Quantitative Detection of Ascorbic Acid Additive in Flour Based on Raman Imaging Technology
WANG Xiao-bin1, 2, 3, ZHANG Xi1, GUAN Chen-zhi1, HONG Hua-xiu1, HUANG Shuang-gen2*, ZHAO Chun-jiang3
1. School of Physics and Electronic Information，Nanchang Normal University，Nanchang 330032，China
2. Key Laboratory of Modern Agricultural Equipment，Jiangxi Agricultural University，Nanchang 330045，China
3. National Research Center of Intelligent Equipment for Agriculture，Beijing 100097，China
Abstract：Ascorbic acid is a common flour quality improver, which is used to improve the rheological properties of dough and the baking quality of bread. In this study, the mixed samples containing different concentrations of ascorbic acid in flour were used as the research object, and the detection, identification and quantitative analysis of ascorbic acid in flour were explored by Raman imaging technology. Raman images of flour, ascorbic acid and flour-ascorbic acid mixed samples were collected respectively, and the region of interest and spectral range were determined. The average Raman spectra of the mixed samples were analyzed based on the three Raman peaks (631, 1 128 and 1 658 cm-1) of the ascorbic acid Raman spectrum with higher intensity and different from the flour. The results showed that it could not effectively evaluate the content of ascorbic acid in flour. The Raman spectrum corresponding to each pixel in the image was analyzed to detect ascorbic acid effectively in flour. The partial least squares (PLS) model was established by using the Raman spectra of each pixel in the mixed sample image as the correction set and the linear combination spectra of flour average Raman spectra and ascorbic acid average Raman spectra as the verification set. The regression coefficients of the model were used to reconstruct the three-dimensional Raman image of the sample into a two-dimensional grayscale image. The threshold segmentation method was used to classify flour pixels and ascorbic acid pixels in the image, and a quantitative analysis model was established based on the classification results. The results showed that the PLS model’s highest and lowest regression coefficients correspond to the highest Raman peaks of ascorbic acid and flour respectively. After all regression coefficients were applied to Raman images and converted into grayscale images, the flour and ascorbic acid pixels were still difficult to recognise. The threshold segmentation method transforms the gray image into a binary image to classify flour pixels and ascorbic acid pixels, which realizes the effective detection of ascorbic acid in flour. The minimum detection concentration of ascorbic acid in flour in this study was determined to be 0.01% (100 mg·kg-1) by analyzing the number of ascorbic acid pixels identified in the corresponding sub-samples of mixed samples with different concentrations of ascorbic acid. There was a good linear relationship between ascorbic acid concentration in the mixed sample, and the identified ascorbic acid pixels in the image in the range of 0.01%~0.20%, and the coefficient of determination was 0.996 0. The research results provide method support for the quantitative detection of ascorbic acid additives in flour and provide technical reference for large-scale rapid screening.
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