Optimization of spray drying process parameters for the food bioactive ingredients

Abstract

Spray drying (SD) is one of the most important thermal processes used to produce different powders and encapsulated materials. During this process, quality degradation might happen. The main objective of applying optimization methods in SD processes is maximizing the final nutritional quality of the product besides sensory attributes. Optimization regarding economic issues might be also performed. Applying optimization approaches in line with mathematical models to predict product changes during thermal processes such as SD can be a promising method to enhance the quality of final products. In this review, the application of the response surface methodology (RSM), as the most widely used approach, is introduced along with other optimization techniques such as factorial, Taguchi, and some artificial intelligence-based methods like artificial neural networks (ANN), genetic algorithms (GA), Fuzzy logic, and adaptive neuro-fuzzy inference system (ANFIS). Also, probabilistic methods such as Monte Carlo are briefly introduced. Some recent case studies regarding the implementation of these methods in SD processes are also exemplified and discussed.

HIGHLIGHTS

• 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.

Keywords:

• Artificial neural networks
• factorial design
• fuzzy logic
• Monte Carlo
• optimization
• response surface methodology
• Taguchi

Disclosure statement

The authors declare no conflicts of interest.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.