Journal of Cluster Science

, Volume 29, Issue 6, pp 1151–1159 | Cite as

Modelling and Optimization of Biogenic Synthesis of Gold Nanoparticles from Leaf Extract of Swertia chirata Using Artificial Neural Network

  • Nirlipta Saha
  • Gonzalo AstrayEmail author
  • S. Dutta Gupta
Original Paper


Swertia chirata is a medicinal plant studied for its ability to synthesize polyshaped gold nanoparticles (AuNP). The process of AuNP biosynthesis was studied using artificial neural networks (ANN) with different activation function on output node (logistic or linear) and different training algorithm (back propagation or Levenberg–Marquardt). The maximum biosynthesis was checked under the optimized condition of 17.24% leaf extract, pH 4.61, gold chloride concentration 4 mM and temperature 53.61 °C. A significant improvement in the model efficiency for predicting AuNP biosynthesis around 37.60%, in terms of root mean square error was obtained with the developed ANN-linear2 model, compared to the traditional response surface methodology.


Swertia chirata Green synthesis Gold nanoparticles Modelling Artificial neural networks 





Artificial neural network


Average absolute percentage deviation


Gold nanoparticles


Individual percentage deviation


Root mean square error


Coefficient of determination


Response surface methodology


Leaf extract concentration




Gold chloride concentration





Astray G. thanks Xunta de Galicia, Consellería de Cultura, Educación e Ordenación Universitaria, for his postdoctoral Grant B, POS-B/2016/001, K645 P.P.0000 421S 140.08.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Agricultural and Food EngineeringIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.Department of Physical Chemistry, Faculty of ScienceUniversity of VigoOurenseSpain

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