拉曼光谱
食品安全
质量(理念)
直线(几何图形)
食品质量
光谱学
环境科学
材料科学
遥感
地质学
化学
光学
物理
食品科学
几何学
量子力学
数学
作者
Jianwei Qin,Moon S. Kim,Kuanglin Chao,Walter Schmidt,Byoung–Kwan Cho,Stephen R. Delwiche
标识
DOI:10.1016/j.jfoodeng.2016.11.016
摘要
Abstract Both surface and subsurface food inspection is important since interesting safety and quality attributes can be at different sample locations. This paper presents a multipurpose line-scan Raman platform for food safety and quality research, which can be configured for Raman chemical imaging (RCI) mode for surface inspection and spatially offset Raman spectroscopy (SORS) mode for subsurface inspection. In the RCI mode, macro-scale imaging was achieved using a 785 nm line laser up to 24 cm long with a push-broom method. In the SORS mode, a 785 nm point laser was used and a complete set of SORS data was collected in an offset range of 0–36 mm with a spatial interval of 0.07 mm using one CCD exposure. The RCI and SORS modes share a common detection module including a dispersive imaging spectrograph and a CCD camera, covering a Raman shift range from −674 to 2865 cm−1. A pork shoulder and an orange carrot were used to test large-field-of-view (230 mm wide) and high-spatial-resolution (0.07 mm/pixel) settings of the RCI mode for food surface evaluation. Fluorescence-corrected images at selected Raman peak wavenumbers were used to view Raman-active analytes on the whole sample surfaces (e.g., fat on the pork shoulder and carotenoids over the carrot cross section). Also, three layered samples, which were created by placing carrot slices with thicknesses of 2, 5, and 8 mm on top of melamine powder, were used to test the SORS mode for subsurface food evaluation. Raman spectra from carrot and melamine were successfully resolved for all three layered samples using self-modeling mixture analysis. The line-scan Raman imaging and spectroscopy platform provides a new tool for surface and subsurface inspection for food safety and quality.
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