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Near‐infrared hyperspectral imaging for detection and quantification of azodicarbonamide in flour
Author(s) -
Wang Xiaobin,
Zhao Chunjiang,
Huang Wenqian,
Wang Qingyan,
Liu Chen,
Yang Guiyan
Publication year - 2018
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.8776
Subject(s) - hyperspectral imaging , pixel , mathematics , segmentation , pattern recognition (psychology) , correlation coefficient , wheat flour , artificial intelligence , chemistry , computer science , food science , statistics
BACKGROUND The present study aimed to establish a method for the detection and quantification of azodicarbonamide (ADC) in flour using hyperspectral imaging technology. Hyperspectral images of pure flour, pure ADC and flour–ADC mixtures with different concentrations of ADC were collected. F ‐values of one‐way analysis of variance for all possible wavebands within the spectra of the flour and ADC were calculated, and the maximum value indicated that the two wavebands have more significant differences, i.e. the optimal two wavebands. Threshold segmentation was used for band ratio images of two wavebands to create a binary image. This allowed visual identification of ADC‐rich pixels in the mixtures. RESULTS The two wavebands with the largest difference between flour and ADC were 2039 nm and 1892 nm. Using the binary image construction method, different concentrations of ADC in flour were identified. The minimum detected concentration was 0.2 g kg −1 . In the mixtures, the number of ADC‐rich pixels detected had a good linear relationship with the ADC concentrations, with a correlation coefficient of 0.9845. CONCLUSION This study indicated that the band ratio algorithm combination with threshold segmentation for hyperspectral images provides a non‐destructive method for detecting and quantifying of ADC in flour. © 2017 Society of Chemical Industry