Abstract
A new method based on accelerated solvent extraction (ASE) combined with response surface methodology (RSM) modeling and optimization has been developed for the extraction of four lignans in Fructus Schisandrae (the fruits of Schisandra chinensis Baill). The RSM method, based on a three level and three variable Box-Behnken design (BBD), was employed to obtain the optimal combination of extraction condition. In brief, the lignans schizandrin, schisandrol B, deoxyschizandrin and schisandrin B were optimally extracted with 87% ethanol as extraction solvent, extraction temperature of 160 °C, static extraction time of 10 min, extraction pressure of 1,500 psi, flush volume of 60% and one extraction cycle. The 3D response surface plot and the contour plot derived from the mathematical models were applied to determine the optimal conditions. Under the above conditions, the experimental value of four lignans was 14.72 mg/g, which is in close agreement with the value predicted by the model.
Highlights
The construction of mathematical models for prediction of target compound extraction and separation is an important tool in the field of natural product chemistry [1]
Technique can simulate and optimize complex processes because it allows more efficient and easier arrangement and interpretation of experiments compared to other traditional method. It is less laborious and time-consuming than other methods. In terms of these advantages, it is widely employed in optimizing the extraction of natural components including phenolics [6,7,8], chromones [4], saponins [3], and polysaccharides [9]
The whole design consisted of 15 experimental points as listed in Table 1, and three replicates at the center of the design were used for estimating a pure error sum of squares
Summary
The construction of mathematical models for prediction of target compound extraction and separation is an important tool in the field of natural product chemistry [1]. Response surface methodology (RSM) is an effective modeling tool to solve linear and non-linear multivariate regression problems [4,5,6]. The RSM technique can simulate and optimize complex processes because it allows more efficient and easier arrangement and interpretation of experiments compared to other traditional method. It is less laborious and time-consuming than other methods. In terms of these advantages, it is widely employed in optimizing the extraction of natural components including phenolics [6,7,8], chromones [4], saponins [3], and polysaccharides [9]
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