Optimisation of lignin peroxidase production from Phanerochaete chrysosporium using response surface methodology

Abstract

The ligninolytic white rot fungus Phanerochaete chrysosporium, holds good promise as a biological treatment tool due to its ability to produce the lignin peroxidase enzyme which has the potential to degrade a wide variety of hazardous compounds. The effective application of this technique requires optimisation of the process variables to maximise the enzyme production. Response surface methodology was applied to determine the effects of glucose, ammonium tartarate and ferrous sulphate and their mutual interactions on lignin peroxidase production. With a view to simultaneously reducing the number of experiments and obtaining more information on the mutual interactions between the variables, a 2 3 full-factorial central composite experimental design was adopted. The experimental data were ®tted to a second order polynomial equation using multiple regression analysis and also analysed by appropriate statistical methods. Solving the regression equation using the multi-stage monte-carlo optimisation techniques, the optimum process conditions for enhanced production of lignin peroxidase were obtained as: glucose 0.9728 kg/m 3 , ammonium tartarate 0.288 kg/m 3 , and ferrous sulphate 0.097 kg/m 3 .

Optimization and characterization of rice–pigeon pea flour blend using extrusion cooking process

“…The response surface plots showing the synergistic effects of the selected parameters on the EI of the developed pigeon–pea flour blend were presented in Figure 1. The significance of the interactions is determined by the shape of the response surface, based on the circular or elliptical nature as well as the overall ANOVA values (Manimekalai & Swaminathan, 1999). In Figure 1a, the effect of feed blend composition and feed moisture content on EI at fixed screw speed of 200 rpm (centre point) revealed that EI is dependent on all the factors and their synergistic effect resulted in optimum response, as indicated in Table 2, with a p ‐value of <0.0001.…”

Optimisation of porous starch preparation by ultrasonic pretreatment followed by enzymatic hydrolysis

“…The P ‐values are used as a tool to check the significance of each coefficient. The smaller the value of P , the more significant the corresponding coefficient (Manimekalai & Swaminathan, 1999).…”