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Development of an artificial neural network utilizing particle swarm optimization for modeling the spray drying of coconut milk


Citation

Lee, Jesse Kar Ming and Anuar, Mohd Shamsul and Mohd Firdaus How, Muhammad Syahmeer How and Mohd Noor, Samsul Bahari and Abdullah, Zalizawati and Taip, Farah Saleena (2021) Development of an artificial neural network utilizing particle swarm optimization for modeling the spray drying of coconut milk. Foods, 10 (11). art. no. 2708. pp. 1-14. ISSN 2304-8158

Abstract

Spray drying techniques are one of the methods to preserve and extend the shelf-life of coconut milk. The objective of this research was to create a particle swarm optimization-enhanced artificial neural network (PSO-ANN) that could predict the coconut milk spray drying process. The parameters for PSO tuning were selected as the number of particles and acceleration constant, respectively, for both global and personal best using a 2k factorial design. The optimal PSO settings were recorded as global best, C1 = 4.0; personal best, C2 = 0; and number of particles = 100. When comparing different types of spray drying models, PSO-ANN had an MSE value of 0.077, GA-ANN had an MSE of 0.033, while ANN had an MSE of 0.082. Sensitivity analysis was conducted on all three models to evaluate the significance level of each parameter on the model, and it was discovered that inlet temperature had the most significant influence on the model performance. In conclusion, the PSO-ANN was found to be more effective than ANN but less effective than GA-ANN in predicting the quality of coconut milk powder.


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Official URL or Download Paper: https://www.mdpi.com/2304-8158/10/11/2708

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.3390/foods10112708
Publisher: Multidisciplinary Digital Publishing Institute
Keywords: Spray drying; Coconut milk; Artificial neural network; Particle swarm optimization; Processes
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 01 Dec 2022 08:29
Last Modified: 01 Dec 2022 08:29
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3390/foods10112708
URI: http://psasir.upm.edu.my/id/eprint/96688
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