Raman spectroscopy and imaging to detect contaminants for food safety applications

Kuanglin Chao; Jianwei Qin; Moon S. Kim; Yankun Peng; Diane Chan; Yu-Che Cheng

doi:10.1117/12.2018616

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29 May 2013 Raman spectroscopy and imaging to detect contaminants for food safety applications

Kuanglin Chao, Jianwei Qin, Moon S. Kim, Yankun Peng, Diane Chan, Yu-Che Cheng

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Proceedings Volume 8721, Sensing for Agriculture and Food Quality and Safety V; 87210S (2013) https://doi.org/10.1117/12.2018616
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

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.

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Kuanglin Chao, Jianwei Qin, Moon S. Kim, Yankun Peng, Diane Chan, and Yu-Che Cheng "Raman spectroscopy and imaging to detect contaminants for food safety applications", Proc. SPIE 8721, Sensing for Agriculture and Food Quality and Safety V, 87210S (29 May 2013); https://doi.org/10.1117/12.2018616

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