Parameter Selection for Raman Spectroscopy-Based Detection of Chemical Contaminants in Food Powders

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<abstract> Raman spectroscopy has proven to be a reliable method for detection of chemical contaminants in food ingredients and products. To detect each contaminant particle in a food sample, it is important to determine the effective depth of penetration of the laser through the food sample and the corresponding laser intensity required for the penetration. It is also important to determine the effective spatial resolution needed to detect each contaminant particle in the mixture. This study examined the depth of penetration of a 785 nm laser through tapioca starch and wheat flour when using three different laser intensities. Melamine, known to exhibit identifiable Raman spectral peaks, was selected as a subsurface reference material for determining the depth of laser penetration through the food powder samples. The food powders were layered in five depths between 1 and 5 mm over a Petri dish packed with melamine. It was observed that the 785 nm laser could penetrate to 3 mm depth in starch; however, the penetration depth was limited to 2 mm in flour. These depths were achieved using laser intensities of 200 and 100 mW for starch and flour, respectively. The selected depth and laser intensity parameters were next used to examine the effective spatial resolution required for detection of maleic acid in starch and benzoyl peroxide in flour, which was selected to be 0.5 mm. Finally, an experiment was conducted to demonstrate the use of these parameters for quantitative Raman imaging-based detection of these contaminants prepared in mixtures at 0.1%, 0.3%, and 0.5% (w/w) concentrations.