Research Article | OPEN ACCESS
Prediction of the Yield of Enzymatic Synthesis of Betulinic Acid Ester Using Artificial Neural Networks and Support Vector Machine
1Run Wang, 2Qiaoli Mo, 1Qian Zhang, 3Fudi Chen and 3, 4Dazuo Yang
1College of Light Industry, Textile and Food Science Engineering
2College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China
3Key Laboratory of Marine Bio-Resources Restoration and Habitat Reparation in Liaoning Province, Dalian Ocean University, Dalian 116023, China
4College of Life Science and Technology, Dalian University of Technology, Dalian 116021, China
Advance Journal of Food Science and Technology 2016 12:653-662
Received: July ‎7, ‎2015 | Accepted: August ‎2, ‎2015 | Published: December 25, 2016
Abstract
3&beta-O-phthalic ester of betulinic acid is of great importance in anticancer studies. However, the optimization of its reaction conditions requires a large number of experimental works. To simplify the number of times of optimization in experimental works, here, we use Artificial Neural Network (ANN) and Support Vector Machine (SVM) models for the prediction of yields of 3&beta-O-phthalic ester of betulinic acid synthesized by betulinic acid and phthalic anhydride using lipase as biocatalyst. General Regression Neural Network (GRNN), Multilayer Feed-forward Neural network (MLFN) and the SVM models were trained based on experimental data. Four indicators were set as independent variables, including time (h), temperature (°C), amount of enzyme (mg) and molar ratio, while the yield of the 3β-O-phthalic ester of betulinic acid was set as the dependent variable. Results show that the GRNN and SVM models have the best prediction results during the testing process, with comparatively low RMS errors (4.01 and 4.23 respectively) and short training times (both 1s). The prediction accuracy of the GRNN and SVM are both 100% in testing process, under the tolerance of 30%.
Keywords:
Artificial neural network, betulinic acid ester, biocatalyst, support vector machine,
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Competing interests
The authors have no competing interests.
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