Near‐infrared hyperspectral imaging for detection and quantification of azodicarbonamide in flour

Wang, Xiaobin; Zhao, Chunjiang; Huang, Wenqian; Wang, Qingyan; Liu, Chen; Yang, Guiyan

doi:10.1002/jsfa.8776

Cited by 13 publications

(7 citation statements)

References 33 publications

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“…The limits of quantitation and detection of the model were 72 and 15 mg/kg, respectively. Recently, the ADA content in wheat flour was determined using NIR hyperspectral imaging technology by Wang et al (2018) . From this study, it was found that the two wavelength bands with the largest differences between wheat flour and ADA were 1892 nm and 2039 nm, respectively, and the result showed that the minimum detected concentration of the optimal model was 0.2 g/kg.…”

Section: Safety Analysis Of Wheat Flourmentioning

confidence: 99%

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Zhang

Liu

Shen

et al. 2022

Current Research in Food Science

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Section: Safety Analysis Of Wheat Flourmentioning

confidence: 99%

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Zhang

Liu

Shen

et al. 2022

Current Research in Food Science

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“…The number of pixels classified as ADA in the mixed sample with the lowest ADA content was 7, 6, 9, and 8, as shown in Figure 4. This indicated that the minimum detectable content of ADA in wheat flour by this method was 100 mg/kg, which was lower than the limit of detection of ADA in flour by near-infrared hyperspectral imaging (200 mg/kg) [Wang et al, 2018]. Differences in the number of ADA pixels among subsamples were probably caused by the random distribution of ADA particles in the mixed sample.…”

Section: Quantitative Analysis Of Ada In Wheat Flourmentioning

confidence: 75%

Non-Destructive Quantitative Analysis of Azodicarbonamide Additives in Wheat Flour by High-Throughput Raman Imaging

Wang¹,

Zhao²

2021

Pol. J. Food Nutr. Sci.

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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.

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“…This is related to the intensity distribution of the original spectral data. These selected bands were statistically significant [ 44 , 51 ]. In general, only two characteristic wavelengths were selected for each orientation by the ANOVA method, which greatly reduces the number of variables compared with full spectral data (1185 bands).…”

Section: Resultsmentioning

confidence: 99%

“…Band ratio calculation is not only a spectral analysis method, but also an effective image processing method. It can reduce the spectral difference between samples of the same category and increase the contrast between different samples [ 43 , 44 ]. ANOVA of the band ratio has been widely used to detect fruit quality using spectral image [ 45 , 46 , 47 , 51 , 52 ].…”

Section: Methodsmentioning

confidence: 99%

Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Zhang

Cai

Fan

et al. 2021

Foods

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Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.

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Near‐infrared hyperspectral imaging for detection and quantification of azodicarbonamide in flour

Abstract: 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.

Cited by 13 publications

References 33 publications

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Non-Destructive Quantitative Analysis of Azodicarbonamide Additives in Wheat Flour by High-Throughput Raman Imaging

Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

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