Chemical attributes of corn under the path analysis in an Amazon ecosystemic domain

Authors

  • Luana da Silva Pinheiro Federal Rural University of the Amazon
  • Mateus Monteles Vieira Federal University of Maranhão https://orcid.org/0000-0003-3756-8946
  • Gabriel Gustavo Tavares Nunes Federal Rural University of the Amazon
  • Claudete Rosa da Silva Federal Rural University of the Amazon https://orcid.org/0000-0001-5063-8932
  • Job Teixeira Oliveira Federal University of Mato Grosso do Sul https://orcid.org/0000-0001-9046-0382
  • Tulio Russino Castro Federal University of Mato Grosso do Sul
  • Cassiano Garcia Roque Federal University of Mato Grosso do Sul https://orcid.org/0000-0001-6872-0424
  • Priscilla Andrade Silva Federal University of the Amazon

DOI:

https://doi.org/10.13083/reveng.v32i1.16811

Keywords:

agroecology, biotechnology, precision agriculture, productivity, Zea mays L.

Abstract

The present study aims to expose information about the dynamics between the chemical attributes of corn and the direct and indirect influence of these attributes on the proteins of the grain. The attributes analyzed were grain mass, total soluble solids, pH, total titratable acidity, ashes, moisture content, lipids, proteins, and carbohydrates. A network of correlations was obtained and descriptive statistical results of the attributes were generated. Through a path analysis, in which protein content was the main variable, the direct and indirect correlation between the attributes was determined. The moisture level and ash content obtained for the corn were similar to those found in literature. The levels of protein, lipids, and carbohydrates were lower than those established in the Brazilian Table of Food Composition. It was concluded that lipids were the attributes that best determine corn proteins.

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References

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Published

2024-08-23

How to Cite

Pinheiro, L. da S., Vieira, M. M., Nunes, G. G. T., Silva, C. R. da, Oliveira, J. T., Castro, T. R., Roque, C. G., & Silva, P. A. (2024). Chemical attributes of corn under the path analysis in an Amazon ecosystemic domain. Engineering in Agriculture, 32(Contínua), 37–46. https://doi.org/10.13083/reveng.v32i1.16811

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