Raman imaging for detection of adulterants in paprika powder: A comparison of data analysis methods

Authors: LOHUMI, SANTOSH - Chungnam National University; LEE, HOONSOO - Us Forest Service (FS); Kim, Moon; Qin, Jianwei - Tony Qin; CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Applied Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/21/2018
Publication Date: 3/23/2018
Citation: Lohumi, S., Lee, H., Kim, M.S., Qin, J., Cho, B. 2018. Raman imaging for detection of adulterants in paprika powder: A comparison of data analysis methods. Applied Sciences. 8:485. https://doi.org/10.3390/app8040485.
DOI: https://doi.org/10.3390/app8040485

Interpretive Summary: Synthetic dyes are frequently used as food colorants to enhance appearance and hence to promote sales. The toxicity and carcinogenicity of some synthetic dyes such as Sudan dyes, if used to enhance coloration of paprika- and chili-containing food products, are harmful for human consumption. In this research, we developed a high-throughput Raman chemical imaging method for direct inspection of two adulterants (Sudan-I and Congo Red dyes) mixed in the paprika powder. Using two selected spectral bands for each adulterant, chemical images were created to identify and map the Sudan-I and Congo Red particles in the paprika powder. The two dye adulterants can be detected at a weight by weight concentration of 0.1%. The results showed that the image-based method can be used for quantitative analysis of the adulterants. The Raman chemical imaging method developed in this study can potentially be used by regulatory agencies and food processors to authenticate paprika powder as well as other food powders and their ingredients.

Technical Abstract: Raman imaging requires effective extraction of chemical information from the corresponding datasets, which can be achieved by a range of analytical methods. However, since each of these methods exhibits both strengths and weaknesses, we herein directly compare univariate, bivariate, and multivariate analyses of Raman imaging data by evaluating their performance in the quantitation of two adulterants in paprika powder. Univariate and bivariate models were developed based on the spectral features of the target adulterants, whereas spectral angle mapper (SAM), adopted as a multivariate analysis method, utilized the complete dataset. The results demonstrate that despite being simple and easily implemented, the univariate method affords false positive pixels in the presence of background noise. Luckily, the above problem can be easily resolved using the bivariate method, which utilizes the multiplication of two band images wherein the same adulterant shows high-intensity peaks exhibiting the least overlap with those of other sample constituents. Finally, images produced by SAM contain abundant false negative pixels of adulterants, particularly for low-concentration samples. Notably, the bivariate method affords results closely matching the theoretical adulterant content, exhibiting the advantages of using non-complex data (only two bands are utilized) and being well suited for online applications of Raman imaging in the agro-food sector.