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

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Google Scholar 

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

    Article  CAS  Google Scholar 

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

    CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

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

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

Download references

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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Authors and Affiliations

  1. Department of Agricultural and Food Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India

    Nirlipta Saha & S. Dutta Gupta

  2. Department of Physical Chemistry, Faculty of Science, University of Vigo, 32004, Ourense, Spain

    Gonzalo Astray

Authors
  1. Nirlipta Saha
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  2. Gonzalo Astray
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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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