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

Abstract

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

Abbreviations

A:

Absorbance

ANN:

Artificial neural network

AAPD:

Average absolute percentage deviation

AuNP:

Gold nanoparticles

IPD:

Individual percentage deviation

RMSE:

Root mean square error

R2 :

Coefficient of determination

RSM:

Response surface methodology

x1 :

Leaf extract concentration

x2 :

pH

x3 :

Gold chloride concentration

x4 :

Temperature

References

  1. 1.

    S. Ahmed, M. Ahmad, B. L. Swami, and S. Ikram (2016). J. Adv. Res. 7, 17.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    P. Singh, Y. J. Kim, D. Zhang, and D. C. Yang (2016). Trends Biotechnol. 34, 588.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    M. Noruzi (2015). Bioprocess Biosyst. Eng. 38, 1.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    M. M. Poojary, P. Passamonti, and A. V. Adhikari (2016). BioNanoScience. 6, 110.

    Article  Google Scholar 

  5. 5.

    K. J. Rao and S. Paria (2015). ACS Sustain. Chem. Eng. 3, 483.

    CAS  Article  Google Scholar 

  6. 6.

    N. Saha and S. Dutta Gupta (2016). Synthesis, characterization and bioactivity of nanoparticles from medicinal plants, in M. Pathak and J. N. Govil (eds.), Recent Progress in Medicinal Plants (pp. 471–501). Studium Press, USA.

  7. 7.

    K. Saha, S. S. Agasti, C. Kim, X. Li, and V. M. Rotello (2012). Chem. Rev. 112, 2739.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    J. F. Hainfeld, D. N. Slatkin, T. M. Focella, and H. M. Smilowitz (2006). Br. J. Radiol. 79, 248.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    A. Oluwasanmi, M. Malekigorji, S. Jones, A. Curtis, and C. Hoskins (2016). RSC Adv. 6, 95044.

    CAS  Article  Google Scholar 

  10. 10.

    S. K. Balakrishnan and P. V. Kamat (2017). ACS Energy Lett. 2, 88.

    CAS  Article  Google Scholar 

  11. 11.

    M. Bonarowska, Z. Kaszkur, G. Slowik, J. Ryczkowski, and Z. Karpinski (2016). ChemCatChem 8, 2625.

    CAS  Article  Google Scholar 

  12. 12.

    T. Kubota, S. Kuroda, T. Morihiro, H. Tazawa, S. Kagawa, and T. Fujiwara (2016). Cancer Res. 76, 4747.

    Article  Google Scholar 

  13. 13.

    P. Lin, F. Chai, R. Zhang, G. Xu, X. Fan, and X. Luo (2016). Microchim. Acta 183, 1235.

    CAS  Article  Google Scholar 

  14. 14.

    M. Cordeiro, F. Ferreira Carlos, P. Pedrosa, A. Lopez, and P. Viana Baptista (2016). Diagnostics 6, 43.

    Article  CAS  PubMed Central  Google Scholar 

  15. 15.

    E. Hao, G. C. Schatz, and J. T. Hupp (2004). J. Fluoresc. 14, 331.

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    N. Saha, P. Trivedi, and S. Dutta Gupta (2016). J. Cluster Sci. 27, 1893.

    CAS  Article  Google Scholar 

  17. 17.

    T. B. Devi and M. Ahmaruzzaman (2017). Chem. Eng. J. 317, 726.

    CAS  Article  Google Scholar 

  18. 18.

    N. Saha and S. Dutta Gupta (2016). J. Cluster Sci. 27, 1419.

    CAS  Article  Google Scholar 

  19. 19.

    M. Rahimi-Nasrabadi, S. M. Pourmortazavi, Z. Rezvani, K. Adib, and M. R. Ganjali (2015). Mater. Manuf. Process. 30, 34.

    CAS  Article  Google Scholar 

  20. 20.

    M. Rohini, P. Reyes, S. Velumani, M. Latha, G. Oza, I. Becerril-Juarez, et al. (2015). Mater. Sci. Semicond. Process. 37, 151.

    CAS  Article  Google Scholar 

  21. 21.

    D. Bas and I. H. Boyaci (2007). J. Food Eng. 78, 846.

    CAS  Article  Google Scholar 

  22. 22.

    A. M. Akintunde, S. O. Ajala, and E. Betiku (2015). Ind. Crops Prod. 67, 387.

    CAS  Article  Google Scholar 

  23. 23.

    G. Astray, B. Gullón, J. Labidi, and P. Gullón (2016). Ind. Crop. Prod. 92, 290.

    CAS  Article  Google Scholar 

  24. 24.

    G. E. P. Box and K. B. Wilson (1951). J. R. Stat. Soc 13, 1.

    Google Scholar 

  25. 25.

    M. J. Zhu, J. Yao, W. B. Wang, X. Q. Yin, W. Chen, and X. Y. Wu (2016). Desalin. Water Treat. 57, 15314.

    CAS  Article  Google Scholar 

  26. 26.

    S. Ghosh, R. Chakraborty, A. Chatterjee, and U. Raychaudhuri (2014). J. Inst. Brew. 120, 550.

    CAS  Google Scholar 

  27. 27.

    T. Kikhavani, S. N. Ashrafizadeh, and B. Van Der Bruggen (2014). J. Appl. Polym. Sci. 131, 39888.

    Article  CAS  Google Scholar 

  28. 28.

    J. S. Min, S. O. Lee, M. I. Khan, D. G. Yim, K. H. Seol, M. Lee, et al. (2015). Lipids Health Dis. 14, 77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    M. Martínez, B. Gullón, R. Yáñez, J. L. Alonso, and J. C. Parajó (2009). J. Agric. Food Chem. 57, 5510.

    Article  CAS  PubMed  Google Scholar 

  30. 30.

    K. M. Desai, S. A. Survase, P. S. Saudagar, S. S. Lele, and R. S. Singhal (2008). Biochem. Eng. J. 41, 266.

    CAS  Article  Google Scholar 

  31. 31.

    S. K. Ashan, M. A. Behnajady, N. Ziaeifar, and R. Khalilnezhad (2017). Neural Comput. Appl. 1, (29), 969.

    Google Scholar 

  32. 32.

    Y. Huang (2009). Algorithms 2, 973.

    Article  Google Scholar 

  33. 33.

    E. A. Perpetuo, D. N. Silva, I. R. Avanzi, L. H. Gracioso, M. P. G. Baltazar, and C. A. O. Nascimento (2012). Environ. Technol. 33, 1739.

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    R. Hosseini Nia, M. Ghaedi, and A. M. Ghaedi (2014). J. Mol. Liq. 195, 219.

    CAS  Article  Google Scholar 

  35. 35.

    K. Salehi, H. Daraei, P. Teymouri, B. Shahmoradi, and A. Maleki (2016). Desalin. Water Treat. 57, 22074.

    CAS  Article  Google Scholar 

  36. 36.

    Y. Li, M. R. Abbaspour, P. V. Grootendorst, A. M. Rauth, and X. Y. Wu (2015). Eur. J. Pharm. Biopharm. 94, 170.

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    J. X. Gao, X. F. Xu, K. X. Song, P. Q. Li, X. H. Guo, and R. H. Liu (2006). Chin. J. Aeronaut. 19, S36.

    Article  Google Scholar 

  38. 38.

    T. Murashige and F. Skoog (1962). Physiol. Plant. 15, 473.

    CAS  Article  Google Scholar 

  39. 39.

    D. Kriesel, A brief introduction to neural networks (2007). http://www.dkriesel.com. Accessed 20 Nov 2017.

  40. 40.

    G. Astray, J. F. Gálvez, J. C. Mejuto, O. A. Moldes, and I. Montoya (2013). J. Comput. Chem. 34, 355.

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    G. Astray, B. Soto, D. Lopez, M. A. Iglesias, and J. C. Mejuto (2016). Water Sci. Technol. 73, 1756.

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    G. Astray, M. Fernández-González, F. J. Rodríguez-Rajo, D. López, and J. C. Mejuto (2016). Sci. Total Environ. 548–549, 110.

    Article  CAS  PubMed  Google Scholar 

  43. 43.

    M. Hernández Suárez, G. Astray Dopazo, D. Larios López, and F. Espinosa (2015). PLoS ONE 10, e0128566.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    V. Venkatasubramanian, R. Rengaswamy, S. N. Kavuri, and K. Yin (2003). Comput. Chem. Eng. 27, 327.

    CAS  Article  Google Scholar 

  45. 45.

    K. Metaxiotis, A. Kagiannas, D. Askounis, and J. Psarras (2003). Energy Convers. Manag. 44, 1525.

    Article  Google Scholar 

  46. 46.

    J. V. Tu (1996). J. Clin. Epidemiol. 49, 1225.

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    A. Witek-Krowiak, K. Chojnacka, D. Podstawczyk, A. Dawiec, and K. Pokomeda (2014). Bioresour. Technol. 160, 150.

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    A. Sharma, S. Kumari, P. Wongputtisin, M. J. R. Nout, and P. K. Sarkar (2015). J. Appl. Microbiol. 119, 162.

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    M. Rakshit, A. Sharma, J. Saha, and P. K. Sarkar (2015). LWT Food Sci. Technol. 63, 814.

    CAS  Article  Google Scholar 

  50. 50.

    Z. M. Lu, J. Y. Lei, H. Y. Xu, J. S. Shi, and Z. H. Xu (2011). Appl. Microbiol. Biotechnol. 92, 371.

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    T. Guo, J. Q. Wei, Y. Wang, D. Su, Z. Zhang, and Y. L. Yao (2015). Adv. J. Food Sci. Technol. 7, 67.

    CAS  Article  Google Scholar 

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Acknowledgement

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). https://doi.org/10.1007/s10876-018-1429-8

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Keywords

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