Modeling of microwave-assisted extraction of natural dye from seeds of Bixa orellana (Annatto) using response surface methodology (RSM) and artificial neural network (ANN)
By K. Sinha, Shamik Chowdhury, P. Saha, S. Datta. 2013
181 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Industrial and Manufacturing Engineering, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/68a379e2b0a310de1be060a926a9d674b6493325
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In Analytical Chemistry
In Industrial and Manufacturing Engineering
In Water Science and Technology
Citations in the corpus
- Applications of Response Surface Methodology in the Food Industry Processes (2017, methodology): “For example, Sinha et al. (2013) used RSM and ANN to develop predictive models for simulation and optimization of the annatto dye extraction process and they compared both modeling methodologies (RSM and ANN) statistically by R2, root mean square error (RMSE), and AAD based on the validation data…”
- Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annua (2014, methodology): “…at 0.25 g/ml ( f artemisinin from petroleum ether extraction, which is conistent with the findings of other research that compared both ethodologies for natural product extraction (Cheok et al., 2012; architan et al., 2010; Pouralinazar et al., 2012; Sinha et al., 2013; ahedi and Azarpour, 2011).”