Open AccessChironji (Buchanania lanzan) fruit juice extraction using cellulase enzyme: modelling and optimization of process by artificial neural network and response surface methodology
Author(s) -
Dileswar Pradhan,
S. Abdullah,
Rama Chandra Pradhan
Publication year - 2020
Publication title -
journal of food science and technology/journal of food science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.656
H-Index - 68
eISSN - 0975-8402
pISSN - 0022-1155
DOI - 10.1007/s13197-020-04619-8
Subject(s) - cellulase , response surface methodology , yield (engineering) , incubation , extraction (chemistry) , chemistry , incubation period , chromatography , food science , enzyme , materials science , biochemistry , composite material
Chironji fruit juice extraction using cellulase enzyme was performed at various concentrations of cellulase, incubation temperature, and time. Artificial neural network (ANN) conjugated with genetic algorithm (GA), and response surface methodology (RSM) were used for attaining the process conditions where the highest juice yield can be achieved. The recommended values of process parameters obtained using ANN-GA method were cellulase concentration of 0.093% (w/w), incubation temperature of 45.7 °C, and incubation time of 116 min. Using RSM method, the recommended values were cellulase concentration of 0.081% (w/w), incubation temperature of 39.6 °C, and incubation time of 99 min. However, the incubation time was found to be the most significant independent process parameter followed by cellulase concentration that affect the juice yield. The juice yield determined experimentally at RSM and ANN-GA recommended conditions was 69.77 ± 0.16% and 70.15 ± 0.12%, respectively. These values indicated that both RSM and ANN-GA methods have comparable accuracies. However, juice extracted using ANN-GA recommended conditions had better physicochemical properties than the juice extracted using RSM recommended conditions.