Review on nanocellulose production from agricultural residue through response surface methodology and its applications
Marjun C. Alvarado,
Ma. Cristine Concepcion D. Ignacio,
Ma. Camille G. Acabal,
Anniver Ryan P. Lapuz,
Kevin F. Yaptenco
Affiliations
Marjun C. Alvarado
Agricultural Food and Bioprocess Engineering Division (AFBED), Institute of Agricultural and Biosystems Engineering, College of Engineering and Agro‐industrial Technology, University of the Philippines‐Los Baños, College Batong Malake, Laguna 4031, Philippines; Corresponding author.
Ma. Cristine Concepcion D. Ignacio
Agricultural Food and Bioprocess Engineering Division (AFBED), Institute of Agricultural and Biosystems Engineering, College of Engineering and Agro‐industrial Technology, University of the Philippines‐Los Baños, College Batong Malake, Laguna 4031, Philippines
Ma. Camille G. Acabal
Agricultural Food and Bioprocess Engineering Division (AFBED), Institute of Agricultural and Biosystems Engineering, College of Engineering and Agro‐industrial Technology, University of the Philippines‐Los Baños, College Batong Malake, Laguna 4031, Philippines
Anniver Ryan P. Lapuz
Department of Science and Technology Forest Products Research and Development Institute (DOST-FPRDI), Laguna 4031, Philippines
Kevin F. Yaptenco
Agricultural Food and Bioprocess Engineering Division (AFBED), Institute of Agricultural and Biosystems Engineering, College of Engineering and Agro‐industrial Technology, University of the Philippines‐Los Baños, College Batong Malake, Laguna 4031, Philippines
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.
