Improvement of Garlic Crude Protease-based Inhibitive Enzyme Assay for Mercury Using Response Surface Methodology
DOI:
https://doi.org/10.54987/jebat.v7i1.1045Keywords:
Inhibitive assay, Mercury, Garlic crude protease, RSM, CCDAbstract
Enzyme-based assays are an excellent preliminary screening tool with near real-time potential. An inhibitory enzyme assay using crude garlic protease, in conjunction with the casein and Coomassie dye-binding assay, is a sensitive method for detecting mercury. This study employed Response Surface Methodology (RSM) using a Central Composite Design (CCD) to optimize conditions for maximum mercury detection using crude garlic protease and casein as key variables. Twenty CCD experiments were generated with six center point replicates, and the resulting model was statistically significant (F = 367.00, p < 0.0001), showing strong predictive capability (Pred R² = 0.9877; Adj R² = 0.9943). Analysis of variance revealed that garlic protease (B), casein (C), their interaction (BC), and quadratic terms (A², B², C²) significantly influenced the response. Diagnostic plots confirmed the model's robustness, showing minimal residual deviations and no significant outliers. The low coefficient of variation (CV = 5.46%) indicated experimental precision. 3D surface and contour plots highlighted a notable antagonistic interaction between garlic protease and casein concentrations, where their combined effects reduced individual contributions to the response. Model validation demonstrated an excellent correlation between predicted (A595 nm, 0.387) and experimental values (A595 nm, 0.381), confirming the accuracy of the optimization. Optimal conditions were identified at pH 6.78, with garlic protease at 0.534 mg/mL and casein at 0.1176 mg/mL, resulting in a verified response of A595nm = 0.381. Compared to traditional OFAT methods, RSM achieved a 26.27% improvement in the response. This optimized model provides an effective predictive framework for enhancing inhibitive crude garlic protease enzyme assay of mercury. These findings confirm the statistical soundness of RSM, validating its use for multifactorial optimization in an enzyme-based metal detection system.
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