Lipase-catalysed synthesis of a novel galanthamine derivative: process optimisation by artificial neural networks
Artificial neural networks (ANNs) analysis was carried out to optimize the esterification of galanthamine and acetic acid in a solvent system. To predict performance parameters of the enzymatic reaction conditions, several parameters were studied which were reaction temperature (50–90 °C), enzyme am...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Elsevier
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/86580/1/Lipase-catalysed%20synthesis.pdf http://psasir.upm.edu.my/id/eprint/86580/ https://www.sciencedirect.com/science/article/abs/pii/S0022286020300855?via%3Dihub |
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| Summary: | Artificial neural networks (ANNs) analysis was carried out to optimize the esterification of galanthamine and acetic acid in a solvent system. To predict performance parameters of the enzymatic reaction conditions, several parameters were studied which were reaction temperature (50–90 °C), enzyme amount (2–5 wt%), reaction time (6–18 h), and substrate molar ratio of galanthamine to acetic acid (2–5:1). The algoritms used in the network were batch back propagation (BBP), incremental back propagation (IBP), genetic algorithm (GA), Levenberg–Marguardt (LM) and quick propagation (QP) algorithms. The configuration of 4 inputs, one hidden layer with 7 nodes, and 1 output using the batch back propagation (BBP) was determined as the optimum algorithm. The predicted and experimental percentage yield value were 60.24% and 60.36%, respectively. These results prove the validity of ANN model. |
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