Viscoelastic behaviour, sensitivity analysis and process optimization of aloe Vera/HM pectin mix gels: An investigation using RSM and ANN and its application to food gel formulation
Kiran Patruni, P. Srinivasa Rao
Abstract
Reconstituted Aloe vera non-fibrous alcohol insoluble residue (NFAIR) hydrogels were utilized to combine gel formation with HM pectin samples in order to minimize the additive. The Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to examine the impact of the Aloe vera/HM pectin mix ratio (0.25–1.0), sucrose (0–60% w/w), and pH (3–7) on the responses to the Power law model fitted (RSM). The RSM and ANN statistical performance was assessed based on the validation data set utilizing the coefficient of determination (R2), root mean square error (RMSE), standard error of prediction (SEP), model predictive error (MPE), and absolute average deviation (AAD). The validation data produced coefficients of determination (R2) for the RSM and ANN models that were 0.869 and 0.991, respectively. According to the commonly used steepest ascent numerical optimization technique, the addition of Aloe vera/HM pectin with a mix ratio (r) ranging from 0.40 to 0.59 can lower sucrose from 60% to 40% weight-weight while maintaining a similar gel strength and being more appealing. According to the results, Aloe vera was a good choice for the development of HM pectin mix gel formation with the addition of less sugar.