Review on nanocellulose production from agricultural residue through response surface methodology and its applications

Nanocellulose (NC) shows great potential across industries like food, pharmaceuticals, cosmetics, textiles, electronics, and construction. It can be sustainably extracted from agricultural residues using methods such as mechanical processes, acid hydrolysis, and bacterial biosynthesis. This review emphasizes the use of Response Surface Methodology (RSM) in optimizing NC extraction by examining variables like acid concentration, reaction time, and temperature. While RSM is effective, its assumptions of linear and quadratic relationships limit its accuracy in complex systems. Advanced techniques like artificial neural networks (ANN) offer a better alternative, capturing nonlinear relationships more effectively. However, ANN's application in NC extraction is underexplored, calling for future research to improve model precision. Expanding optimization to include response variables like thermal stability and surface charge is also essential for enhancing NC's industrial applications.