Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica

Molecules

Hui-Chuan Yu¹  Chwen-Jen Shieh¹  Shang-Ming Huang¹  Wei-Min Lin²  Chia-Hung Kuo³ 

[1] Biotechnology Center, National Chung Hsing University, 250 Kuokuang Road, Taichung 40227, Taiwan;Department of Chemical Engineering, National Chung Hsing University, 145 Xingda Road, Taichung 40227, Taiwan;Department of Seafood Science, National Kaohsiung University of Science and Technology, 142 Haijhuan Road, Nanzih District, Kaohsiung 811, Taiwan;

关键词: Lonicera japonica;    chlorogenic acid;    extraction;    optimization;    response surface methodology;    artificial neural networks;   

DOI  :  10.3390/molecules24122304

来源: DOAJ

【 摘 要 】

Chlorogenic acid (CGA), a bioactive compound commonly found in plants, has been demonstrated possessing nutraceutical potential in recent years. However, the more critical issue concerning how to improve production efficacy of CGA is still limited. It is a challenge to harvest a large amount of CGA without prolonging extraction time. In this study, the feasibility of using ultrasound for CGA extraction from Lonicera japonica was investigated. A central composite design (CCD) was employed to evaluate the effects of the operation parameters, including temperature, ethanol concentration, liquid to solid ratio, and ultrasound power on CGA yields. Meanwhile, the process of ultrasound-assisted extraction was optimized through modeling response surface methodology (RSM) and artificial neural network (ANN). The data indicated that CGA was efficiently extracted from the flower of Lonicera japonica by ultrasound assistance. The optimal conditions for the maximum extraction of CGA were as follows: The temperature at 33.56 °C, ethanol concentration at 65.88%, L/S ratio at 46:1 mL/g and ultrasound power at 150 W. ANN possessed greater optimization capacity than RSM for fitting experimental data and predicting the extraction process to obtain a maximum CGA yield. In conclusion, the process of ultrasound-assisted extraction can be well established by a methodological approach using either RSM or ANN, but it is worth mentioning that the ANN model used here showed the superiority over RSM for predicting and optimizing.

【 授权许可】

Unknown