Valorization of Neolamarckia cadamba fruit peel into pectin: an integrated RSM–ANN modeling and structural characterization approach

Abstract

The growing demand for sustainable, clean-label food ingredients has driven the search for alternative sources of functional biopolymers like pectin. This study explores fruit peel waste from Neolamarckia cadamba, an underutilized agro-resource, as a novel substrate for pectin extraction. The extraction process was optimized using Response Surface Methodology (RSM), and the predictive capability of the model was further enhanced through Artificial Neural Network (ANN) analysis. ANN outperformed RSM in all statistical metrics (R2, RMSE, MAE), better capturing non-linear process dynamics. Under optimal conditions (pH 2.05, 90 °C, 60 min), a maximum pectin yield of 15.89% (w/w) was achieved. Structural characterization using Fourier Transform Infrared Spectroscopy (FTIR), Liquid Chromatography – Mass Spectrometry (LC-MS), and 1H Nuclear Magnetic Resonance (1H NMR) confirmed the presence of galacturonic acid-rich homogalacturonan. The extracted pectin was subsequently evaluated as a gelling agent in mixed fruit jam formulations at concentrations of 2%, 4%, and 6%, and its performance was compared with that of a commercial jam. The 4% formulation showed favourable texture – hardness, chewiness, and springiness, closely matching the commercial product. Nutritional analysis revealed lower sugar (40.64–42.65 g/100 g), higher protein (4.2–4.5 g/100 g), and enhanced mineral content in the experimental jams. This work highlights N. cadamba peel as a promising, sustainable pectin source and showcases the strength of RSM–ANN integration for optimizing biopolymer extraction.