Response Surface and Particle Swarm-Artificial Neural Network Model Optimize the Microwave-assisted Extraction of Paeoniflorin

DU Meiling; CHEN Zhihong; ZHU Xuanchi; LAN Hongyu; LI Yong; ZHANG Xiuling

doi:10.13386/j.issn1002-0306.2022110133

Volume 44 Issue 15

Jul. 2023

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Science and Technology of Food Industry > 2023 > 44(15): 248-257. > DOI: 10.13386/j.issn1002-0306.2022110133

DU Meiling, CHEN Zhihong, ZHU Xuanchi, et al. Response Surface and Particle Swarm-Artificial Neural Network Model Optimize the Microwave-assisted Extraction of Paeoniflorin[J]. Science and Technology of Food Industry, 2023, 44(15): 248−257. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022110133.

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Response Surface and Particle Swarm-Artificial Neural Network Model Optimize the Microwave-assisted Extraction of Paeoniflorin

DU Meiling^{1, 2,},
CHEN Zhihong³,
ZHU Xuanchi⁴,
LAN Hongyu⁵,
LI Yong^{1, 2, ,},
ZHANG Xiuling^3, ,

1.
College of Light Industry and Textile, Qiqihar University, Qiqihar 161006, China
2.
Engineering Research Center for Hemp and Product in Cold Region of Ministry of Education, Qiqihar 161006, China
3.
Food Science College, Northeast Agricultural University, Harbin 150030, China
4.
Electrical and Information College, Northeast Agricultural University, Harbin 150030, China
5.
Qiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, China

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Received Date: November 13, 2022
Available Online: June 05, 2023

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Abstract

Abstract

The effects of the following independent variables-micro intensity, extraction time, ethanol concentration, and solvent-to-solid ratio on the extraction yield of paeoniflorin from Paeoniae Radix Rubra were examined by single factor test and Box-Behnken design. The antioxidant activity in vitro of extractions was also assessed. Then, the paeoniflorin extraction process was optimized using the response surface methodology (RSM) and particle swarm optimization-artificial neural network (PSO-ANN). Results showed that the prediction and optimization performance of PSO-ANN was better than RSM, that with the correlation coefficient R² was 0.9925 and 0.9099, respectively. The optimized extraction conditions by PSO-ANN were as follows: Ethanol concentration (81% v/v), solvent-to-solid ratio (30 mL/g), extraction time (22 s), extractions (5 times), and micro intensity (420 W). Under the optimized parameters, the extraction yield of paeoniflorin was 378.977±1.982 mg PE/g d.w.. The scavenging rates of paeoniflorin extract (100 μg/mL) on DPPH and ABTS⁺ free radicals were 87.61% and 80.74% respectively, that closed to the positive control. The extract also had a certain reduction ability. The results of this study provide a new method for optimizing the extraction process, as well as provide a theoretical basis for the application of effective components of Paeoniae Radix Rubra as additives.

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