Synergistic Intensification of Bioactive Compounds in Germinated Brown Rice via Ultrasonic-Enzymatic Pretreatment:...

Area of Science:

• Food Science and Technology
• Biotechnology
• Agricultural Science

Background:

• Germinated brown rice (GBR) offers enhanced nutritional value over regular brown rice.
• Ultrasonic-enzymatic pretreatment is a promising method for improving GBR quality.
• Further research is needed to optimize structural-metabolic interactions and processing parameters.

Purpose of the Study:

• To systematically assess the impact of ultrasonic time, cellulase concentration, and liquid-to-solid ratio on GBR properties.
• To compare the predictive accuracy of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models for process optimization.
• To identify optimal conditions for maximizing beneficial compounds and antioxidant activity while minimizing cooking time.

Main Methods:

• Box-Behnken design for experimental planning.
• Utilized RSM and ANN for predictive modeling of GBR properties.
• Employed Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for multiobjective optimization.
• Scanning Electron Microscopy (SEM) to analyze structural changes.

Main Results:

• The ANN model exhibited superior predictive accuracy compared to RSM.
• Optimized conditions significantly increased gamma-aminobutyric acid (GABA) content and antioxidant properties (DPPH, FRAP).
• Optimal pretreatment reduced the optimal cooking time (OCT) of GBR.
• SEM revealed increased particle breakage and pore formation, indicating effective cavitation-assisted hydrolysis.

Conclusions:

• ANN-based optimization of ultrasonic-enzymatic pretreatment is highly effective for producing functional GBR.
• The optimized process enhances nutritional profile (GABA, phenolics, flavonoids) and technological properties (cooking time).
• Structural modifications induced by cavitation facilitate compound release and improve overall quality.