Title: Simultaneous detection of multiple adulterants in dry milk using macro-scale Raman chemical imaging Authors
Submitted to: Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 23, 2012
Publication Date: April 1, 2013
Repository URL: http://handle.nal.usda.gov/10113/56645
Citation: Qin, J., Chao, K., Kim, M.S. 2013. Simultaneous detection of multiple adulterants in dry milk using macro-scale Raman chemical imaging. Food Chemistry. 138:998-1007. Interpretive Summary: In recent years, unscrupulous companies were found to have deliberately added unsafe chemical adulterants to dry milk powder to increase its perceived nitrogen content, consequently causing illness and death for consumers who ingested products made with the adulterated ingredient. Incidents such as these have helped increase the interest in development of methods for screening food products for contaminants. In this study, a macro-scale Raman chemical imaging method was developed for simultaneous detection of multiple adulterants in dry milk. Ammonium sulfate, dicyandiamide, melamine, and urea were mixed together into samples of dry milk at equal concentrations, between 0.1% and 5.0% by weight. Raman chemical images were created that visualized the identification and spatial distribution of the multiple adulterant particles in the milk powder samples. This Raman chemical imaging method shows promise for use by food processors and regulatory agencies as a non-destructive method for rapid screening of food ingredients for adulterants that pose food safety risks.
Technical Abstract: The potential of Raman chemical imaging for simultaneously detecting multiple adulterants in milk powder was investigated. Potential chemical adulterants, including ammonium sulfate, dicyandiamide, melamine, and urea, were mixed together into skim dry milk in the concentration range of 0.1–5.0% for each adulterant. A Raman imaging system using a 785-nm laser acquired hyperspectral images in the wavenumber range of 102–2538 cm–1 for a 25×25 mm2 area of each mixture sample was developed, with a spatial resolution of 0.25 mm. Self-modeling mixture analysis (SMA) was used to extract pure component spectra, by which the four types of the adulterants were identified at all concentration levels based on their spectral information divergence values to the reference spectra. Raman chemical images were created using the contribution images from SMA, and their use to effectively visualize identification and spatial distribution of the multiple adulterant particles in the dry milk was demonstrated.