Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #351295

Research Project: Sensing Technologies for the Detection and Characterization of Microbial, Chemical, and Biological Contaminants in Foods

Location: Environmental Microbial & Food Safety Laboratory

Title: Detection of azo dyes in curry powder using a 1064-nm dispersive hyperspectral Raman imaging system

Author
item Dhakal, Sagar - Us Forest Service (FS)
item Chao, Kuanglin - Kevin Chao
item Schmidt, Walter
item Qin, Jianwei - Tony
item Kim, Moon
item Huang, Qing - Anhui Agricultural University

Submitted to: Applied Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/27/2018
Publication Date: 4/5/2018
Citation: Dhakal, S., Chao, K., Schmidt, W.F., Qin, J., Kim, M.S., Huang, Q. 2018. Detection of azo dyes in curry powder using a 1064-nm dispersive hyperspectral Raman imaging system. Applied Sciences. 8(4):564.

Interpretive Summary: Curry powder is a mixture of spices such as turmeric, cumin, coriander, cardamom, which have valuable benefits to human health. Economically motivated contamination of curry powder by hazardous color dyes illustrates the need to develop a nondestructive method for rapid detection of chemical contaminants in curry powder. ARS researchers at the Environmental Microbial and Food Safety Laboratory in Beltsville, MD, have recently developed a new 1064 nm Raman spectral imaging system to measure food powders which emit high fluorescence. This study uses the 1064 nm Raman spectral imaging system to detect hazardous color dyes in curry powder. Curry powder-metanil yellow and curry powder-Sudan I mixture samples were prepared at 1%, 3%, 5%, 7%, and 10% concentration levels. A Raman spectral image of each sample was measured covering the sample surface area of 25 mm x 25 mm. The system could detect the chemical contaminants in curry powder at concentrations as low as 1%. The 1064 nm Raman spectral imaging system was further used for simultaneous detection of multiple chemical contaminants in the curry powder. This 1064 nm Raman imaging-based technique shows promise to authenticate the food powders with high fluorescence. Given the widespread distribution of many powdered ingredients through food processing supply lines nationally and worldwide, this research will benefit food processors and food safety regulators seeking to ensure safety and quality of ingredients ultimately consumed by the public.

Technical Abstract: Curry powder is extensively used in Southeast Asian dishes. It has been subject to adulteration by azo dyes. This study used a newly developed 1064 nm dispersive hyperspectral Raman imaging system for detection of metanil yellow and Sudan-I contamination in curry powder. Curry powder was mixed with metanil yellow and (separately) with Sudan-I, at concentration levels of 1%, 3%, 5%, 7%, and 10% (w/w). Each sample was packed into a nickel-plated sample container (25 mm x 25 mm x 1mm). One Raman spectral image of each sample was acquired across the 25 mm x 25 mm surface area. Intensity threshold value was applied to the spectral images of Sudan-I mixtures (at 1593 cm-1) and metanil yellow mixtures (at 1147 cm-1) to obtain binary detection images. The results show that the number of detected adulterant pixels is linearly correlated with the sample concentration (R2= 0.99). The hyperspectral Raman imaging system was further used to obtain Raman spectral image of a curry powder sample mixed together with Sudan-I and metanil yellow, each contaminant at equal concentration of 5% (w/w). The multi-component spectra of the mixture sample were decomposed using self-modeling mixture analysis (SMA) to extract pure component spectra, which were then identified as matching those of Sudan-I and metanil yellow using spectral information divergence (SID) values. The results show that the 1064 nm dispersive hyperspectral Raman imaging system is a potential tool for rapid and nondestructive detection of multiple chemical contaminants in the complex food matrix.