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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 #327594

Title: Subsurface inspection of food safety and quality using line-scan spatially offset Raman spectroscopy technique

item Qin, Jianwei - Tony Qin
item Chao, Kuanglin - Kevin Chao
item Schmidt, Walter
item DHAKAL, SAGAR - Forest Service (FS)
item CHO, BYOUNG-KWAN - Chungnam National University
item PENG, YANKUN - China Agricultural University
item HUANG, MIN - Jiangnan University
item LEE, HOONSOO - Forest Service (FS)
item Kim, Moon

Submitted to: Food Control
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/7/2016
Publication Date: 5/1/2017
Citation: Qin, J., Chao, K., Schmidt, W.F., Dhakal, S., Cho, B., Peng, Y., Huang, M., Lee, H., Kim, M.S. 2017. Subsurface inspection of food safety and quality using line-scan spatially offset Raman spectroscopy technique. Food Control. 75:246-254.

Interpretive Summary: Current development of optical sensing for food safety and quality inspection focuses largely on spectroscopy and spectral imaging of food surfaces. Subsurface inspection, such as detecting adulterants through packaging and evaluating tissue quality under fruit skin, is challenging due to complex interactions between light and heterogeneous/layered samples. This research presents a newly developed line-scan-based technique of spatially offset Raman spectroscopy (SORS) for subsurface inspection of food and agricultural products. The basic principle of SORS laterally separates a point laser source and a detector, allowing light to travel through a deeper volume of sample material so that the light exiting the surface carries subsurface signals. More flexible and efficient than traditional optical fiber probe methods, line-scan SORS collects a whole series of Raman spectra at once within a broad offset range with narrow spatial interval using only one CCD exposure. A single-scan image contains a complete set of spatially offset spectra with sufficient spatial and spectral information for subsurface evaluation of heterogeneous or layered food samples. In this study, the line-scan SORS method for subsurface food inspection was tested using two samples: granulated cane sugar was overlaid with 1 mm thick plastic, and melamine powder was overlaid with a 5 mm thick carrot slice. The Raman scattering signals of the sugar and the melamine powder were each successfully detected through their respective overlays using line-scan SORS with self-modeling mixture analysis algorithms. The line-scan SORS technique can perform and rapid and nondestructive subsurface inspection of foods, such as authentication of ingredients through packaging, evaluation of internal quality/maturity of fruits and vegetables, and contaminant detection inside volumes of food and agricultural products. The use of this technique would benefit food processors, inspectors, and regulators tasked with ensuring the safety or quality of food products.

Technical Abstract: Subsurface inspection of food and agricultural products is challenging for optical-based sensing techniques due to complex interactions between light and heterogeneous or layered samples. In this study, a method for subsurface food inspection was presented based on a newly developed line-scan spatially offset Raman spectroscopy (SORS) technique. A 785 nm point laser was used as a Raman excitation source. The line-shape SORS data from the sample was collected in a wavenumber range of 0–2815 cm-1 using a detection module consisting of an imaging spectrograph and a CCD camera. Two layered samples, one by placing a 1 mm thick plastic sheet cut from original container on top of cane sugar and the other by placing a 5 mm thick carrot slice on top of melamine powder, were created to test the subsurface food inspection method. For each sample, a whole set of SORS data was acquired using one CCD exposure in an offset range of 0–36 mm (two sides of the incident laser point) with a spatial interval of 0.07 mm. Raman spectra from the cane sugar under the plastic sheet and the melamine powder under the carrot slice were successfully resolved using self-modeling mixture analysis (SMA) algorithms, demonstrating the potential of the technique for authenticating foods and ingredients through packaging and evaluating internal food safety and quality attributes. The line-scan SORS measurement technique provides a rapid and nondestructive method for subsurface inspection of food safety and quality.