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A Raman chemical imaging system for detection of contaminants in food

机译：拉曼化学成像系统，用于检测食品中的污染物

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This study presented a preliminary investigation into the use of macro-scale Raman chemical imaging for the screening of dry milk powder for the presence 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 necessary, 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.
机译：这项研究对使用宏观拉曼化学成像技术筛查奶粉中是否存在化学污染物进行了初步研究。将三聚氰胺以0.2％，0.5％，1.0％，2.0％，5.0％和10.0％的浓度（w / w）混合到干奶中，并通过光谱信息散度算法分析混合物的图像。将硫酸铵，双氰胺和尿素分别以0.5％，1.0％和5.0％的浓度分别混合到干奶中，并将基于自建模混合物分析的算法应用于这些样品图像。使用这些算法可以成功检测出污染物，并在样品混合物中观察污染物的空间分布。尽管有必要进行进一步的研究，但是宏观拉曼化学成像显示出有望用于检测食品成分中的污染物，并且也可能对食品成分的鉴定有用。

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