Abstract
Machine learning techniques were employed to evaluate the effect of process parameters viz. microwave power (100 W, 300 W, 600 W); pH (1, 1.5, 2); and microwave time (the 60 s, 120 s, 180 s) on the pectin yield from Citrus limetta peel. A fourth-order polynomial function of 66.60 scales was used by the Support Vector Regression (SVR) model at an epsilon (ε) value of 0.003. The co-efficient of determination (R2) and root mean square error-values for training data and test data were 0.984; 0.77 and 0.993; 0.66 respectively. At optimized conditions, microwave power 600 W, pH 1, and time 180 s the best yield of 32.75% was obtained. The integrity of pectin skeletal was confirmed with FTIR and 1H NMR spectrums. The physicochemical analysis revealed that CLP is a high-methoxyl pectin (HMP) with a 63.20 ± 0.88% degree of esterification, 798.45 ± 26.15 equivalent weight, 8.06 ± 0.62% methoxyl content, 67.93 ± 3.36 AUA content, 6.27 ± 0.27 g water/g pectin WHC, 2.68 ± 0.20 g oil/g pectin OHC, low moisture, ash and protein content of 6.85 ± 0.10%, 3.87 ± 0.10% and 2.61 ± 0.06% respectively, which can be utilized as a food additive. Therefore, pectin extraction from Citrus limetta peel using a greener technique like MAE is an eco-friendly, time-saving approach to transform waste into a versatile food additive.