Submitted to: Journal of Food Measurement & Characterization
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/30/2014
Publication Date: 2/13/2014
Citation: Qin, J., Chao, K., Kim, M.S., Lee, H., Peng, Y. 2014. Development of a Raman chemical imaging detection method for authenticating skim milk powder. Journal of Food Measurement & Characterization. 8(2):122-131. Interpretive Summary: In recent years, there have been incidents where milk powder has been deliberately adulterated for the purpose of improving perceived quality. Such adulterants present a serious food safety hazard. Since traditional detection methods for identification of adulterants are time consuming and costly, there is a need to develop a rapid and accurate method for authentication of milk powder. This study focused on the potential of Raman chemical imaging for detection of multiple adulterants in milk powder. Potential chemical adulterants, including ammonium sulfate, dicyandiamide, melamine, and urea, were mixed together into skim dry milk. A simple image classification method was developed to generate Raman chemical images allowing for the visualization and identification of the multiple adulterant particles in the milk powder at concentrations as low as 0.1%. The detection method developed in this study has the potential to be adapted for future high throughput inspection systems for rapid and accurate authentication of dry food ingredients. The technique would benefit food processors in ensuring the safety and quality of their products/ingredients, as well as regulatory agencies, such as the U.S. Food and Drug Administration and the USDA Food Safety and Inspection Service, with interests in enforcing standards of food safety and quality.
Technical Abstract: This research demonstrated that Raman chemical imaging coupled with a simple image classification algorithm can be used to detect multiple chemical adulterants in skim milk powder. Ammonium sulfate, dicyandiamide, melamine, and urea were mixed into the milk powder as chemical adulterants in the concentration range of 0.1–5.0%. A Raman imaging system with a 785-nm laser was used to acquire hyperspectral images in the wavenumber range of 102–2538 cm–1 for a 25×25 mm2 area of each mixture. An image classification method was developed based on single-band fluorescence-free images at unique Raman peaks of the adulterants. Raman chemical images were created to visualize identification and distribution of the multiple adulterant particles in the milk powder. The detection algorithm developed in this study has the potential to be adapted for a future high-throughput inspection system for rapid and accurate authentication of milk powder and other powdered food and food ingredients.