Optimization of spray drying process parameters for the food bioactive ingredients.

The quality of spray dried products can be enhanced using different optimization methods.Drying air temperature and flow rate, feed flow rate, wall material concentration, and atomization pressure are the most significant adjustable input variables in the optimization of the spray drying process.Product yield, moisture content, water activity, hygroscopicity, solubility, total color differences, particle size, bulk density, glass transition temperature, encapsulation efficiency, and viability (about probiotics) are the most important output variables in the optimization of the spray drying process.Response surface methodology (RSM), is the most widely used approach in spray drying optimization.Artificial intelligence-based methods like artificial neural networks (ANN), genetic algorithms (GA), Fuzzy logic, and adaptive neuro-fuzzy inference system (ANFIS) have a great potential in spray drying optimization.Probabilistic methods such as Monte Carlo are able to predict and optimize the spray drying process.