From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
Proper chemical analyses of materials in sealed containers are important for quality control purpose. Although it is feasible to detect chemicals at top surface layer, it is relatively challenging to detect objects beneath obscuring surface. This study used spatially offset Raman spectroscopy (SORS) method to detect 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. With increasing offset distance, the fraction of information from the deeper subsurface material increased compared to that from the top surface material. The series of measurements was analyzed to differentiate and identify the top surface and subsurface materials. Containing mixed contributions from the powder and capsule, the SORS of each sample was decomposed using self modeling mixture analysis (SMA) to obtain pure component spectra of each component and corresponding components were identified using spectral information divergence values. Results show that SORS technique together with SMA method has a potential for non-invasive detection of chemicals at deep subsurface layer.
Sagar Dhakal, Kuanglin Chao, Jianwei Qin, Walter F. Schmidt, Moon S. Kim, Diane E. Chan, and Abigail Bae, "Raman spectroscopy method for subsurface detection of food powders through plastic layers," Proc. SPIE 10217, Sensing for Agriculture and Food Quality and Safety IX, 1021706 (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 13, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2262102.
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