Ponceau 4R elimination from fruit juice: An integrated optimization strategy utilizing artificial neural networks, least squares, and chitosan-nickel ferrite Nano Sorbent

Abstract

The goal of present work is to examine the efficiency of aminated-chitosan/NiFe2O4 nanoparticles (AmCs/NiFe2O4 NPs) produced for removing Ponceau 4R (P4R) from fruit juice through an adsorption process. The resulting nanoparticles were characterized using various techniques. The modeling of results was done using least squares (LS) and Radial basis function-artificial neural network (RBF-ANN). The optimum removal of P4R (91.43 %) was obtained at the following optimum conditions: pH 4.47, adsorbent dosage 0.047 g/L, contact time approximately 57.78 min, and initial concentration P4R 26.89 mg/L. The highest adsorption capacity (qm) was found to be 208.33 mg g−1. The P4R adsorption mostly followed the Freundlich and pseudo-second-order isotherm kinetic models. Both LS-based models and RBF-ANN provided good predictions for independent variables. The dye elimination efficacy for juice samples were approximately 90.34 %. Therefore, based on the obtained results, it can be claimed that the prepared AmCs/NiFe2O4 NPs can be used to remove P4R.

Keywords

• Chitosan
• Fruit juice
• Response surface methodology
• Nickel ferrite
• Ponceau 4R
• Artificial neural network