Identification of soybean seed varieties by spectral luminescence method

Optical spectral photoluminescent diagnostic methods, characterized by high speed and low implementation costs, can serve as an alternative to existing methods of varietal seed identification. The study aims to develop a methodology for identifying soybean seed varieties using a spectral luminescence method. The author used a diffraction spectrofluorimetric complex to measure the spectral excitation characteristics of photoluminescence in six soybean varieties: ultra-early maturing (Puma and Sayana), early maturing (Greya and Selena), and mid-maturing (Vilana and Vilana beta). Based on the obtained dependencies, he calculated integral and statistical parameters: integral absorptive capacity, mathematical expectation, variance, skewness, and kurtosis. The photoluminescence excitation characteristics of soybean seeds were found in the range between 330 and 475 nm, with primary peaks at 392 and 420 nm. For Gaussian decomposition, the spectra were converted to the frequency domain using the inverse Fourier transform. Based the research findings, the author selected the integral absorptive capacity in the range between 350 and 396 nm as the variety identification parameter. Skewness was recognized as the most effective statistical parameter for the unambiguous identification of three soybean varieties. Gaussian decomposition parameters – variance, skewness, and frequency-domain mathematical expectation – can be applied for the unique identification of all studied varieties. The methodology for identifying soybean seed varieties includes sample preparation, irradiation in the range between 330 and 475 nm, and registration of photoluminescent signals by a radiation receiver to obtain the U(λ) dependency and, based on it, calculate the varietal identification parameters: Gaussian amplitude and variance, frequency-domain mathematical expectation, integral absorptive capacity, and skewness. Narrow-spectrum LEDs and a broad-spectrum photodiode are used to implement the developed methodology. The characteristics of the U(λ) photo-signals help determine the varietal affiliation of the seeds. The results obtained can be used to develop a portable, high-speed variety control device for both laboratory and field conditions.

1. Veklenko V.I., Dadashev B.A. Soybean oil production in the world and Russia. Vestnik Kurskoy gosudarstvennoy selskokhozyaystvennoy akademii. 2024;2:192-197. (In Russ.)

2. Li M., Zhao M., Shi J., Huang Y., Li L., Jin N., Kong Z., Simal-Gandara J., Wang F., Fan B., Xie H. Discovery and Verification of Soybean Sprouting Selection Based on Quality Across Various Origins and Varieties: Varietal Effects on Sprouted Soybean Quality. Food Science & Nutrition. 2025;13:e70016. https://doi.org/10.1002/fsn3.70016.

3. Lobachevskiy Ya.P., Dorokhov A.S. Digital technologies and robotic devices in the agriculture. Agricultural Machinery and Technologies. 2021;15(4):6-10. (In Russ.) https://doi.org/10.22314/2073-7599-2021-15-4-6-10

4. Ramazanova S.A., Savichenko V.G., Ustarkhanova E.G. et al. Search of the new SSR-loci of DNA for development of effective technology for soybean genotyping. Oil crops. 2021;4(188):18-24. (In Russ.) https://doi.org/10.25230/2412-608X-2021-4-188-18-24

5. Torikov V.E., Shpilev N.S., Klimenkov F.I. Electrophoretic techniques for crops variety identification. Vestnik Altayskogo gosudarstvennogo agrarnogo universiteta. 2019;2(172):5-12. (In Russ.)

6. Smolikova G.N., Shavarda A.L., Alekseichuk I.V. et al. The metabolomic approach to the assessment of cultivar specificity of Brassica napus L. seeds. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2015;19(1):121-127. (In Russ.) https://doi.org/10.18699/VJ15.015

7. Pang L., Wang Z., Mi S., Li H. Feasibility study on identifying seed variety of soybean with hyperspectral imaging and deep learning. Journal of Chemometrics. 2025;39(5):e70035. https://doi.org/10.1002/cem.70035

8. Miranda M.C.C., Aono A.H., Fagundes T.G. et al. High-throughput phenotyping and machine learning techniques in soybean breeding: Exploring the potential of aerial imaging and vegetation indices. Agronomy Journal. 2025;117(1):e70012. https://doi.org/10.1002/agj2.70012

9. Kumar A., Sahu J.K., Naik S., Suhas Dr. Lipid characterization of black soybean (Glycine max [L.] Merrill) seeds using GC-MS/HS fingerprinting. Journal of Food Processing and Preservation. 2022;46(2):e17092. https://doi.org/10.1111/jfpp.17092

10. Li H., Zhang L., Sun H. et al. Identification of soybean varieties based on hyperspectral imaging technology and one-dimensional convolutional neural network. Journal of Food Process Engineering. 2021;44(15):e13767. https://doi.org/10.1111/jfpe.13767

11. Alexandrova V.A., Lysenkova A.A. Identification of soybean varieties by optical spectroscopy methods. Food Products Commodity Expert. 2025;2(247):84-87. (In Russ.)

12. Xu P., Yang R., Zeng T. et al. Varietal classification of maize seeds using computer vision and machine learning techniques. Journal of Food Process Engineering. 2021;44(2):e13846. https://doi.org/10.1111/jfpe.13846

13. Singh T., Garg N.M., Iyengar S.R.S. Nondestructive identification of barley seeds variety using near-infrared hyperspectral imaging coupled with convolutional neural network. Journal of Food Process Engineering. 2021;44(6):e13821. https://doi.org/0.1111/jfpe.13821

14. Sharma A., Singh T., Garg N. Nondestructive identification of wheat seed variety and geographical origin using near-infrared hyperspectral imagery and deep learning. Journal of Chemometrics. 2024;38(2):e3585. https://doi.org/10.1002/cem.3585

15. Nazari L., Shaker M., Karimi A., Ropelewska E. Identification of sorghum genotypes using a machine vision system. Journal of Food Process Engineering. 2021;44(5):e13673. https://doi.org/10.1111/jfpe.13673

16. Belyakov M.V. Optical luminescent properties of agricultural plants’ seeds. Agrophysica. 2024;4:72-78. (In Russ.)

17. Pan Y.-L. Detection and characterization of biological and other organic-carbon aerosol particles in atmosphere using fluorescence. Journal of Quantitative Spectroscopy and Radiative Transfer. 2015;150:12-35. https://doi.org/10.1016/j.jqsrt.2014.06.007

18. Matveeva T.A., Sarimov R.M., Persidskaya O.K., et al. Application of fluorescence spectroscopy for early detection of fungal infection of winter wheat grains. AgriEngineering. 2024;6(3):3137-3158.

19. Donaldson L. Autofluorescence in Plants. Molecules. 2020;25(10):2393. https://doi.org/10.3390/molecules25102393

20. Belyakov M.V., Lysenkova A.A. Varietal features of soybean photoluminescence. Vestnik of the Russian Agricultural Science. 2025;2:12-16. (In Russ.) https://doi.org/10.31857/S2500208225020032