L-asparaginase production in solid-state fermentation using Aspergillus niger: process modeling by artificial neural...

Area of Science:

• Biotechnology
• Enzyme Technology
• Industrial Microbiology

Background:

• L-asparaginase is a valuable enzyme with applications in cancer therapy and food processing.
• Current production methods can be costly, necessitating exploration of alternative, economical substrates.
• Agro-industrial waste represents a sustainable and abundant resource for enzyme production.

Purpose of the Study:

• To evaluate niger (Guizotia abyssinica) de-oiled cake as a sole substrate for cost-effective L-asparaginase production via solid-state fermentation.
• To optimize key process parameters influencing L-asparaginase yield.
• To compare the predictive accuracy of artificial neural network (ANN) and response surface methodology (RSM) for process optimization.

Main Methods:

• Solid-state fermentation using niger de-oiled cake as the primary substrate.
• CHNS analysis to determine the substrate's suitability (C/N ratio).
• Optimization of autoclaving time, moisture content, temperature, and pH.
• Modeling and prediction using Artificial Neural Network (ANN) and Response Surface Methodology (RSM).

Main Results:

• Niger de-oiled cake demonstrated favorable C/N content for L-asparaginase synthesis.
• Optimized conditions (30.3 min autoclaving, 62% moisture, 30°C, pH 6.2) yielded a maximum enzyme activity of 34.65 ± 2.18 IU/gds after 96 hours.
• A 1.36-fold improvement in enzyme activity was achieved.
• The ANN model exhibited superior predictive performance over RSM (MSE=0.072, RMSE=0.268, R²=0.99).

Conclusions:

• Niger de-oiled cake is a viable and cost-effective substrate for L-asparaginase production.
• Optimization using ANN and RSM significantly enhances enzyme yield.
• This approach offers a sustainable method for producing L-asparaginase from readily available agro-industrial waste.