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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 #280124
Title: Raman chemical imaging technology for food safety and quality evaluation

Author
item QIN, JIANWEI - University Of Maryland
item Chao, Kuanglin - Kevin Chao
item Kim, Moon

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 3/27/2012
Publication Date: 3/27/2012
Citation: Qin, J., Chao, K., Kim, M.S. 2012. Raman chemical imaging technology for food safety and quality evaluation. BARC Poster Day.

Interpretive Summary:

Technical Abstract: Raman chemical imaging combines Raman spectroscopy and digital imaging to visualize composition and morphology of a target. This technique offers great potential for food safety and quality research. Most commercial Raman instruments perform measurement at microscopic level, and the spatial range cannot be used for whole-surface inspection of individual food items. To achieve macro-scale imaging of large food samples, a Raman chemical imaging platform was developed in our lab. The system uses a 785-nm laser as an excitation source. The detection module consists of a fiber optic probe, a dispersive Raman spectrometer, and a high-performance CCD camera. Spectral and spatial calibrations were performed to the system. The system covers a wavenumber range of 102.2–2538.1 cm–1 with a spectral resolution of 3.7 cm–1, and an area of 127×127 mm2 with a spatial resolution as high as 0.1 mm. System interface software was developed using LabVIEW. Various Raman spectral and image analysis algorithms (e.g., mixture analysis, spectral matching, and image classification) were implemented using MATLAB. This platform is versatile and can be used for safety and quality evaluation of food and agricultural products. Two examples were presented to demonstrate its applications. The first example is authentication of milk powder. Adulterants are purposely added to milk powder to boost the nitrogen content, causing illnesses and deaths for consumers. Potential chemical adulterants, including ammonium sulfate, dicyandiamide, melamine, and urea, were mixed into dry milk in the concentration range of 0.1%–5.0%. Each mixture was imaged in an area of 25×25 mm2 with a spatial resolution of 0.25 mm. Self-modeling mixture analysis was used to extract pure component spectra, by which the four adulterants were identified at all concentration levels based on their spectral similarities to the reference spectra. Raman chemical images were created using the contribution maps from the mixture analysis, and they can be used to visualize identification and distribution of the multiple adulterants in the dry milk. The second example is evaluation of tomato maturity. Harvest at inconsistent maturity stages can discourage tomato consumption due to poor quality and potential food safety risks. Accurate maturity evaluation is thus critical for tomato production. Lycopene is a major carotenoid in tomatoes and detecting its content changes can be used to monitor the ripening of tomatoes. Tomato samples at different ripeness stages were collected and cut open for imaging. A polynomial curve-fitting method was used to correct the underlying fluorescence background in the original spectra. An image classification method was developed based on spectral similarities to identify lycopene in the tomatoes. Raman chemical images were created to illustrate internal lycopene development patterns during the postharvest ripening process, which formed a basis for developing a nondestructive method to evaluate internal maturity of tomatoes.