Optimization Of Drying Kinetics, Physical Properties, And Predictive Modeling of Pumpkin (Cucurbita Maxima) Powder: A Comparative Analysis of Mathematical and Ann Approaches

Authors

  • Sinthiya R Research Scholar, Department of Food Processing and Preservation Technology, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Avinashilingam University, Coimbatore (India). Author
  • Dr.ALovelin Jerald Department of Food Processing and Preservation Technology, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Avinashilingam University, Coimbatore (India). Author
  • Veerapandi Loganathan Department of Food Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, (India). Author

DOI:

https://doi.org/10.48047/n8f81t02

Keywords:

Pumpkin powder (PP), Drying kinetics, Artificial Neural Network (ANN), Mathematical modelling, Functional food ingredient, Physical properties.

Abstract

This study investigates the drying kinetics, physical properties, and predictive modelling of pumpkin (Cucurbita
maxima) powder using both mathematical and artificial neural network (ANN) approaches. Pumpkin, a rich
source of beta-carotene and antioxidants, holds significant potential as a functional food ingredient. The drying
process was performed at three temperatures (65°C,70°C,75°C) using a tray dryer to examine its effects on
moisture content, drying rates, and product quality. The results revealed that higher drying temperatures
accelerated moisture reduction but also caused nutrient degradation and texture changes. Among the tested
temperatures, 70°C provided the best balance between efficiency and quality, while 75°C achieved the fastest
drying rate. Mathematical modelling using five drying models, including the Logarithmic and Diffusion models,
demonstrated excellent correlation coefficients (R²), indicating their efficacy in predicting drying kinetics

Downloads

Download data is not yet available.

Downloads

Published

2025-02-20

Issue

Vol. 54 No. 4 (2025): Year 2025, Volume 54, Issue 4

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

How to Cite

Optimization Of Drying Kinetics, Physical Properties, And Predictive Modeling of Pumpkin (Cucurbita Maxima) Powder: A Comparative Analysis of Mathematical and Ann Approaches (S. R, A. Jerald, & V. . Loganathan , Trans.). (2025). Cuestiones De Fisioterapia, 54(4), 5454-5465. https://doi.org/10.48047/n8f81t02