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Proceedings Paper

Nondestructive rapid detection of benzoyl peroxide in flour based on Raman hyperspectral technique
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Paper Abstract

In recent years, the quality and safety issues related to flour and pasta products have attracted great attention from the society. The quality of flour will directly affect the quality of downstream pasta products, as well as the physical and mental health and economic benefits of consumers. In this study, the illegal additive benzoyl peroxide in flour was the research object, and the rapid real-time non-destructive detection of benzoyl peroxide in flour was realized by Raman hyperspectral technique. By comparing the Raman spectra of pure benzoyl peroxide and pure flour, several Raman spectral characteristic peaks of benzoyl peroxide and their assignments were found. Characteristic peaks with strong signal at 1001 cm-1 and 1777 cm-1 were extracted for quantitative analysis. A gradient concentration of benzoyl peroxide-doped flour samples from 1% to 0.05% was prepared. And a series of pretreatment including S-G 5-point smoothing and background removal were performed to extract the number of effective benzoyl peroxide pixels in the mixed sample. And the proportion of benzoyl peroxide pixel points in total pixel points with different benzoyl peroxide concentrations was acquired. By comparing the relationship between the proportion and the concentration of benzoyl peroxide, a quantitative analysis model for the benzoyl peroxide doping in flour was established. The verification results show that there was good correlation between the proportion and the concentration of benzoyl peroxide. Both the averaged benzoyl peroxide signal intensities of effective pixel points and the number of effective pixels were combined for quantitative analysis. The research provided a methodological support for the detection of additives in flour by hyperspectral techniques and was a reference for the detection of dopants in food.

Paper Details

Date Published: 30 April 2019
PDF: 6 pages
Proc. SPIE 11016, Sensing for Agriculture and Food Quality and Safety XI, 110160G (30 April 2019); doi: 10.1117/12.2517454
Show Author Affiliations
Yan Li, China Agricultural Univ. (China)
Yankun Peng, China Agricultural Univ. (China)
Kuanglin Chao, U.S. Dept. of Agriculture, Agricultural Research Service (United States)
Jianwei Qin, U.S. Dept. of Agriculture, Agricultural Research Service (United States)
Sagar Dhakal, U.S. Dept. of Agriculture, Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 11016:
Sensing for Agriculture and Food Quality and Safety XI
Moon S. Kim; Bryan A. Chin; Byoung-Kwan Cho, Editor(s)

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