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Determination of starch and carbohydrate in mango leaves using Vis-NIR spectroscopy

Determinação de amido e carboidrato em folhas de mangueira com o uso espectroscopia Vis-NIR.



How to Cite
Alves Santana, E., dos Santos Costa, D., & Francismar de Medeiros, J. . (2023). Determination of starch and carbohydrate in mango leaves using Vis-NIR spectroscopy. Sour Topics, 27(2), 397-410. https://doi.org/10.21897/rta.v27i2.3114

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PlumX
Elisson Alves Santana
Daniel dos Santos Costa
Jose Francismar de Medeiros

Mango production presents challenges, such as the maturation of the mango branches, which, combined with good nutrition and biochemicals involved in this process, such as carbohydrate and starch favor the development of the plant. Therefore, the use of non-destructive, fast techniques to determine the levels of these components in the plant, such as spectroscopy, can optimize the analysis of these components. Therefore, this work aimed to develop predictive models for determination of starch and carbohydrate contents in “Palmer” mango leaves using vis-nir spectroscopy subjected to different potassium sources. The work was carried out in the region of San Francisco Valley, using the following steps: (1) leaf sampling; (2) spectral analysis; (3) lab determination of carbohydrate and starch contents; and (4) development of predictive regression and classification models. The predictive regression models used were Principal Components Regression (PCR) and Partial Least Squares Regression (PLSR). Supervised discriminant models were also developed to classify mango leaves according to different potassium sources used, using linear discriminant analysis (LDA). Vis-NIR spectroscopy showed low values for the non-destructive evaluation of “Palmer” mango leaves using PCR and PLSR for carbohydrate and starch prediction with R2 of 0.58 lower than the models considered excellent (R2 >0.90); The development of classification models did not allow the discrimination of different sources of potassium in “Palmer” mango leaves with an accuracy of 64.2%.


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  1. Barnes, R. J., dhanoa, M. S., Lister, S. J. 1989. Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra. Appl Spectrosc 43(5): 772–777. https://doi.org/10.1366/0003702894202201
  2. Barreto, N. S. 2020. Aplicação da técnica de espectroscopia do visível e infravermelho próximo (Vis/NIR) no controle físico-químico da qualidade de vinhos produzidos no Submédio do Vale do São Francisco. Dissertação Mestrado em Ciência e Tecnologia de Alimentos, Universidade Federal de Sergipe- UFSE, São Cristovão.
  3. Cao, F., Wu, D., He,Y. 2010. Soluble solids contente and Ph prediction and varieties discrimination of grapes based on visible-near infrared spectroscopy. Comput. Electron. Agric, 71(6): 15-18. http://dx.doi.org/10.101/j.compag.2009.05.011
  4. Cavalcante, Í. H. L., Santos, G. N. F. Dos., Silva, M. A. Da., Martins, R. S., Lima, A. M. N., Modesto, P. I. R., Alcobia, A. M., SILVA, T. R. S., Araujo, E. A., R. A., Beckmann-Cavalcante, M. A. 2018. New approach to induce mango shoot maturation in Brazilian semi-arid environment. Journal of Applied Botany and Food Quality, 91: 281-286.https://doi.org/10.5073/JABFQ.2018.091.036
  5. Coombe, B. G. 1995. Adoption of a system for identifying grapevine growth stages. Australian Journal of Grape and Wine Research, 1(2): 100-110. https://doi.org/10.1111/j.1755-0238.1995.tb00086.x
  6. Costa, D. S., Mesa, N. F. O., Freire, M. S.; Ramos, R. P.; Medeiros, B. J. T. 2019. Development of predictive models for quality and maturation stage atributes of wine grapes usin vis-nir reflectance spectroscopy. Posthavest Biology and Technology, 150: 166-178. https://doi.org/10.1016/j.postharvbio.2018.12.010
  7. Curran, P. J. 1989. Remote sensing of foliar chemistry. Remote Sensing of Environment, 30(3): 271–278. https://doi.org/10.1016/0034-4257(89)90069-2
  8. Das, B. Sahoo R. N., Pargal S., Krishna G., Verma R., Chinnusamy V., Sehgal V. K., Gupta V. K., Traço S. K., Swain P. 2018. Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 192(5): 41–51.https://doi.org/10.1016/j.saa.2017.10.076
  9. De Bei, R., Fuentes, S., Sullivan, W., Edwards, E. J., Cozzolino, D. 2017. Rapid measurement of total non-structural carbohydrate concentration in grapevine trunk and leaf tissues using near infrared spectroscopy. Computers and Electronics in Agriculture, 136(15):176–183. https://doi.org/10.1016/j.compag.2017.03.007
  10. Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A., Smith, F. 1956. Colorimetric Method for Determination of Sugars and Related Substances. Anaytical Chemistry, 28(3): 350-356, https://pubs.acs.org/doi/10.1021/ac60111a017
  11. Frey, L. A., Baumann, P., Aasen, H., Studer, B., Kolliker, R. 2020. A Non-destructive Method to Quantify Leaf Starch Content in Red Clover. Frontiers in Plant Science, 11. https://doi.org/10.3389/fpls.2020.56994
  12. Geladi, P., Kowalski, B. R. 1986. Partial least-squares regression: a tutorial. Anal. Chimica Acta, 185:1-17, 1986.
  13. Genisheva, Z., Quintelas, C., Mesquita, D. P., Ferreira, E. C., Oliveira, J. M., Amaral, A. L. 2018. New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy (NIR). Food Chemistry, 246: 172–178, https://doi.org/10.1016/j.foodchem.2017.11.015
  14. Gonzáles-Aguiar, D.,Colás-Sánchez, A., Rodriguez-López, O., Álvarez-Vázquez, D. L., Gattorno-Muñoz, S.,Chacón-Iznaga, Ahmed. 2020. Estimación De La Materia Orgánica En Suelo PardoMullido Medianamente Lavado Mediante Espectroscopia Vis-NIR. Centro Agrícola, 23-32. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0253-57852020000300023&lng=es&nrm=iso
  15. Gould, K., Jay-Allemand, C., Logan, B. A., Baissac, Y., Bidel, L. P. R. 2018. When are foliar anthocyanins useful to plants? Re-evaluation of the photoprotection hypothesis using Arabidopsis thaliana mutants that differ in anthocyanin accumulation. Environmental and Experimental Botany, 154(1): 11-22, https://doi.org/10.1016/j.envexpbot.2018.02.006
  16. Lohr, D., Tillmann, P., Druege, U., Zerche, S., Rath, T., Meinken, E. 2017. Non-destructive determination of carbohydrate reserves in leaves of ornamental cuttings by near-infrared spectroscopy (NIRS) as a key indicator for quality assessments. Biosystems Engineering, 158:51–63, https://doi.org/10.1016/j.biosystemseng.2017.03.005
  17. Martens, H., Martens, M. 2000. Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Food Quality and Preference, 11(1): 5–16, https://doi.org/10.1016/S0950-3293(99)00039-7
  18. Nakajima, S., Shiraga, K., Suzuki, T., Kondo, N., Ogawa, Y. 2019. Quantification of starch content in germinating mung bean seedlings by terahertz spectroscopy. Food Chemistry, 294(1): 203–208, https://doi.org/10.1016/j.foodchem.2019.05.065
  19. Neves, L., Moraes, D. M. 2005. Vigour And A-Amylase Analisis In Seeds Of Rice Cultivars Submitted To Several Treatments With Acetic Acid. Revista de Ciências Agroveterinárias, 4(1): 35–43,
  20. Novo, E. M. L. M. 2010. Sensoriamento remoto: princípios e aplicações. 3a edição ed. São Paulo: Blucher.
  21. Oliveira, G. P., Siqueira, S. L., Cecon, P. R., Salomão, L. C. C. 2018. Teores de carboidratos em mangueira “Ubá” submetida a diferentes doses de paclobutrazol. Revista de Ciências Agrárias, 41(3): 749–756, https://doi.org/10.19084/RCA18016
  22. Oliveira, M. B., Figueiredo, M. G. F., Pereira, M. C. T., Mouco, M. A. Do C., Ribeiro, l. M., Simões, M. O. M. 2019. Structural and cytological aspects of mango floral induction using paclobutrazol. Scientia Horticulturae, 262(27): 109057, https://doi.org/10.1016/j.scienta.2019.109057
  23. Oliveira, N., Tinini, R., Costa, D. S., Ramos, R., Wetterich, C., Teruel, B. 2021. Predictive models of chlorophyll content in sugarcane seedlings using spectral images. Engenharia Agrícola, 41(4):475–484 https://doi.org/10.1590/1809-4430-Eng.Agric.v41n4p475-484/2021
  24. Paz-Kagan, T., Shmilovitch, Z., Yermiyahu, U., Rapaport, T., Sperling, O. 2020. Assessing the nitrogen status of almond trees by visible-to-shortwave infrared reflectance spectroscopy of carbohydrates. Computers and Electronics in Agriculture, 178:105755. https://doi.org/10.1016/j.compag.2020.105755
  25. Souza, A. C. F., Lima, J. R. F. 2023. Comportamento dos preços de manga Palmer ao produtor do Vale do Submédio São Francisco. Revista de Economia e Sociologia Rural, 61(1): 1-20. https://doi.org/10.1590/1806-9479.2021.250161
  26. Souza, de A. P., Ferreira, I. J. S., Costa, D. dos S. 2022. Determination of quality atributes and ripening stage using Vis-NIR spectroscopy in intact seriguela and umbu fruits. Revista Engenharia na Agricultura, 30: 127-141. https://doi.org/10.13083/reveng.v3O-0i1.12929
  27. Souza, I. C. O., Maia, G. A. M., Almeida. N. M. Abreu Neto, J. C., Freire, G. S. S. 2020. Sedimentary dynamic and composition of a tidal channel in a tropical hot semi-arid environment, NE Brasil. Anuário do Instituto de Geociência – UFRJ, 43: 144-155. https://doi.org/10.11137/2020_4_144_155.

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