Development of an Artificial Neural Network Utilizing Particle Swarm Optimization for Modeling the Spray Drying of Coconut Milk

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 P...

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Bibliographic Details
Published in:Foods 2021-11, Vol.10 (11), p.2708
Main Authors: Ming, Jesse Lee Kar, Anuar, Mohd Shamsul, How, Muhammad Syahmeer, Noor, Samsul Bahari Mohd, Abdullah, Zalizawati, Taip, Farah Saleena
Format: Article
Language:English
Subjects:
Algorithms
artificial neural network
Artificial neural networks
Beverages
Coconut milk
Design
Drying
Factorial design
Food science
Inlet temperature
Mathematical models
Milk
Moisture content
Neural networks
Optimization techniques
Particle swarm optimization
Plant-based beverages
Process parameters
processes
Sensitivity analysis
Shelf life
Spray drying
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Summary: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 2 factorial design. The optimal PSO settings were recorded as global best, C = 4.0; personal best, C = 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.
ISSN:2304-8158
2304-8158
DOI:10.3390/foods10112708