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


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.

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Fig. 1
Fig. 2





Artificial neural network


Average absolute percentage deviation


Gold nanoparticles


Individual percentage deviation


Root mean square error

R2 :

Coefficient of determination


Response surface methodology

x1 :

Leaf extract concentration

x2 :


x3 :

Gold chloride concentration

x4 :



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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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Correspondence to Gonzalo Astray.

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Saha, N., Astray, G. & Dutta Gupta, S. Modelling and Optimization of Biogenic Synthesis of Gold Nanoparticles from Leaf Extract of Swertia chirata Using Artificial Neural Network. J Clust Sci 29, 1151–1159 (2018).

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  • Swertia chirata
  • Green synthesis
  • Gold nanoparticles
  • Modelling
  • Artificial neural networks