Czech J. Food Sci., 2022, 40(6):445-455 | DOI: 10.17221/129/2022-CJFS

Rapid non-destructive identification of selenium-enriched millet based on hyperspectral imaging technologyOriginal Paper

Fu Zhang1,2,3, Xiahua Cui1, Chaochen Zhang1, Weihua Cao1, Xinyue Wang1, Sanling Fu*,4, Shuai Teng1
1 College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
2 Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang, China
3 Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang, China
4 College of Physical Engineering, Henan University of Science and Technology, Luoyang, China

To meet rapid and non-destructive identification of selenium-enriched agricultural products selenium-enriched millet and ordinary millet were taken as objects. Image regions of interest (ROI) were selected to extract the spectral average value based on hyperspectral imaging technology. Reducing noise by the Savitzky-Golay (SG) smoothing algorithm, variables were used as inputs that were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), CARS-SPA, UVE-SPA, and UVE-CARS, while sample variables were used as outputs to build support vector machine (SVM) models. The results showed that the accuracy of CARS-SPA-SVM was 100% in the training set and 99.58% in the test set equivalent to that of CARS-SVM and UVE-CARS-SVM, which was higher than that of SPA-SVM, UVE-SPA-SVM, and UVE-SVM. Therefore, the method of CARS-SPA had superiority, and CARS-SPA-SVM was suitable to identify selenium-enriched millet. Finally, 454.57 nm, 484.98 nm, 885.34 nm, and 937.1 nm, which were obtained by wavelength extraction algorithms, were considered as the sensitive wavelengths of selenium information. This study provided a reference for the identification of selenium-enriched agricultural products.

Keywords: millet; selenium element; spectroscopy; secondary variable screening; support vector machine

Received: July 18, 2022; Accepted: November 15, 2022; Prepublished online: December 19, 2022; Published: November 22, 2022  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Zhang F, Cui X, Zhang C, Cao W, Wang X, Fu S, Teng S. Rapid non-destructive identification of selenium-enriched millet based on hyperspectral imaging technology. Czech J. Food Sci. 2022;40(6):445-455. doi: 10.17221/129/2022-CJFS.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Araújo M.C.U., Saldanha T.C.B., Galvão R.K.H., Yoneyama T., Chame H.C., Visani V. (2001): The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems, 57: 65-73. Go to original source...
    2. Cao C.L., Wang T.L., Gao M.F., Li Y., Li D.D., Zhang H.J. (2021): Hyperspectral inversion of nitrogen content in maize leaves based on different dimensionality reduction algorithms. Computers and Electronics in Agriculture, 190: 106461. Go to original source...
    3. Garcia-Martin J.F., Badaró A.T., Barbin D.F., Álvarez-Mateos P. (2020): Identification of copper in stems and roots of jatropha curcas L. by hyperspectral imaging. Processes, 8: 823. Go to original source...
    4. Guo W.C., Gu J.S., Liu D.Y., Shang L. (2016): Peach variety identification using near-infrared diffuse reflectance spectroscopy. Computers and Electronics in Agriculture, 123: 297-303. Go to original source...
    5. Guo Z.M., Wang M.M., Agyekum A.A., Wu J.Z., Chen Q.S., Zuo M., El-Seedi H.R., Tao F.F., Shi J.Y., Ouyang Q., Zou X.B. (2020): Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy. Journal of Food Engineering, 279: 109955. Go to original source...
    6. Ji H.Y., Ren Z.Q., Rao Z.H. (2019): Discriminant analysis of millet from different origins based on hyperspectral imaging technology. Spectroscopy and Spectral Analysis, 39: 2271-2277.
    7. Khan I.H., Liu H.Y., Li W., Cao A.Z., Wang X., Liu H.Y., Cheng T., Tian Y.C., Zhu Y., Cao W.X., Yao X. (2021): Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in wheat. Remote Sensing, 13: 23612. Go to original source...
    8. Li H.D., Liang Y.Z., Xu Q.S., Cao D.S. (2009): Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta, 648: 77-84. Go to original source... Go to PubMed...
    9. Liu Y., Qiao F., Wang S. W., Wang R. T., Xu L. L. (2021): Application of hyperspectral imaging technology for rapid identification of ruditapes philippinarum contaminated by heavy metals. RSC Advances, 11: 33939-33951. Go to original source... Go to PubMed...
    10. Liang K.H., Lu L.G., Zhu D.Z., Zhu H. (2020): Research progress of selenium content and selenium enrichment in millet of China. Journal of Chinese Institute of Food Science and Technology, 20: 337-343.
    11. Mojadadi A., Au A., Salah W., Witting P., Ahmad G. (2021): Role for selenium in metabolic homeostasis and human reproduction. Nutrients, 13: 3256. Go to original source... Go to PubMed...
    12. Mu T.T., Zhang F.Y., Li Z.H., Liu, Z., Tian G. (2018): Effects of spraying selenium on selenium content, conversion rate of organic selenium and quality of foxtail millet in different stages. Acta Agriculturae Boreali-Sinica, 33: 193-198.
    13. Rayman M.P. (2000): The importance of selenium to human health. The Lancet, 356: 233-241. Go to original source... Go to PubMed...
    14. Si G.Z., Yue X., Lyu Z., Yang H., Wang S.N., Li F.J., Song S.Z., Wen C.L., Tan Y. (2020): Rapid classification of northeast rice varieties based on hyperspectral imagery. Journal of Terahertz Science and Electronic Information Technology, 18: 687-691.
    15. Sun J., Jin X.M., Mao H.P., Wu X.H., Yang N. (2014): Application of hyperspectral imaging technology for detecting adulterate rice. Transactions of the Chinese Society of Agricultural Engineering, 30: 301-307.
    16. Wang H., Yu G. H., Hou Y., Hou S. Y., Han Y. H., Li H. Y., Xing G. F. (2021): Establishment and evaluation of nearinfrared prediction model for selenium content in millet. Journal of China Agricultural University, 26: 157-163.
    17. Wang J., Cheng J.J., Liu Y., Chang J.L., Wang Z.H. (2021): Identification of rice variety based on hyperspectral imaging technology. Journal of Agricultural Science and Technology, 23: 121-128.
    18. Wang X., Li Z., Zheng D., Wang W. (2020): Nondestructive identification of millet varieties using hyperspectral imaging technology. Journal of Applied Spectroscopy, 87: 54-61. Go to original source...
    19. Zhao C.X., Gao J., Fu Z.B., Chang Q.F. (2021): Research progress on the detection methods of total selenium in food. Journal of Food Safety and Quality, 12: 1653-1661.
    20. Zou X.B., Zhao J.W., Malcolm J.W.P., Mel H., Mao H.P. (2010): Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta, 667: 14-32. Go to original source... Go to PubMed...
    21. Yu Q., Zhang X., Si X.Z., Suo Y.Y., Li L., Mao J.W. (2018): Research progress of selenium in crops. Journal of Shanxi Agricultural Sciences, 46: 2122-2126.

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content