Multivariate Statistical Optimization of Enzyme Immobilization Onto Solid Matrix Using Central Composite Design

Abstract

In recent years, scientist have been used alternative technology in order to increase enzyme stability and also reduce the cost of production of enzyme. Immobilization methods have attracted the attention of scientists due to its advantages in comparison with soluble enzyme or other methods. Immobilization process can be affected by many factors for this reason it is important to optimize the effective factors in order to enhance success of this process. In preliminary studies, Bradford protein assay was used for determination of protein concentration. In order to increase sensitivity and accuracy of this assay, Bradford protein assay was combined with a multivariate calibration methods. Genetic Inverse Least Squares (GILS) and Partial Least Squares (PLS) were used for multivariate calibration. Calibration model was constructed for various concentration of Bovine Serum Albumin (BSA). Standard Error of Calibration (SEC) and Standard Error of Prediction (SEP) were calculated and results of multivariate calibration method were compared with univariate calibration methods and each other. In this study, the bovine serum albumin immobilization studies were carried out. The bovine serum albumin was immobilized on chitosan nanoparticles and effective factors such as chitosan concentration, immobilization time, pH and temperature were optimized by using central composite design (CCD). Central composite design is used to investigate interaction between these parameters and to find the optimum values of effective factors.