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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.

Show MeSH
Three dimensional plot showing the effect of amount of enzyme, substrate molar ratio and their mutual effect on the synthesis of wax esters. Other variables are constant: incubation time, 5 h and temperature, 50°C.
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Figure 8: Three dimensional plot showing the effect of amount of enzyme, substrate molar ratio and their mutual effect on the synthesis of wax esters. Other variables are constant: incubation time, 5 h and temperature, 50°C.

Mentions: Figure 8 represents the effect of varying amount of enzyme and substrate molar ratio on alcoholysis at 5 h and 50°C. At low amount of enzyme and low substrate molar ratio, the yield was lower. Reaction with high amount of enzyme and substrate molar ratio of 1:3 – 3.5 showed maximal percentage yields. The presence of higher amount of substrates generally increases the probability of substrate enzyme collision [29], and increasing amount of enzyme will lead to an increased percentage yield. This relationship holds when there are no limiting factors such as a low substrate concentration, presence of activators or inhibitors or mass transfer effect. The percentage yield was slightly decreased at substrate molar ratio 1:4. It is known that hydrophilic substrates have the capability of stripping off even the essential water from the enzyme surface, leading to insufficiently hydrated enzyme molecule and in turn to a decrease in enzyme activity [30]. However, two authors reported that even at high substrate levels and low enzyme concentration, high conversion could be achieved which is relevant from the economic point since the cost of enzyme is usually higher than that of substrate [[9] and [27]].


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)

Three dimensional plot showing the effect of amount of enzyme, substrate molar ratio and their mutual effect on the synthesis of wax esters. Other variables are constant: incubation time, 5 h and temperature, 50°C.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Three dimensional plot showing the effect of amount of enzyme, substrate molar ratio and their mutual effect on the synthesis of wax esters. Other variables are constant: incubation time, 5 h and temperature, 50°C.
Mentions: Figure 8 represents the effect of varying amount of enzyme and substrate molar ratio on alcoholysis at 5 h and 50°C. At low amount of enzyme and low substrate molar ratio, the yield was lower. Reaction with high amount of enzyme and substrate molar ratio of 1:3 – 3.5 showed maximal percentage yields. The presence of higher amount of substrates generally increases the probability of substrate enzyme collision [29], and increasing amount of enzyme will lead to an increased percentage yield. This relationship holds when there are no limiting factors such as a low substrate concentration, presence of activators or inhibitors or mass transfer effect. The percentage yield was slightly decreased at substrate molar ratio 1:4. It is known that hydrophilic substrates have the capability of stripping off even the essential water from the enzyme surface, leading to insufficiently hydrated enzyme molecule and in turn to a decrease in enzyme activity [30]. However, two authors reported that even at high substrate levels and low enzyme concentration, high conversion could be achieved which is relevant from the economic point since the cost of enzyme is usually higher than that of substrate [[9] and [27]].

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