Analysis of the growth curve of Tommy Atkins mango fruits by non-linear models

Authors

  • Edilene Azarias Universidade Federal de Lavras https://orcid.org/0000-0002-5580-3754
  • Natiele de Almeida Gonzaga Universidade Federal de Lavras
  • Rafaela de Carvalho Salvador Universidade Federal de Lavras https://orcid.org/0000-0001-7519-7280
  • Edilson Marcelino Silva Universidade Federal Rural do Rio de Janeiro
  • Joel Augusto Muniz Universidade Federal de Lavras

Keywords:

Regression, Model Gompertz, Model von Bertalanffy, Dry mass, Fresh mass

Abstract

Mango is a tropical fruit appreciated not only for its flavor but also for its health benefits, as it is rich in vitamins A and C, and contains over 20 different substances and minerals. The fruit is perishable with a short shelf life, making it essential to study the growth curve of mango fruits to assist in management methods. The objective of this study was to analyze the growth curves of Tommy Atkins mango fruits, considering both fresh and dry masses, using the nonlinear Gompertz and von Bertalanffy models. Nineteen samples were collected starting five days after anthesis (DAA) and ending when the mangoes reached ripening stage. Model parameter estimation was conducted using the least squares method with the Gauss-Newton iterative method. Quality of fit was evaluated using coefficients of determination, Akaike information criterion, residual standard deviation, and Bates and Watts curvatures. Based on these evaluations, the Gompertz model was deemed most suitable for describing the data. The asymptotic weight of fresh mango fruit was approximately 419 g, with stabilization of fresh mass gain occurring at 89 DAA, accumulating to 355 g. For dry mass, the asymptotic weight was around 89 g, with stabilization occurring later at 137 DAA, accumulating approximately 75 g.

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Published

04-11-2024

How to Cite

Azarias, E., Gonzaga, N. de A., Salvador, R. de C., Silva, E. M., & Muniz, J. A. (2024). Analysis of the growth curve of Tommy Atkins mango fruits by non-linear models. Sigmae, 13(4), 187–195. Retrieved from https://publicacoes.unifal-mg.edu.br/revistas/index.php/sigmae/article/view/2492

Issue

Section

Probability and Statistics