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

Abstract

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 (R2) 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 R2 = 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.