  We present results of Raman spectroscopic studies of urine to determine the
suitability of near-infrared Raman spectroscopy for quantitative estimation of
urinary urea. The Raman spectra were acquired from the urine samples with an
inbuilt Raman spectroscopy setup that employs a 785-nm diode laser as the Raman
excitation source. A multivariate algorithm based on partial least square (PLS)
regression was developed to predict the concentration of urea depending on the
measured sets of Raman spectra and the reference urea concentration. The
computed results shows that Raman spectroscopy in amalgamation with PLS-based
multivariate chemometric algorithm can detect urea in urine samples with an
accuracy of >90 %
