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

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. 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. 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.