|Qin, Jianwei - Tony Qin
|Chao, Kuanglin - Kevin Chao
|DHAKAL, SAGAR - Us Forest Service (FS)
|LEE, HOONSOO - Us Forest Service (FS)
Submitted to: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 6/20/2016
Publication Date: 7/18/2016
Citation: Qin, J., Kim, M.S., Chao, K., Dhakal, S., Lee, H. 2016. Detecting adulterants in milk powder using high-throughput Raman chemical imaging. ASABE Annual International Meeting, St. Joseph, MI. ASABE Paper No. 162460307.
Interpretive Summary: Milk is a vulnerable target for economically motivated adulteration. Adulterants added to the milk can cause illnesses or even deaths for the consumers. This study presents a high-throughput Raman chemical imaging method for authenticating milk powder. Chemical images were created to map two chemical adulterants (i.e., melamine and urea) mixed in the milk powder. Both melamine and urea were detected at a concentration of 50 parts per million (ppm), which is lower than the concentration levels (e.g., thousands of ppm) reported in some real-life milk adulteration incidents. The method developed in this study is effective and efficient for detecting the adulterants in the milk powder, which can be used to help regulatory agencies and food processors authenticate the milk powder as well as other types of powdered foods and ingredients.
Technical Abstract: This study used a line-scan high-throughput Raman imaging system to authenticate milk powder. A 5 W 785 nm line laser (240 mm long and 1 mm wide) was used as a Raman excitation source. The system was used to acquire hyperspectral Raman images in a wavenumber range of 103–2881 cm-1 from the skim milk powder mixed with two nitrogen-rich adulterants (i.e., melamine and urea) at eight concentrations from 50 to 10,000 ppm. An acoustic mixer that utilizes high-intensity acoustic waves was used to prepare milk-adulterant mixtures. The mixed samples were put in sample holders with a surface area of 150 mm×100 mm and a depth of 2 mm for push-broom image acquisition. Varying fluorescence signals from the milk powder were removed using a correction method based on adaptive iteratively reweighted penalized least squares. Image classifications were conducted using a simple thresholding method applied to single-band fluorescence-corrected images at unique Raman peaks selected for melamine (673 cm-1) and urea (1009 cm-1). Chemical images were generated by combining individual binary images of melamine and urea to visualize identification, spatial distribution, and morphological features of the two adulterant particles in the milk powder. Limits of detection for both melamine and urea were estimated in the order of 50 ppm.