Abstract
The artificial neural network (ANN), response surface methodology (RSM), and adaptive neuro-fuzzy inference system (ANFIS) were applied to predict the yield of fermentable sugar from Colocynthis vulgaris Shrad seeds shell (CVSSS). Enzymatic hydrolysis was done by applying Aspergillus Niger. The ANN, RSM, and ANFIS models got investigated based on; hydrolysing temperature, time and pH as input variables, whereas the percentage yield of sugar was the response factor. Statistical error tasks were additionally, applied to relate the adequacy of the models. The result showed that, sugar can be obtained from CVSSS. The ANFIS, ANN, and RSM tools presented a nigh perfection, in predicting the yield of fermentable sugar from CVSSS with R squared value of 0.9986, 0.9978, and 0.9975, correspondingly. Additional statistical guides gave acceptance to ANFIS and RSM as the best and the least predictive tools respectively. Optimization result with ANFIS tool, presented an optimum hydrolysis productivity of 60.65%.
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