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 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.

The effect of drying temperature and sodium caseinate concentration on the functional and physical properties of spray-dried coconut milk.

This study investigated the effect of drying temperature on the stability and quality of spray-dried coconut milk. A low concentration (1-2% w/w) of sodium caseinate (SC) was used as emulsifying agent with 8-9% of maltodextrin. The spray drying temperature was varied from 140 to 180 °C. Emulsions prepared at different SC concentration remained stable without phase separation for 24 h. Higher the SC concentration produced smaller-sized of droplet and powder particles. The spray dried coconut milk has a skin-forming structure. Emulsion with low concentration of SC (1% w/w) is unstable during atomisation process due to re-coalescence of fat. Adding SC to the emulsion reduce the moisture content to less than 5%. However, drying the emulsions at 180 °C gave negative impact to the powder properties. Some particles rupture and lead to high free fat content, high insolubility and larger fat droplet size. Presence of fleck is also noticed in the powder.

Nonlinear Model-Based Inferential Control of Moisture Content of Spray Dried Coconut Milk.

The moisture content of a powder is a parameter crucial to be controlled in order to produce stable products with a long shelf life. Inferential control is the best solution to control the moisture content due to difficulty in measuring this variable online. In this study, fundamental and empirical approaches were used in designing the nonlinear model-based inferential control of moisture content of coconut milk powder that was produced from co-current spray dryer. A one-dimensional model with integration of reaction engineering approach (REA) model was used to represent the dynamic of the spray drying process. The empirical approach, i.e., nonlinear autoregressive with exogenous input (NARX) and neural network, was used to allow fast and accurate prediction of output response in inferential control. Minimal offset (<0.0003 kg/kg) of the responses at various set points indicate high accuracy of the neural network estimator. The nonlinear model-based inferential control was able to provide stable control response at wider process operating conditions and acceptable disturbance rejection. Nevertheless, the performance of the controller depends on the tuning rules used.