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Non-Destructive Quantitative Analysis of Azodicarbonamide Additives in Wheat Flour by High-Throughput Raman Imaging
Xiaobin Wang 1, 2, 3, 4, 5  
,   Chunjiang Zhao 2, 3, 4, 5  
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School of Physics and Electronic Information, Nanchang Normal University, Nanchang 330032, China
Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
Xiaobin Wang   

School of Physics and Electronic Information, Nanchang Normal University, 330032, Nanchang, China
Submission date: 2021-04-17
Final revision date: 2021-10-02
Acceptance date: 2021-10-05
Online publication date: 2021-10-26
Publication date: 2021-10-26
Azodicarbonamide (ADA) additives are limited or prohibited from being added to wheat flour by various countries because they may produce carcinogenic semicarbazide in humid and hot conditions. This study aimed to realize the non-destructive detection of ADA additives in wheat flour using high-throughput Raman imaging and establish a quantitative analysis model. Raman images of pure wheat flour, pure ADA, and wheat flour-ADA mixed samples were collected respectively, and the average Raman spectra of each sample were calculated. A partial least squares (PLS) model was established by using the linear combination spectra of pure wheat flour and pure ADA and the average Raman spectra of mixed samples. The regression coefficients of the PLS model were used to reconstruct the 3D Raman images of mixed samples into 2D grayscale images. Threshold segmentation was used to classify wheat flour pixels and ADA pixels in grayscale images, and a quantitative analysis model was established based on the number of ADA pixels. The results showed that the minimum detectable content of ADA in wheat flour was 100 mg/kg. There was a good linear relationship between the ADA content in the mixed sample and the number of pixels classified as ADA in the grayscale image in the range of 100 – 10,000 mg/kg, and the correlation coefficient was 0.9858. This study indicated that the combination of PLS regression coefficients with threshold segmentation had provided a non-destructive method for quantitative detection of ADA in Raman images of wheat flour-ADA mixed samples.
This work was supported by the Project supported by the National Natural Science Foundation of China (No. 32160417), the Science and Technology Project of Jiangxi Provincial Department of Education (No. GJJ212605), the Doctoral Research Funds of Nanchang Normal University (No. NSBSJJ2018016), and the Scientific research project of Nanchang Normal University (No. N21KJYB02).
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