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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 #338852

Research Project: Sensing Technologies for the Detection and Characterization of Microbial, Chemical, and Biological Contaminants in Foods

Location: Environmental Microbial & Food Safety Laboratory

Title: A spatially offset Raman spectroscopy method for non-destructive detection of gelatin-encapsulated powders

Author
item Chao, Kuanglin - Kevin Chao
item Dhakal, Sagar - Forest Service (FS)
item Qin, Jianwei - Tony
item Schmidt, Walter
item Kim, Moon
item Chan, Diane

Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/16/2017
Publication Date: 3/18/2017
Citation: Chao, K., Dhakal, S., Qin, J., Schmidt, W.F., Kim, M.S., Chan, D.E. 2017. A spatially offset Raman spectroscopy method for non-destructive detection of gelatin-encapsulated powders. Sensors. 17(3):618.

Interpretive Summary: Incidents of pharmaceutical capsule tampering and adulterated health supplements and food powders have illustrated the need to develop non-destructive methods that can evaluate such materials through sealed containers. This study developed a method of using spatially offset Raman spectroscopy (SORS) to detect and characterize encapsulated chemical powders, demonstrating subsurface chemical detection and identification for urea, ibuprofen and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules. SORS uses a laser illumination point on a sample surface to provide light for surface measurements acquired along a series points moving away from the illumination point. Light that has traveled from the illumination point and then through some amount of subsurface material before resurfacing at the measurement point carries a mix of information from the surface and subsurface materials. With increasing distance between points, the fraction of information from the deeper subsurface material increases compared to that from the surface material. The series of measurements can be analyzed to differentiate and identify the surface and subsurface materials. This study demonstrated effective SORS-based chemical detection and identification of encapsulated powders through 0.11 to 0.88 mm total thicknesses of gelatin capsules. With a wide range of applications that includes detection of concealed drugs, quality control evaluation or adulteration/fraud detection of encapsulated drugs and food supplements, and identification or analysis of food constituents or other safety/quality attributes for packaged foods, SORS can thus be used for nondestructive inspection of biological/chemical materials contained in semi-transparent coatings or packaging by processors, distributors, or regulators interested in verifying the safety and quality of food and pharmaceutical products.

Technical Abstract: Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785-nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. As offset distance was increased, the spectral contribution from the subsurface powder gradually outweighed that of the surface capsule layers, allowing for detection of the encapsulated powders. Containing mixed contributions from powder and capsules, the SORS spectra for each sample were resolved into pure component spectra using self modeling mixture analysis (SMA) and the corresponding components were identified using spectral information divergence values. As demonstrated here for detecting chemicals contained inside thick capsule layers, this SORS measurement technique coupled with SMA has the potential to be a reliable non-destructive method for subsurface inspection and authentication of foods, health supplements, and pharmaceutical products that are prepared or packaged with semi-transparent materials.