Optimization of total flavonoid compound extraction from Gynura medica leaf using response surface methodology and chemical composition analysis (original) (raw)
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— A use of natural phytochemicals is increasing in different applications over synthetically produced one. Extraction is the very basic process to get natural products from plant material. Due to limited amount of natural products availability, there is a need of predictive modelling to approximate the recovery of products and other characteristics. In this article, an attempt has been made to introduce artificial neural network as modelling and optimization technique for phytochemical extraction process. Literature survey has been made from latest research articles on natural product extraction. A brief about multi response optimization as a future trend is also discussed.
International Journal of Food Science and Technology, 2010
Pink guava puree industry produces huge amount of by-products that have potential as sources for polyphenols. Response surface methodology was implemented to optimise the extraction conditions for phenolics (Y1) and flavonoids (Y2) from a by-product of the guava industry. A three-factor inscribed central composite design was employed to determine the effects of three independent variables, namely pH (X1: 2–6), temperature (X2: 40–60 °C) and time (X3: 1–5 h), on the response variables. The corresponding predicted values for phenolics and flavonoids were 336.30 and 427.35 mg 100 g−1, respectively. Predicted values for extraction rates of phenolics agreed well with experiment values; R2 of 0.902. However, the model derived for flavonoids extraction was less reliable; R2 of 0.983. Increase in time and temperature was found significant in increasing the extraction rate. The optimum conditions for extracting phenolics by ethanolic solvent occurred at a pH of 2 and 60 °C for a 5-h extraction.
Journal of Applied Sciences and Environmental Management, 2020
Bioactive compounds in the fruits of Tetrapleura tetraptera is widely used in food as a flavouring agent and for spices. In this study, bioactive compounds were extracted by solid-liquid extraction process and the yield was optimized by response surface methodology (RSM) and artificial neural network (ANN). The process parameters optimized were the extraction temperature, particle size and extraction time. Box-Behnken Design was used to study the effect of the process parameters on the extract yield. A quadratic model was obtained by RSM which was used topredict the extract yield. While for ANN, Bayesian Regularization learning algorithm with hyperbolic function (Tanh) for both hidden and output layers was the best model for predicting the extract yield. The performance of both models was established based on their R2 and RMSE values. (R2 and RMSE values were 0.9391 and 3.10 for RSM and 0.9637 and 0.8193 for ANN respectively). ANN gave the maximum extract yield of 29.15 % higher tha...
Response Surface Methodology in Engineering Science, 2021
The response surface methodology (RSM) is a relevant mathematical and statistical tool for process optimization. A state of the art on the optimization of the extraction of phenolic compounds from Brassica has shown that this approach is not sufficiently used. The reason for this is certainly an apparent complexity in comparison with the implementation of a one-factor-at-a-time (OFAT) optimization. The objective of this chapter is to show how one implement the response surface methodology in a didactic way on a case study: the extraction of sinapine from mustard bran. Using this approach, prediction models have been developed and validated to predict the sinapine content extracted as well as the purity of the extract in sinapine. The methodology presented in this chapter can be reproduced on any other application in the field of process engineering.