DEVELOPMENT OF NEW AND IMPROVED SYSTEMS TO ENHANCE FOOD SAFETY INSPECTION AND SANITATION OF FOOD PROCESSING
Title: A Raman chemical imaging system for detection of contaminants in food
Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: April 29, 2011
Publication Date: September 14, 2011
Citation: Chao, K., Qin, J., Kim, M.S., Mo, C. 2011. A Raman chemical imaging system for detection of contaminants in food. Proceedings of SPIE. 8027.
Interpretive Summary: Incidents of contaminated milk products and animal feed in recent years have brought increased interest into the development of methods suitable for screening food products and food ingredients for contaminants. In this study, a macro-scale Raman chemical imaging method was developed for acquisition and analysis of sample mixtures of dry milk powder contaminated with melamine, ammonium sulfate, dicyandiamide, and urea at concentrations between 0.2% and 10% by weight. The sample images were analyzed and used to create image maps that demonstrated the detection of the contaminants with visualization of the spatial distribution of the contaminant particles within the sample mixtures. This Raman chemical imaging method shows great promise as a rapid and non-destructive method for detecting the adulteration of food ingredients that pose food safety risks, which wood benifit food processors seeking to ensure the safety and quality of their ingredients, and also regulatory agencies, such as the U.S. Food and Drug Administration, with an interest in enforcing standards of safety and quality.
This study presented a preliminary investigation into the use of macro-scale Raman chemical imaging for the screening of dry milk powder for the prescence of chemical contaminants. Melamine was mixed into dry milk at concentrations (w/w) of 0.2%, 0.5%, 1.0%, 2.0%, 5.0%, 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 of (w/w) of 0.5%, 1.0%, 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. Although further studies are neccessary, macro-scale Raman chemical imaging shows promise for use in detecting contaminants in food ingredients and may also be useful for authentication of food ingredients.