This study presents the use of Raman chemical imaging for the screening of dry milk powder for the presence of chemical contaminants and Raman spectroscopy for quantitative assessment of chemical contaminants in liquid milk. For image-based screening, melamine was mixed into dry milk at concentrations (w/w) between 0.2% and 10.0%, and images of the mixtures were analyzed by a spectral information divergence algorithm. Ammonium sulfate, dicyandiamide, and urea were each separately mixed into dry milk at concentrations (w/w) between 0.5% and 5.0%, and an algorithm based on self-modeling mixture analysis was applied to these sample images. The contaminants were successfully detected and the spatial distribution of the contaminants within the sample mixtures was visualized using these algorithms. Liquid milk mixtures were prepared with melamine at concentrations between 0.04% and 0.30%, with ammonium sulfate and with urea at concentrations between 0.1% and 10.0%, and with dicyandiamide at concentrations between 0.1% and 4.0%. Analysis of the Raman spectra from the liquid mixtures showed linear relationships between the Raman intensities and the chemical concentrations. Although further studies are necessary, Raman chemical imaging and spectroscopy show promise for use in detecting and evaluating contaminants in food ingredients.
In this research, four chemicals, urea, ammonium sulfate, dicyandiamide, and melamine, were mixed into liquid
nonfat milk at concentrations starting from 0.1% to a maximum concentration determined for each chemical
according to its maximum solubility, and two Raman spectrometers-a commercial Nicolet Raman system and
an in-house Raman Chemical Imaging (RCI) system-were used to acquire Raman shift spectra for these
mixture samples. These chemicals are potential adulterants that could be used to artificially elevate protein
measurements of milk products evaluated by the Kjeldahl method. Baseline subtraction was employed to
eliminate milk intensity, and the normalized Raman intensity was calculated from the specific Raman shift from
the spectrum of solid chemical. Linear relationships were found to exist between the normalized Raman
intensity and chemical concentrations. The linear regression coefficients (R2) ranged from 0.9111 to 0.998.
Although slightly higher R2 values were calculated for regressions using spectral intensities measured by the
Nicolet system compared to those using measurements from the RCI system, the results from the two systems
were similar and comparable. A very low concentration of melamine (400 ppm) in milk was also found to be
detectable by both systems. Raman sensitivity of Nicolet Raman system was estimated from normalized Raman
intensity and slope of regression line in this study. Chemicals (0.2%) were dissolved in milk and detected the
normalized Raman intensity. Melamine was found to have the highest Raman sensitivity, with the highest values
for normalized Raman intensity (0.09) and regression line slope (57.04).
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