Response surface methodology as a tool for modelling galacto-oligosaccharide production.

Area of Science:

• Biotechnology
• Food Science
• Enzyme Technology

Background:

• Galacto-oligosaccharides (GOS) are valuable prebiotics.
• Cheese whey is an abundant byproduct with potential for GOS production.
• Optimizing GOS synthesis requires understanding enzyme dose and substrate concentration effects.

Purpose of the Study:

• To investigate the impact of β-galactosidase enzyme dose and cheese whey concentration on GOS production.
• To develop predictive mathematical models for GOS concentration, yield, and reaction time.
• To identify optimal reaction conditions for manufacturing GOS-enriched products.

Main Methods:

• Central Composite Rotational Design (CCRD) for experimental planning.
• Response Surface Methodology (RSM) for data modeling.
• Analysis of variance (ANOVA) to assess model significance and lack of fit.

Main Results:

• Mathematical models accurately described GOS concentration, yield, and reaction time within the studied ranges.
• High coefficients of determination (>0.97) indicated strong model fit.
• No significant lack of fit was observed, validating the models' reliability.

Conclusions:

• The developed models provide a reliable tool for optimizing GOS production.
• Optimal conditions can be selected for manufacturing high-value products rich in bioactive compounds.
• This research contributes to the efficient utilization of cheese whey for prebiotic synthesis.