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Spectral characterization and quantification of Phakopsora pachyrhizi urediniospores by Fourier transformed infrared with attenuated total reflectance

  • Lucas Henrique FantinEmail author
  • Ana Lúcia de Souza Madureira Felício
  • Karla Braga
  • Giancarlo Michelino Gaeta
  • José Alexandre de França
  • Marcelo Giovanetti Canteri
Article
  • 51 Downloads

Abstract

Asian soybean rust, Phakopsora pachyrhizi, is the most devastating disease in the soybean crop. Despite efforts to control the disease, such as a soybean-free period and cultivar resistance, outbreaks have been increasing in Brazil, with losses reaching 80%. Chemical control is the main strategy adopted by South American farmers to control the disease. Fungicide application is recommended before the occurrence of symptoms; monitoring for spores can improve decision-making and treatment applications. The objective of this study was to identify a spectral signature for the fungus P. pachyrhizi and quantify the number of urediniospores in a sample of water using the Fourier Transform Infrared spectroscopy technique in the MIR range, 650 cm−1 to 4000 cm−1, with the attenuated total reflectance (ATR) accessory. In FTIR spectroscopy, the samples are irradiated by infrared light resulting in absorption of energy by molecular vibrations. Fungus samples were collected in 48 Brazilian soybean growth regions. A total of 130 samples were assessed. The characterization of fungi samples was conducted using Infrared Spectroscopy from 1000 cm−1 to 1560 cm−1. The spectral signature of P. pachyrhizi was between 1500 cm−1 to 1550 cm−1. The quantification showed high values for the calibration coefficients (R2 = 0.9478), cross validation (R2 = 0.9330) and prediction (R2 = 0.9236), which demonstrates the accuracy for estimation of the number of urediniospores. The FTIR-ATR spectroscopy enables the characterization and quantification of P. pachyrhizi urediniospores in water samples, creating a more efficient way to monitor spore dispersal and management of this disease.

Keywords

FTIR-ATR spectroscopy Spectral signature Multivariate analysis Asian soybean rust Soybean 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal studies

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Andrade, L. H. C., Freitas, P. G., Mantovani, B. G., Figueiredo, M. S., Lima, R. A., Lima, S. M., Rangel, M. A. S., & Mussury, R. M. (2008). Detection of soybean rust contamination in soy leaves by FTIR photoacoustic spectroscopy. The European Physical Journal Special Topics, 153(1), 539–541.  https://doi.org/10.1140/epjst/e2008-00503-8.CrossRefGoogle Scholar
  2. Baker, M. J., Trevisan, J., Bassan, P., Bhargava, R., Butler, H. J., Dorling, K. M., Fielden, P. R., Fogarty, S. W., Fullwood, N. J., Heys, K. A., Hughes, C., Lasch, P., Martin-Hirsch, P. L., Obinaju, B., Sockalingum, G. D., Sulé-Suso, J., Strong, R. J., Walsh, M. J., Wood, B. R., Gardner, P., & Martin, F. L. (2014). Using Fourier transform IR spectroscopy to analyze biological materials. Nature Protocols, 9(8), 1771–1791.  https://doi.org/10.1038/nprot.2014.110.CrossRefGoogle Scholar
  3. Barnes, C. W., Szabo, L. J., & Bowersox, V. C. (2009). Identifying and quantifying Phakopsora pachyrhizi spores in rain. Phytopathology, 99(4), 328–338.  https://doi.org/10.1094/phyto-99-4-0328.CrossRefGoogle Scholar
  4. Bozza, A., Tralamazza, S., Rodriguez, J., Scholz, M. B., Reynaud, D., Dalzoto, P., & Pimentel, I. (2013). Potential of fourier transform infrared spectroscopy ( FT-IR ) to detection and quantification of ochratoxin a : a comparison between reflectance and transmittance techniques. International Journal of Pharmaceutical, Chemical and Biological Sciences, 3(4), 1242–1247.Google Scholar
  5. Del Ponte, E. M., Godoy, C. V., Li, X., & Yang, X. B. (2006). Predicting severity of Asian soybean rust epidemics with empirical rainfall models. Phytopathology, 96(7), 797–803.  https://doi.org/10.1094/PHYTO-96-0797.CrossRefGoogle Scholar
  6. Dias, A. P. S., Li, X., & Yang, X. B. (2014). Modeling the effects of cloudy weather on regional epidemics of soybean rust. Plant Disease, 98(June), 811–816.  https://doi.org/10.1094/PDIS-03-13-0269-RE.CrossRefGoogle Scholar
  7. Godoy, C. V., Seixas, C. D. S., Soares, R. M., Marcelino-Guimarães, F. C., Meyer, M. C., & Costamilan, L. M. (2016). Asian soybean rust in Brazil: past, present, and future. Pesquisa Agropecuária Brasileira, 51(5), 407–421.  https://doi.org/10.1590/S0100-204X2016000500002.CrossRefGoogle Scholar
  8. Igarashi, W. T., de França, J. A., Silva, M. A. d. A. E., Igarashi, S., & Abi Saab, O. J. G. (2016). Application of prediction models of asian soybean rust in two crop seasons, in Londrina, Pr. Semina: Ciências Agrárias, 37(5), 2881.  https://doi.org/10.5433/1679-0359.2016v37n5p2881.Google Scholar
  9. Irudayaraj, J., Yang, H., & Sakhamuri, S. (2002). Differentiation and detection of microorganisms using Fourier transform infrared photoacoustic spectroscopy. Journal of Molecular Structure, 606(1–3), 181–188.  https://doi.org/10.1016/S0022-2860(01)00869-9.CrossRefGoogle Scholar
  10. Klosowski, A. C., Brahm, L., Stammler, G., & May De Mio, L. L. (2016a). Competitive fitness of Phakopsora pachyrhizi isolates with mutations in the CYP51 and CYTB genes. Phytopathology, 106(11), 1278–1284.  https://doi.org/10.1094/PHYTO-01-16-0008-R.CrossRefGoogle Scholar
  11. Klosowski, A., May De Mio, L. L., Miessner, S., Rodrigues, R., & Stammler, G. (2016b). Detection of the F129L mutation in the cytochrome b gene in Phakopsora pachyrhizi. Pest Management Science, 72(6), 1211–1215.  https://doi.org/10.1002/ps.4099.CrossRefGoogle Scholar
  12. Koga, L. J., Canteri, M. G., Calvo, E. S., Martins, D. C., Xavier, S. A., Harada, A., & Kiihl, R. A. S. (2014). Managing soybean rust with fungicides and varieties of the early/semi-early and intermediate maturity groups. Tropical Plant Pathology, 39(2), 129–133.  https://doi.org/10.1590/S1982-56762014000200003.CrossRefGoogle Scholar
  13. Langenbach, C., Campe, R., Beyer, S. F., & Müller, A. N. (2016). Fighting Asian soybean rust. Frontiers in Plant Science, 7, 1–24.  https://doi.org/10.3389/fpls.2016.00797.CrossRefGoogle Scholar
  14. Levasseur-Garcia, C. (2018). Updated overview of infrared spectroscopy methods for detecting mycotoxins on cereals (corn, wheat, and barley). Toxins, 10(1).  https://doi.org/10.3390/toxins10010038.
  15. Levasseur-Garcia, C., Malaurie, H., & Mailhac, N. (2016). An infrared diagnostic system to detect causal agents of grapevine trunk diseases. Journal of Microbiological Methods, 131(December 2016), 1–6.  https://doi.org/10.1016/j.mimet.2016.09.022.CrossRefGoogle Scholar
  16. Minchio, C. A., Canteri, M. G., Fantin, L. H., Silva, A. e., & de, M. A. (2016). Epidemias de ferrugem asiática no Rio Grande do Sul explicadas pelo fenômeno ENOS e pela incidência da doença na entressafra. Summa Phytopathologica, 42(4), 321–326.  https://doi.org/10.1590/0100-5405/2219.CrossRefGoogle Scholar
  17. Minchio, C. A., Fantin, L. H., Caviglione, J. H., Braga, K., Silva, M. A. A. E., & Canteri, M. G. (2018). Predicting Asian soybean rust epidemics based on off-season occurrence and El Niño southern oscillation phenomenon in Paraná and Mato Grosso states, Brazil. Journal of Agricultural Science, 10(11), 562.  https://doi.org/10.5539/jas.v10n11p562.CrossRefGoogle Scholar
  18. Pataca, L. C. M., Borges, W., Marcucci, M. C., & Poppi, R. J. (2007). Determination of apparent reducing sugars , moisture and acidity in honey by attenuated total reflectance-Fourier transform infrared spectrometry. Talanta, 71, 1926–1931.  https://doi.org/10.1016/j.talanta.2006.08.028.CrossRefGoogle Scholar
  19. Rajalahti, T., & Kvalheim, O. M. (2011). Multivariate data analysis in pharmaceutics : A tutorial review. International Journal of Pharmaceutics, 417, 280–290.  https://doi.org/10.1016/j.ijpharm.2011.02.019.CrossRefGoogle Scholar
  20. Salman, A., Pomerantz, A., Tsror, L., Lapidot, I., Zwielly, A., Moreh, R., Mordechai, S., & Huleihel, M. (2011). Distinction of Fusarium oxysporum fungal isolates (strains) using FTIR-ATR spectroscopy and advanced statistical methods. The Analyst, 136(5), 988–995.  https://doi.org/10.1039/c0an00801j.CrossRefGoogle Scholar
  21. Salman, A., Pomerantz, A., Tsror, L., Lapidot, I., Moreh, R., Mordechai, S., & Huleihel, M. (2012). Utilizing FTIR-ATR spectroscopy for classification and relative spectral similarity evaluation of different Colletotrichum coccodes isolates. The Analyst, 137(15), 3558–3564.  https://doi.org/10.1039/c2an35233h.CrossRefGoogle Scholar
  22. Schmitz, H. K., Medeiros, C. A., Craig, I. R., & Stammler, G. (2014). Sensitivity of Phakopsora pachyrhizi towards quinone-outside-inhibitors and demethylation-inhibitors, and corresponding resistance mechanisms. Pest Management Science, 70(3), 378–388.  https://doi.org/10.1002/ps.3562.CrossRefGoogle Scholar
  23. Simões, K., Hawlik, A., Rehfus, A., Gava, F., & Stammler, G. (2018). First detection of a SDH variant with reduced SDHI sensitivity in Phakopsora pachyrhizi. Journal of Plant Diseases and Protection, 125(1), 21–26.  https://doi.org/10.1007/s41348-017-0117-5.CrossRefGoogle Scholar
  24. Sumida, C. H., Fantin, L. H., Gonçalves, R. M., Giovanetti, M., Araújo, K. L., & Giglioti, É. A. (2019). A system to map the risk of infection by Puccinia kuehnii in Brazil. Acta Scientiarum Agronomy, 41(1), 1–11.  https://doi.org/10.4025/actasciagron.v41i1.39905.Google Scholar
  25. Szeghalmi, A., Kaminskyj, S., & Gough, K. M. (2007). A synchrotron FTIR microspectroscopy investigation of fungal hyphae grown under optimal and stressed conditions. Analytical and Bioanalytical Chemistry, 387(5), 1779–1789.  https://doi.org/10.1007/s00216-006-0850-2.CrossRefGoogle Scholar
  26. van den Bosch, F., Oliver, R., van den Berg, F., & Paveley, N. (2014). Governing principles can guide fungicide-resistance management tactics- supplemental material. Annual Review of Phytopathology, 52, 175–195.  https://doi.org/10.1146/annurev-phyto-102313-050158.CrossRefGoogle Scholar
  27. Wen, L., Bowen, C. R., & Hartman, G. L. (2017). Prediction of short-distance aerial movement of Phakopsora pachyrhizi Urediniospores using machine learning. Phytopathology, 107(10), 1187–1198.  https://doi.org/10.1094/PHYTO-04-17-0138-FI.Google Scholar
  28. Wenning, M., Scherer, S., & Naumann, D. (2008). Infrared spectroscopy in the identification of microorganisms. In M. Diem., P. R. Griffitths., & J. M, Chalmers (eds)  Handbook of Vibrational Spectroscopy (pp. 71–96). Chichester: John Wiley and Sons.Google Scholar
  29. Xavier, S. A., Martins, D. C., Fantin, L. H., & Canteri, M. G. (2017). Older leaf tissues in younger plants are more susceptible to soybean rust. Acta Scientiarum Agronomy, 39(1), 17.  https://doi.org/10.4025/actasciagron.v39i1.30638.CrossRefGoogle Scholar
  30. Xue, J., Chen, H., Xiong, D., Huang, G., Ai, H., Liang, Y., Yan, X., Gan, Y., Chen, C., & Ye, L. (2014). Noninvasive measurement of glucose in artificial plasma with near-infrared and raman spectroscopy. Applied Spectroscopy, 68(4), 428–433.  https://doi.org/10.1366/13-07250.CrossRefGoogle Scholar

Copyright information

© Koninklijke Nederlandse Planteziektenkundige Vereniging 2019

Authors and Affiliations

  • Lucas Henrique Fantin
    • 1
    Email author
  • Ana Lúcia de Souza Madureira Felício
    • 2
  • Karla Braga
    • 1
  • Giancarlo Michelino Gaeta
    • 3
  • José Alexandre de França
    • 3
  • Marcelo Giovanetti Canteri
    • 1
  1. 1.Agronomy DepartmentUniversidade Estadual de Londrina (UEL)LondrinaBrazil
  2. 2.Department of Research and DevelopmentInstitute of Economic and Social Development (ITEDES)LondrinaBrazil
  3. 3.Electric Engineer DepartmentUniversidade Estadual de LondrinaLondrinaBrazil

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