Modelling and prediction of hydrolysis index of gluten-free cookies from cardaba banana starch vis-?-vis response surface methodology and support vector machine

Abstract

The increase in the onset of celiac disease among the world populace had increased the demand for gluten-free products. Therefore, this study aimed at modelling and predicting the hydrolysis index of gluten-free cookies using response surface methodology (RSM) and support vector machine (SVM). The baking temperature (150 -180 ?) and baking time (15-25 min) were varied using a central composite design. The obtained result revealed that both modelling approaches (RSM and SVM) accurately predict the hydrolysis index of the gluten-free cookies owing to their higher coefficient of determinant (R2 > 0.9). The predictive capability assessment of response surface methodology and support vector machine revealed the superiority of support vector machine (0.9658, 0.9329, 0.059) in predicting the hydrolysis index of the gluten-free cookies over response surface model (0.9613, 0.9241, 0.063) owing to its high correlation coefficient (R), Coefficient of determinant (R2) and lower mean square of error as well as root mean square of error (RMSE).

Keywords

• gluten-free cookies
• hydrolysis index
• starch digestibility
• mathematical modelling
• machine learning