Application of adaptive neural fuzzy inference system for predicting bio-deterioration of historical papers due to alternaria alternate

Document Type : Research Paper

Authors

1 PhD Student, Preservation of Cultural and Historical Artefacts, Isfahan University of Fine Arts, Isfahan, I. R. Iran.

2 Assistant Professor, Historical and Cultural Objects Restoration, Arts University of Isfahan, Isfahan, I.R.Iran

3 Associate Professor, Mineral Chemistry, Iranian Institute for Chemistry & Chemical Engineering, Tehran, I. R. Iran

4 Associate Professor, The School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, I. R. Iran.

Abstract

Purpose: To explore predictability of growth rate of alternaria alternata in paper by ANFIS to control the bio-deterioration. Mathematical modeling by adaptive neural fuzzy inference system (ANFIS) has proved to be a valuable tool in predicting fungal growth rate in the fields of microbiology and food industry. This method is an alternative way to the classic means of incubation in biotechnology. However, the modeling of filamentous fungi has not received the same level of attention in controlling filamentous fungi in the paper art works.
Method and Research Design: The combined effect of temperature (5-10°C), aw (0.8-0.99), pH (3-9) and time (24-268h) on A. alternata growth rate in paper art works was modeled by ANFIS. MTLAB, 2018a Software was employed for the purpose of analysis.
Findings and Conclusion: From comparisons between experimental data results, growth rates predicted by ANFIS were confirmed, because of the high accuracy of the Gaussian membership function. Also, the ANFIS model is a useful tool for quickly predicting the growth rate of A. alternata in paper art works.

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References

English Translationof References
Al-Mahasneh, Majdi; Aljarrah, Mohannad; Rababah, Taha; Alu’datt, Muhammad. (2016). “Application of Hybrid Neural Fuzzy System (ANFIS) in Food Processing and Technology”. Food Engineering Reviews,8(3), pp 351-366. DOI: 10.1007/s12393-016-9141-7.
Borrego, Sofía; Guiamet, Patricia; Gómez de Saravia, Sandra; Batistini, Patricia; Garcia, miniet; Lavin, Paola; Perdomo, Ivette. (2010). “The quality of air at archives and the biodeterioration of photographs”. International Biodeterioration& Biodegradation, 64(2), pp 139-145. Retrieved from www.elsevier.com/locate/ibiod. 2012/05/12.
Cappitelli, F.; Sorlini, C. (2005). “From papyrus to compact disc: the microbial deterioration of documentary heritage”. Critical Reviews in Microbiology, 31(1), pp 1-10.
Florian, Mary-Lou E. (2004). Fungal facts: Solving fungal problems in heritage collections. Archetype Publications, Great Britain, pp 90-97.
Garcia, Daiana; Ramos, Antonio J; Sanchis, Vicente; Marín, Sonia. (2009). “Predicting mycotoxins in foods: A review”. Food Microbiology, 26(8), pp 757-769.
Ghahri, Mohammad. (1391/2012). “Avāmel-e mikrobi-ye āsib-resān be mavād-e āršivi va ketābxāne-ye melli-e” (Microbial factors deteriorating archival and library material). Tehran: Sāzmān-e AsnādvaKetābxāne-ye Melli-ye Jomhuri-ye Eslāmi-ye Iran (Sākmā) (National Library and Archives of Iran).[Persian]
Gutarowska, Beata; Rembisz, Daria; Pietrzak, Katarzyna; Skóra, Justyna; Szynkowska, Malgorzata; Gliścińska, Eulalia; Koziróg, Anna. (2012). “Optimization and application of the misting method with silver nanoparticles for disinfection of the historical objects”. International Biodeterioration& Biodegradation, 75, pp 167-175.
Hajmeer, Maha N; Basheer, Imad A; Najjar, Yacoub M. (1997). “Computational neural networks for predictive microbiology II. Application to microbial growth”. International Journal of Food Microbiology, 34(1), pp 51-66.
Haykin, Simon. (1994). Neural networks: Acomprehensive foundation.Macmillan, New York.
Ho, Wen-Hsien; Tsai, Jinn-Tsong; Wang, Hue-Yu. (2012). “Neural Fuzzy Network model with evolutionary learning algorithm for mycological study of foodborne fungi”. International journal of innovative computing, information & control (IJICIC), 8(7).
Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), pp 665-685.
Lou, Wen; Nakai, Shuyro. (2001a). “Application of artificial neural networks for predicting the thermal inactivation of bacteria: a combined effect of temperature, pH and water activity”. Food Research International, 34(7), pp 573-579.
Lou, Wen; Nakai, Shuyro. (2001b). “Artificial neural network-based predictive model for bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products”. Journal of Agricultural and Food Chemistry, 49(4), pp 1799-1804.
Michaelsen, Astrid; Pi˜nar, Guadalupe; Pinzari, Flavia. (2010). “Molecular and microscopical investigation of the microflora inhabiting a deteriorated Italian manuscript dated from the Thirteenth Century”. Microbial Ecology, vol. 60, no. 1, pp 69-80.
Michaelsen, Astrid; Pinzari, Flavia; Barbabietola, Nicoletta; Piñar, Guadalupe. (2012). “Monitoring the effects of different conservation treatments on paper-infecting fungi”. International Biodeterioration& Biodegradation, xxx, pp 1-9.
Mohsenian, SeyedehSomayeh; Azadi, Mehrnaz; Afsharpour, Maryam; Mozaffar, Farhang. (1397/2018). “Arz-yā bi-ye ta’sir-e motqayer-hā –ye damā ,fa’āliat-e ābi, qelzat-e yon-e hidrožen, va zaman bar rošd-e qārč-hā-ye āsperjelusnāyjer, penisiliyom va ā lternvriāālternātā dar kāqaz-hā-ye tārixi” (The effect of temperature, water activity, ph. and time on the growth of Aspergillus Niger, AlternariaAlternata and Penicillium sp. in the historical papers). Ganjine-ye asnād, autumn 2018, 28(3), pp. 166-202.[Persian]
Palla, Franco; Barresi, Giovanna. (2017). Biotechnology and conservation of cultural heritage. Switzerland: Springer, Cham. Online ISBN: 978-3-319-46168-7. Retrieved from https://doi.org/10.1007/978-3-319-46168-7.
Panagou, E. Z.; Kodogiannis, V. S. (2009). “Application of neural networks as a non‑linear modelling technique in food mycology”. Expert Systems with Applications, 36(1), pp 121-131.
Ponce-Jimenez, Maria; Toral, Fernando; Fornue, Ezequiel. (2002). “Antifungal protection and sizing of paper with Chitosan salts and cellulose ethers. Part 1, physical effects”. Journal of the American Institute for Conservation,41(3), pp 243-254.
Schubert, Mark; Mourad, Safer; Schwarze, Francis. (2010). “Radial basis function neural networks for modeling growth rates of the basidiomycetes Physisporinusvitreus and Neolentinuslepideus”. Applied Microbiology and Biotechnology, 85(3), pp 703-712. DOI: 10.1007/s00253-009-2185-3.
Strang, Thomas. (2012). “Studies in pest control for cultural property”. ActaUniversitatisGothoburgensis. GOTHENBURG STUDIES IN CONSERVATION 30. PHD thesis. Sweden, 2012.
Vereecken, Evy; Roels, Staf. (2012). “Review of mould prediction models and their influence on mould risk evaluation”. Building and Environment, 51, pp 296-310.
Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien. (2013). “LeuconostocMesenteroides Growth in Food Products: Prediction and Sensitivity Analysis by Adaptive-Network-Based Fuzzy Inference Systems”. PLoS ONE, 8(5): e64995. DOI: 10.1371/journal.pone.0064995. retrieved from https://doi.org/10.1371/journal.pone.0064995.
Zhang, Guoqiang; Patuwo, B. Eddy; Hu, Michael Y. (1998). “Forecasting with artificial neural network: The state of art”. International Journal of Forecasting, 14(1), pp 35-62.
Ganjine-ye Asnad
Volume 28, Issue 4
March 2019
Pages 196-216

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History

  • Receive Date: 06 October 2018
  • Revise Date: 01 November 2018
  • Accept Date: 27 January 2019

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