2 research outputs found
Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester
3\u3b2-O-phthalic ester of betulinic acid was synthesized from
reaction of betulinic acid and phthalic anhydride using lipase as
biocatalyst. This ester has clinical potential as an anticancer agent.
In this study, artificial neural network (ANN) analysis of Candida
antarctica lipase (Novozym 435) -catalyzed esterification of
betulinic acid with phthalic anhydride was carried out. A multilayer
feed-forward neural network trained with an error back-propagation
algorithm was incorporated for developing a predictive model. The input
parameters of the model are reaction time, reaction temperature, enzyme
amount and substrate molar ratio while the percentage isolated yield of
ester is the output. Four different training algorithms, belonging to
two classes, namely gradient descent and Levenberg-Marquardt (LM), were
used to train ANN. The paper makes a robust comparison of the
performances of the above four algorithms employing standard
statistical indices. The results showed that the quick propagation
algorithm (QP) with 4-9-1 arrangement gave the best performances. The
root mean squared error (RMSE), coefficient of determination (R2) and
absolute average deviation (AAD) between the actual and predicted
yields were determined as 0.0335, 0.9999 and 0.0647 for training set,
0.6279, 0.9961 and 1.4478 for testing set and 0.6626, 0.9488 and 1.0205
for validation set using quick propagation algorithm (QP)