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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 #327575

Research Project: DEVELOPMENT OF SENSING AND INSTRUMENTATION TECHNOLOGIES FOR FOOD SAFETY AND SANITATION INSPECTION IN FRESH FRUIT AND VEGETABLE PROCESSING

Location: Environmental Microbial & Food Safety Laboratory

Title: Raman spectroscopy-based detection of chemical contaminants in food powders

Author
item Chao, Kuanglin - Kevin Chao
item Dhakal, Sagar - Forest Service (FS)
item Qin, Jianwei - Tony
item Kim, Moon

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 4/15/2016
Publication Date: 5/15/2016
Citation: Chao, K., Dhakal, S., Qin, J., Kim, M.S. 2016. Raman spectroscopy-based detection of chemical contaminants in food powders. Proceedings of SPIE 9864, Sensing for Agriculture and Food Quality and Safety VII, 98640Y-1 - 98640Y 6.

Interpretive Summary: With concerns about food safety and security increasing worldwide, there is an increasing need for reliable, fast, and nondestructive methods of detecting the presence of chemical contaminants in food ingredients. Although many studies have investigated Raman-based spectral methods for qualitative detection such as identifying the presence of melamine in milk powder, the use of Raman methods for quantitative detection has not been fully realized. For effective imaging-based quantitative detection, each contaminant particle in a food sample must be detected; to do so, it is important to determine appropriate imaging parameters such as the necessary spatial resolution, which is influenced by particle size and time considerations, since smaller particles require higher resolution but increased resolution can lengthen image acquisition time. This study investigated the spatial resolution needed for effective detection of contaminants in food powders using a Raman point-scan imaging to acquire Raman spectral images with of two sample mixtures, 1% maleic acid in tapioca starch and 1% benzoyl peroxide in wheat flour, at four different spatial resolutions (0.25mm, 0.5mm, 1.0mm, and 2.0mm). After image analysis determined that a spatial resolution of 0.5mm should be effective, additional mixtures of maleic acid in starch and benzoyl peroxide in flour were then prepared at concentrations of 0.1%, 0.3%, and 0.5% by weight, for Raman spectral imaging at 0.5mm spatial resolution. A linear correlation was found between the detected numbers of contaminant pixels in the sample images and the actual contaminant concentrations, demonstrating that Raman spectral imaging at an appropriately selected spatial resolution can be effectively used for quantitative detection of chemical contaminants in food powders. Raman spectral imaging shows promise as a rapid, less costly and non-destructive alternative to conventional method of quantitative contaminant detection and screening for food powders. This research benefits food processors and distributors.

Technical Abstract: Raman spectroscopy technique has proven to be a reliable method for qualitative detection of chemical contaminants in food ingredients and products. For quantitative imaging-based detection, each contaminant particle in a food sample must be detected and it is important to determine the necessary spatial resolution needed to effectively detect the contaminant particles. This study examined the effective spatial resolution required for detection of maleic acid in tapioca starch and benzoyl peroxide in wheat flour. Each chemical contaminant was mixed into its corresponding food powder at a concentration of 1% (w/w). Raman spectral images were collected for each sample, leveled across a 45 mm x 45 mm area, using different spatial resolutions. Based on analysis of these images, a spatial resolution of 0.5mm was selected as effective spatial resolution for detection of maleic acid in starch and benzoyl peroxide in flour. An experiment was then conducted using the 0.5mm spatial resolution to demonstrate Raman imaging-based quantitative detection of these contaminants for samples prepared at 0.1%, 0.3%, and 0.5% (w/w) concentrations. The results showed a linear correlation between the detected numbers of contaminant pixels and the actual concentrations of contaminant.