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Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester.

Basri M, Rahman RN, Ebrahimpour A, Salleh AB, Gunawan ER, Rahman MB - BMC Biotechnol. (2007)

Bottom Line: The actual experimental percentage yield was 84.6% at optimum condition, which compared well to the maximum predicted value by ANN (83.9%) and RSM (85.4%).The order of effective parameters on wax ester percentage yield were; respectively, time with 33.69%, temperature with 30.68%, amount of enzyme with 18.78% and substrate molar ratio with 16.85%, whereas R2 and AAD were determined as 0.99998696 and 1.377 for ANN, and 0.99991515 and 3.131 for RSM respectively.Though both models provided good quality predictions in this study, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. mahiran@science.upm.edu.my

ABSTRACT

Background: Wax esters are important ingredients in cosmetics, pharmaceuticals, lubricants and other chemical industries due to their excellent wetting property. Since the naturally occurring wax esters are expensive and scarce, these esters can be produced by enzymatic alcoholysis of vegetable oils. In an enzymatic reaction, study on modeling and optimization of the reaction system to increase the efficiency of the process is very important. The classical method of optimization involves varying one parameter at a time that ignores the combined interactions between physicochemical parameters. RSM is one of the most popular techniques used for optimization of chemical and biochemical processes and ANNs are powerful and flexible tools that are well suited to modeling biochemical processes.

Results: The coefficient of determination (R2) and absolute average deviation (AAD) values between the actual and estimated responses were determined as 1 and 0.002844 for ANN training set, 0.994122 and 1.289405 for ANN test set, and 0.999619 and 0.0256 for RSM training set respectively. The predicted optimum condition was: reaction time 7.38 h, temperature 53.9 degrees C, amount of enzyme 0.149 g, and substrate molar ratio 1:3.41. The actual experimental percentage yield was 84.6% at optimum condition, which compared well to the maximum predicted value by ANN (83.9%) and RSM (85.4%). The order of effective parameters on wax ester percentage yield were; respectively, time with 33.69%, temperature with 30.68%, amount of enzyme with 18.78% and substrate molar ratio with 16.85%, whereas R2 and AAD were determined as 0.99998696 and 1.377 for ANN, and 0.99991515 and 3.131 for RSM respectively.

Conclusion: Though both models provided good quality predictions in this study, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities.

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Neural network topology. Topology of multilayer full feedforward neural network for the estimation of lipase-catalyzed synthesis of palm-based wax ester.
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Figure 2: Neural network topology. Topology of multilayer full feedforward neural network for the estimation of lipase-catalyzed synthesis of palm-based wax ester.

Mentions: The best ANN chosen in the present work was a multilayer full feedforward incremental back propagation network with Tanh transfer function (Table 3, C11) that consisted of a 4-15-1 topology (Figure 2). The optimized values of network for learning rate and momentum were 0.15 and 0.8, respectively. The learning was completed in RMSE = 0.00998, R = 1 and DC = 1. In the case of training data set, the coefficient of determination (R2) and absolute average deviation (AAD) were 1 and 0.002844, respectively, whereas for the testing data set, R2 was 0.994122 and AAD was 1.289405 (Table 4) and for validating data sets R2 and AAD were, 0.99998696 and 1.377, respectively (Table 5). Comparison of predicted and experimental values in training, testing and validating data sets, not only revealed capability of ANN in prediction of known data responses (the data that have been used for training) but also showed the ability of generalization for unknown data (the data that have not been used for training) and implying that empirical models derived from ANN can be used to adequately describe the relationship between the input factors and output in Lipozyme-catalyzed synthesis of wax ester from palm oil and oleyl alcohol.


Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester.

Basri M, Rahman RN, Ebrahimpour A, Salleh AB, Gunawan ER, Rahman MB - BMC Biotechnol. (2007)

Neural network topology. Topology of multilayer full feedforward neural network for the estimation of lipase-catalyzed synthesis of palm-based wax ester.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2211475&req=5

Figure 2: Neural network topology. Topology of multilayer full feedforward neural network for the estimation of lipase-catalyzed synthesis of palm-based wax ester.
Mentions: The best ANN chosen in the present work was a multilayer full feedforward incremental back propagation network with Tanh transfer function (Table 3, C11) that consisted of a 4-15-1 topology (Figure 2). The optimized values of network for learning rate and momentum were 0.15 and 0.8, respectively. The learning was completed in RMSE = 0.00998, R = 1 and DC = 1. In the case of training data set, the coefficient of determination (R2) and absolute average deviation (AAD) were 1 and 0.002844, respectively, whereas for the testing data set, R2 was 0.994122 and AAD was 1.289405 (Table 4) and for validating data sets R2 and AAD were, 0.99998696 and 1.377, respectively (Table 5). Comparison of predicted and experimental values in training, testing and validating data sets, not only revealed capability of ANN in prediction of known data responses (the data that have been used for training) but also showed the ability of generalization for unknown data (the data that have not been used for training) and implying that empirical models derived from ANN can be used to adequately describe the relationship between the input factors and output in Lipozyme-catalyzed synthesis of wax ester from palm oil and oleyl alcohol.

Bottom Line: The actual experimental percentage yield was 84.6% at optimum condition, which compared well to the maximum predicted value by ANN (83.9%) and RSM (85.4%).The order of effective parameters on wax ester percentage yield were; respectively, time with 33.69%, temperature with 30.68%, amount of enzyme with 18.78% and substrate molar ratio with 16.85%, whereas R2 and AAD were determined as 0.99998696 and 1.377 for ANN, and 0.99991515 and 3.131 for RSM respectively.Though both models provided good quality predictions in this study, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. mahiran@science.upm.edu.my

ABSTRACT

Background: Wax esters are important ingredients in cosmetics, pharmaceuticals, lubricants and other chemical industries due to their excellent wetting property. Since the naturally occurring wax esters are expensive and scarce, these esters can be produced by enzymatic alcoholysis of vegetable oils. In an enzymatic reaction, study on modeling and optimization of the reaction system to increase the efficiency of the process is very important. The classical method of optimization involves varying one parameter at a time that ignores the combined interactions between physicochemical parameters. RSM is one of the most popular techniques used for optimization of chemical and biochemical processes and ANNs are powerful and flexible tools that are well suited to modeling biochemical processes.

Results: The coefficient of determination (R2) and absolute average deviation (AAD) values between the actual and estimated responses were determined as 1 and 0.002844 for ANN training set, 0.994122 and 1.289405 for ANN test set, and 0.999619 and 0.0256 for RSM training set respectively. The predicted optimum condition was: reaction time 7.38 h, temperature 53.9 degrees C, amount of enzyme 0.149 g, and substrate molar ratio 1:3.41. The actual experimental percentage yield was 84.6% at optimum condition, which compared well to the maximum predicted value by ANN (83.9%) and RSM (85.4%). The order of effective parameters on wax ester percentage yield were; respectively, time with 33.69%, temperature with 30.68%, amount of enzyme with 18.78% and substrate molar ratio with 16.85%, whereas R2 and AAD were determined as 0.99998696 and 1.377 for ANN, and 0.99991515 and 3.131 for RSM respectively.

Conclusion: Though both models provided good quality predictions in this study, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities.

Show MeSH