Xiaobin Wang1,2,3,4,5, Chunjiang Zhao1,2,3,4,5, Wenqian Huang1,2,3,4, Qingyan Wang1,2,3,4, Chen Liu1,2,3,4, Guiyan Yang1,2,3,4. 1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China. 2. National Research Center of Intelligent Equipment for Agriculture, Beijing, China. 3. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing, China. 4. Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing, China. 5. College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China.
Abstract
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.
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.