Disruption of Pseudomonas putida by high pressure homogenization: a comparison of the predictive capacity of three process models for the efficient release of arginine deiminase

Mahesh D. Patil, Gopal Patel, Balaji Surywanshi, Naeem Shaikh, Prabha Garg, Yusuf Chisti, Uttam Chand Banerjee

Disruption of Pseudomonas putida KT2440 by high-pressure homogenization in a French press is discussed for the release of arginine deiminase (ADI). The enzyme release response of the disruption process was modelled for the experimental factors of biomass concentration in the broth being disrupted, the homogenization pressure and the number of passes of the cell slurry through the homogenizer. For the same data, the response surface method (RSM), the artificial neural network (ANN) and the support vector machine (SVM) models were compared for their ability to predict the performance parameters of the cell disruption. The ANN model proved to be best for predicting the ADI release. The fractional disruption of the cells was best modelled by the RSM. The fraction of the cells disrupted depended mainly on the operating pressure of the homogenizer. The concentration of the biomass in the slurry was the most influential factor in determining the total protein release. Nearly 27 U/mL of ADI was released within a single pass from slurry with a biomass concentration of 260 g/L at an operating pressure of 510 bar. Using a biomass concentration of 100 g/L, the ADI release by French press was 2.7-fold greater than in a conventional high-speed bead mill. In the French press, the total protein release was 5.8-fold more than in the bead mill. The statistical analysis of the completely unseen data exhibited ANN and SVM modelling as proficient alternatives to RSM for the prediction and generalization of the cell disruption process in French press.