| Summary: | 碩士 === 國立中興大學 === 化學工程學系所 === 106 === Lonicera japonica is a traditional Chinese herbal medicine in East Asia. Flower of buds of L. japonica, called honeysuckle, have been applied to anti-oxidant, anti-bacterial、anti-hypertensive effects and anti-diabetes, etc. Chlorogenic acid (CGA), the ester formed between caffeic acid and quinic acid, is a well-known phenolic compound. The known chlorogenic acid, a primary bioactive compound in flower buds, has more pharmacological activities. Many studies have indicated that chlorogenic acid possesses many benefits for health, such as anti-oxidant, whitening, anti-bacterial, antihypertensive effects, anti-obesity, anti-diabetes, neuroprotective properties and anti-tumor. Thus, chlorogenic acid is considerable potential for future development.
To extract the bioactive compound in the plant, the traditional extraction method is conventionally performed by heat-reflux extraction with alcohol as the solvent. However, this method is very time-consuming for heat-reflux extraction. To improve the shortcomings of the traditional extraction method, ultrasonic-assisted extraction has been applied to extract the active ingredients of plants in recent years. The ultrasound could create cavitation in the liquid solution to increase the efficiency of extraction as well as reduce extraction time and solvent consumption.
This study used ultrasonic-assisted extraction and ethanol as a solvent for extraction. The extraction efficiencies of the ultrasonic-assisted extraction and the shaking bath extraction system were compared. The experimental results showed that the ultrasonic-assisted extraction reached its maximum value after 10 minutes of extraction, followed by a steady state for extraction. However, the extraction by shaking bath for 60 minutes had not yet reached a maximum yield, which was inferior to ultrasound-assisted extraction. Using the mass transfer coefficient (KT) for comparison, the calculation results showed that the ultrasonic-assisted extraction method was better than the shaking extraction method. Moreover, we investigated parameter effects on extraction. The parameters are respectively temperature (X1: 30–70°C), ethanol concentration (X2: 55–95%), liquid to solid ratio (X3: 10:1–50:1 mL/g) and ultrasonic power (X4: 90–150W). This study was designed to optimize the ultrasound-assisted extraction of chlorogenic acid of Lonicera japonica using a response surface methodology (RSM) and central composite design (CCD; five-level-four-factors). According to the RSM, we found out the optimum extraction conditions and predicted extraction yield. Additionally, artificial neural network (ANN) is further performed to predict, analyze and compare with the predicted results of RSM. By analysis of variance (ANOVA), the R2 of RSM and ANN were 0.7913 and 0.9898, respectively, and the RMSE, MAD, and MAPE of ANN were better than RSM. The optimal conditions of extraction process were as follows: temperature of 33.56°C, ethanol concentration of 65.88%, liquid to solid ratio of 46:1 mL/g and ultrasonic power of 150W. The RSM showed that the predicted yield was 45.63 mg/g. The ANN showed that the predicted yield was 44.78 mg/g. The actual experimental value was 43.13±1.44 mg/g. Overall, the above experimental results show that ANN has higher prediction accuracy than RSM.
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