基于拉曼成像技术的面粉中抗坏血酸添加剂定量检测研究

doi:10.3964/j.issn.1000-0593(2021)12-3765-06

Abstract
References
Related Citation (15)

Download: PDF (2464 KB)
Export: BibTeX | EndNote (RIS)

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.

Key words：Raman imaging technology；Flour；Ascorbic acid；Non-destructive detection；Quantitative analysis

Received: 2021-04-02 Accepted: 2021-07-16

ZTFLH:

O433.4

Corresponding Authors: HUANG Shuang-gen E-mail: shuang19792@163.com

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)12-3765-06 OR https://www.gpxygpfx.com/EN/Y2021/V41/I12/3765

[1] Guo X，Wu S，Zhu K. Food Chemistry，2020，322：126738.
[2] Osuna M B，Romero C A，Romero A M，et al. LWT-Food Science and Technology，2018，98：54.
[3] Shokraie M，Salehifar M，Pazhooh R A. Journal of Agricultural Science and Technology，2018，20（5）：953.
[4] WU Wei，ZU Guang-peng，CHEN Gui-yun，et al（吴威，祖广鹏，陈桂云，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2020，40（5）：1565.
[5] DENG Jia-min，WANG Wei，ZHAO Xin，et al（邓佳岷，王伟，赵昕，等）. Modern Food Science and Technology（现代食品科技），2019，35（11）：270.
[6] Liu Y，Sun L，Ran Z，et al. Journal of Food Protection，2019，82（10）：1655.
[7] Sun X，Zhu K，Liu J，et al. Journal of Infrared Millimeter and Terahertz Waves，2019，40（4）：466.
[8] DUAN Gui-jiao，ZHANG Jian-wei，ZHANG Zhi-bin，et al（段贵娇，张健伟，张志彬，等）. Food and Fermentation Industries（食品与发酵工业），2019，45（10）：272.
[9] Zhao J，Peng Y，Chao K，et al. Proceedings of SPIE: Sensing for Agriculture and Food Quality and Safety VII，2015，9488：94880S.
[10] ZHAI Chen，PENG Yan-kun，LI Yong-yu，et al（翟晨，彭彦昆，李永玉，等）. Chemical Journal of Chinese Universities（高等学校化学学报），2017，38（3）：369.
[11] Wang X，Zhao C，Huang W，et al. Spectroscopy Letters，2017，50（7）：364.
[12] Qin J，Kim M S，Chao K，et al. Applied Spectroscopy，2017，71（11）：2469.
[13] Wang X，Huang W，Zhao C，et al. European Food Research and Technology，2017，243（12）：2265.
[14] Li Y，Peng Y，Chao K，et al. Proceedings of SPIE: Sensing for Agriculture and Food Quality and Safety XI，2019，11016：110160G.
[15] Singh G，Mohanty B P，Saini G S S. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy，2016，155：61.