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

Abstract

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.

Chlorogenic acid: A comprehensive review of the dietary sources, processing effects, bioavailability, beneficial properties, mechanisms of action, and future directions

“…In particular, the water solubility declines in the following order: 5‐CQA > 5‐FQA > diCQA (Upadhyay & Mohan Rao, 2013). Accordingly, methanol and/or ethanol (EtOH) are usually used as the solvents to extract 5‐CQA from plants sources when using the conventional heat‐reflux extraction (HRE) method (Clifford, Jaganath, Ludwig, & Crozier, 2017; Hicks et al., 2012; Yu, Huang, Lin, Kuo, & Shieh, 2019).…”

“…Recently, an efficient UAE method was developed and used to extract a large amount of 5‐CQA from Lonicera japonica without prolonging extraction time. The optimal conditions to obtain the highest yield of 5‐CQA (45.63 mg/g) were as follows: 65.88% EtOH as the solvent, a liquid/solid ratio of 46:1 (mL/g), extraction at 33.56 °C, and ultrasound power at 150 W (Yu et al., 2019). Moreover, ultrasonic‐assisted aqueous extraction enhances 5‐CQA solubility in water with a 25% reduction in solvent consumption, and also increases extraction efficiency from tobacco leaves (Yu et al., 2019).…”

Response Surface Methodology and Artificial Neural Network Methods Comparative Assessment for Fuel Rich and Fuel Lean Catalytic Combustion

“…The multi-layer perceptron (MLP_ANN) models were superior to the regression model achieving a relatively lower prediction error for modeling Al6082-T6 alloy drilling [24] and better modeling accuracy than RSM for prednisone release from a multipartite System [25], in addition to the superiority for predicting and optimizing the process of ultrasound-assisted extraction [26].…”

Quantification and Optimization of Ethanolic Extract Containing the Bioactive Flavonoids from Millettia pulchra Radix

“…In RSM, the relative importance of each independent variable is evaluated, and optimal operating conditions are determined for each response. The aim of RSM is to reduce the number of experiments required and to determine how different factors affect the experiments [13,14]. Recently, there are several reports on Artificial Neural Network (ANN) as a powerful modelling tool to estimate and optimize the extraction process [15,16].…”