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United States Department of Agriculture

Agricultural Research Service

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Research Project: Development of Line-Scan Chemical Imaging Techniques for Detection of Food Contaminants and Adulterants

Location: Environmental Microbial & Food Safety Laboratory

2012 Annual Report

1a. Objectives (from AD-416):
To develop line-scan technology and design and construct macro-scale Raman chemical imaging instrumentation, and to develop methodology for macro-scale Raman imaging detection of contaminants and adulterants for food samples and food ingredients.

1b. Approach (from AD-416):
Develop optical method to create laser-line illumination from a 785nm laser source. Implement Raman spectrograph with electron-multiplying charge-coupled device camera. Incorporate line laser and detector into a portable instrument for macro-scale Raman imaging of samples up to 10x20 cm in size. Develop computer programs for real-time data acquisition, data analysis, and classification. Perform system calibration/validation. Investigate contaminant and adulterant detection for food samples such as authentication of dry milk powder or other food ingredients, and detection of pesticide residues on fruits and leafy green vegetables.

3. Progress Report:
A point-source Raman chemical imaging system was developed that uses a 785 nm spectrum-stabilized laser to generate Raman scattering that is then detected through the use of a fiber optic Raman probe, a reflection grating-based Raman imaging spectrometer, and a high performance spectroscopic CCD camera. The system demonstrated effective detection of a combination of four contaminants—ammonium sulfate, dicyandiamide, melamine, and urea—mixed together with dry milk powder at concentrations between 0.1 and 5.0% (w/w). Self-modeling mixture analysis (SMA) was able to extract Raman spectra for each of the four adulterants from spectral image data of the five-constituent sample mixtures. This work demonstrated that multiple contaminants in one sample can be simultaneously detected using Raman chemical imaging method coupled with proper mixture analysis algorithms, allowing identification of adulterant particles and spatial mapping of their distribution. The method shows promise for rapid and non-destructive detection of the adulteration of food ingredients relevant to food safety applications.

4. Accomplishments

Last Modified: 06/27/2017
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