Development of a predictive model for determination of urea in milk using silver nanoparticles and UV-Vis spectroscopy

Determination of urea in milk is inevitable in the dairy industry for the quality control processes. A simple and cost-effective method was developed to determine the concentration of urea in milk by silver nanoparticles (uncapped and citrate capped) and ultraviolet-visible (UV-Vis) spectroscopy. The developed nanoparticles were characterized by UV-Vis, scanning electron microscope (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy and a colour change was noted from yellow to blue upon nanoparticles interaction with urea. A predictive model was developed using the spectral data from the analyzed samples. The developed model was able to measure urea concentration (between 6 mg/dL to 90 mg/dL) in milk with the coefficient of determination (R-2) more than0.90 and was validated through para-dimethylaminobenzaldehyde (DMAB) method suggested by the Food Safety and Standards Authority of India (FSSAI). The average accuracy and sample percent relative standard deviation (% RSD) obtained through the developed method + model ranged from 62.67 to 121.85% and 0.11-1.21%. The regression model for uncapped nanoparticles was found to be best-fitted (having R-2 = 0.92, average accuracy 103.75 +/- 8.69%), with higher closeness to the values obtained for urea by FSSAI-DMAB method. This proposed study proves to be useful for the qualitative and quantitative determination of urea in milk by small businesses in the dairy industry.