance of citrate–capped AgNps shifted from 430nm to 400nm for 1mM of urea in the sample and from 430nm to 350nm for both 2mM and 3mM of urea in the sample. Based on these values the neural network model is developed that is able to predict the amount of urea present in the sample with 89.4% accuracy. Thus, this method shows a new approach for the detection of urea in milk. It can be concluded that AgNps and Citrate-capped AgNps can be used as colorimetric sensors with simple, rapid, and low cost.

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