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Concentration Prediction of Total Flavonoids in Aurantii Fructus Extraction Process:Locally Weighted Regression versus Kinetic Model Equation Based on Fick's Law

Concentration Prediction of Total Flavonoids in Aurantii Fructus Extraction Process:Locally Weighted Regression versus Kinetic Model Equation Based on Fick's Law
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摘要 Objective To predict the total flavonoids concentration of Aurantii Fructus fried with bran in its extraction process. Methods Ultraviolet spectrophotometry was used to determine the concentration of total flavonoids in different extraction time (t) and solvent load (M). Then the predicted procedure was carried out using the following data: 1 ) based on Ficks second law, the parameters of the kinetic model could be deduced and the equation was established; 2) Locally weighted regression (LWR) code was developed in the WEKA software environment to predict the concentration. And then we used both methods to predict the concentration of total flavonoids in new experiments. Results After comparing the predicted results with the experimental data, the LWR model had better accuracy and performance in the prediction. Conclusion LWR is applied to analyze the extraction process of Chinese herb for the first time, and it is totally fit for the extraction. LWR-based system is a more simple and accurate way to predict than the established equation. It is a good choice especially for a process which exists no clear rules, and can be used in the real-time control during the process. Objective To predict the total flavonoids concentration of Aurantii Fructus fried with bran in its extraction process. Methods Ultraviolet spectrophotometry was used to determine the concentration of total flavonoids in different extraction time (t) and solvent load (M). Then the predicted procedure was carried out using the following data: 1 ) based on Ficks second law, the parameters of the kinetic model could be deduced and the equation was established; 2) Locally weighted regression (LWR) code was developed in the WEKA software environment to predict the concentration. And then we used both methods to predict the concentration of total flavonoids in new experiments. Results After comparing the predicted results with the experimental data, the LWR model had better accuracy and performance in the prediction. Conclusion LWR is applied to analyze the extraction process of Chinese herb for the first time, and it is totally fit for the extraction. LWR-based system is a more simple and accurate way to predict than the established equation. It is a good choice especially for a process which exists no clear rules, and can be used in the real-time control during the process.
出处 《Chinese Herbal Medicines》 CAS 2015年第1期69-74,共6页 中草药(英文版)
基金 National Nature Science Foundation of China(surface project)(81173563)
关键词 Aurantii Fructus kinetic model locally weighted regression total flavonoids prediction Aurantii Fructus kinetic model locally weighted regression total flavonoids prediction
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