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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #316828
Title: Depth of penetration of a 785nm laser for Raman spectral measurement in food powders.

Author
item Chao, Kuanglin - Kevin Chao
item DHAKAL, SAGAR - Orise Fellow
item Qin, Jianwei - Tony Qin
item Kim, Moon
item PENG, YANKUN, YANKUN - China Agricultural University
item Schmidt, Walter

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 5/15/2015
Publication Date: 6/18/2015
Citation: Chao, K., Dhakal, S., Qin, J., Kim, M.S., Peng, Yankun, Y., Schmidt, W.F. 2015. Depth of penetration of a 785nm laser for Raman spectral measurement in food powders. Proceedings of SPIE 9488, Sensing for Agriculture and Food Quality and Safety VII, 94880U.

Interpretive Summary: Incidents in recent years have demonstrated that economically motivated adulteration of food products can cause illness and death for consumers. Raman spectroscopy has been demonstrated as rapid, and non-destructive method that can be used for qualitative detection of chemical adulterants in food ingredients, such as melamine in milk powder. However, for effective quantitative assessment of adulterants to determine adulterant concentration in a sample, it is important to measure the depth of penetration of the Raman laser light to ensure that chemical particles at the very bottom of a sample volume can be detected. This study investigates the penetration depth of a 785nm laser into three different food powders, namely dry milk powder, corn starch, and wheat flour, using three different laser intensities (100, 200, and 300 mw) and five thicknesses of food powder (1, 2, 3, 4, and 5 mm) layered atop melamine. Melamine was used as the subsurface reference material for measurement because melamine exhibits known and identifiable Raman spectral peaks. Penetration depth of the laser was evaluated by whether the melamine signals were detectable through the food ingredients. The laser source was able to detect the melamine through up to 3 mm of the food ingredients, but was not able to penetrate through the 4mm or 5 mm layers. The results of this study will be used for further work to develop quantitative analysis models for detection of chemical contaminants in food ingredients.

Technical Abstract: Raman spectroscopy is a useful, rapid, and non-destructive method for both qualitative and quantitative evaluation of chemical composition. However it is important to measure the depth of penetration of the laser light to ensure that chemical particles at the very bottom of a sample volume are detected. The aim of this study was to investigate the penetration depth of a 785nm laser (maximum power output 400mw) into three different food powders, namely dry milk powder, corn starch, and wheat flour. The food powders were layered in 5 depths, between 1 and 5 mm, overtop a Petri dish packed with melamine. Melamine was used as the subsurface reference material for measurement because melamine exhibits known and identifiable Raman spectral peaks. Analysis of the sample spectra for characteristics of melamine and characteristics of milk, starch, and flour allowed determination of the effective penetration depth of the laser light in the samples. Three laser intensities (100, 200 and 300mw) were used to study the effect of laser intensity to depth of penetration. It was observed that 785nm laser source was able to easily penetrate through every point in all three food sample types at the 1mm depth. However, the number of points that the laser could penetrate decreased with increasing depth of the food powder. ANOVA test was carried out to study the significant effect of laser intensity to depth of penetration. It was observed that laser intensity significantly influences the depth of penetration. The outcome of this study will be used in our next phase of study to detect different chemical contaminants in food powders and develop quantitative analysis models for detection of chemical contaminants.