Rapid non-destructive identification of selenium-enriched millet based on hyperspectral imaging technology
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.
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.
https://doi.org/10.1016/S0169-7439(01)00119-8
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.
https://doi.org/10.1016/j.compag.2021.106461
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.
https://doi.org/10.3390/pr8070823
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.
https://doi.org/10.1016/j.compag.2016.03.005
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.
https://doi.org/10.1016/j.jfoodeng.2020.109955
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.
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.
https://doi.org/10.3390/rs13183612
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.
https://doi.org/10.1016/j.aca.2009.06.046
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.
https://doi.org/10.1039/D1RA03664E
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.
Mojadadi A., Au A., Salah W., Witting P., Ahmad G. (2021): Role for selenium in metabolic homeostasis and human reproduction. Nutrients, 13: 3256.
https://doi.org/10.3390/nu13093256
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.
Rayman M.P. (2000): The importance of selenium to human health. The Lancet, 356: 233–241.
https://doi.org/10.1016/S0140-6736(00)02490-9
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.
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.
Wang H., Yu G. H., Hou Y., Hou S. Y., Han Y. H., Li H. Y., Xing G. F. (2021): Establishment and evaluation of near-infrared prediction model for selenium content in millet. Journal of China Agricultural University, 26: 157–163.
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.
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.
https://doi.org/10.1007/s10812-020-00962-y
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.
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.
https://doi.org/10.1016/j.aca.2010.03.048
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.
