Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC 20235

Abstract

Aspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-offit of the regression and ANN models was measured by the R 2 of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R 2 of 0.84 and biomass with an R 2 value of 0.83, whereas the regression models predicted enzyme activity with an R 2 of 0.84 and biomass with an R 2 of 0.69.

Comparison of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) Models in Simulating Polygalacturonase Production

“…The number of hidden neurons is one of the most important parameters of ANN modeling. A high number of neurons performs adequately for training data but may fail for testing data (over-fitting), while having too few hidden neurons may result in unsatisfactory convergence (under-fitting) (Tokatli et al 2009). In this study, the number of neurons in the hidden layer was chosen by trial and error method, by varying the neurons from 3 to 12.…”

“…These results indicated that the prediction accuracy of ANN was better than the RSM model for our enzyme production process. Tokatli et al (2009) performed different ANN topologies to predict polygalacturonase (PG) activity as well with a 5-2-1 network topology (R 2 =0.84).…”

“…ANN is also a popular artificial learning tool in biotechnology and has been used for the optimization of bioprocesses (Monahar and Divakar 2005) and polygalacturonase, protease, laccase, and hydantoinase enzyme production from microorganisms (Nagata and Chu 2003;Dutta et al 2004;Rekha et al 2013;Tokatli et al 2009;Chiranjeevi et al 2014). Tokatli et al (2009) compared RSM and ANN models for polygalacturonase activity and biomass production by Aspergillus sojae ATCC 20235, concluding that the ANN model predicted polygalacturonase activity better than the RSM model. In another study, Rekha et al (2013) successfully used ANN for modeling polygalacturonase production from a newly isolated Thalassopira frigidphilosprofundus.…”

Microparticle-enhanced polygalacturonase production by wild type Aspergillus sojae

“…In order to maintain viability, the fungus-grown plates were maintained at 4°C and subcultured monthly. Stock spore cultures were prepared using 20% glycerol and stored at -80°C (Tokatli et al 2009). Molasses agar slants were utilized to obtain the spore suspensions for inoculation.…”

“…The first study using wild type of A. sojae 20235 resulted in 13.5 U/mL PG activity in SmF fermentation without microparticle (Tari et al 2007). Then, the maximum PG activity was enhanced to 20.1 U/mL using the same strain and medium (Tokatli et al 2009). Accordingly, the main objective of this study was not only to improve the fungal morphology of A. sojae by the addition of Al 2 O 3 into the fermentation media but also to increase the PG activity in microparticle-enhanced shake flask fermentation.…”