Combined ANN/EVOP Factorial Design Approach for Media Screening for Cost-effective Production of Alkaline Proteases from Rhizopus oryzae (SN5)/NCIM-1447 under SSF

Abstract

In order to achieve high yield of fungal protease in a very cost effective way and to meet its increased market demand, current study deals with the screening of various agro-wastes as carbon source for the production of protease from Rhizopus oryzae (SN5)/NCIM-1447 under solid state fermentation. Substrates and culture parameters such as wheat bran, soybean meal, black-gram husk, rice husk, mixture of wheat bran, soybean meal, nitrogen sources, pH, temperature and incubation time were first optimized with one factor at time strategy and then EVOP factorial and yield of alkaline protease was achieved 412.8 U/gds at 28 °C and pH = 6 after 72 h of fermentation taking wheat bran and soybean as a substrate in 4:1 ratio. Further artificial neural networks (ANN), was trained with data of EVOP and yield of protease was enhanced up to 422.6 U/gds with wheat bran: soyabean in ratio of 70:30, pH 6.2 at 30 °C. The evolved process and Rhizopus oryzae (SN5)/NCIM-1447 strain would be promising for protease production at industrial scale at low cost.

Enhanced production of alkane hydroxylase from Penicillium chrysogenum SNP5 (MTCC13144) through feed-forward neural network and genetic algorithm

“…2 ). These results are close to the published results (Das et al 2015 ; Negi et al 2020 ; Suryawanshi et al 2020 ). A high degree of accuracy of optimization is attributed to the selection of appropriate training algorithms (i.e., trainlm), several hidden layers and the size of ‘one-variable-at-a-time’ data for the training of the network.…”

“…In bioprocesses optimization (Negi et al 2020 ), pattern recognition in spectrum data, functional analysis of genomes and proteomes (Wardah et al 2019 ) and their nonlinear functions are designed through FFNN (May et al 2011 ). Many studies reported that FFNN has better efficiency, accuracy and yield as compared to the other statistical optimization methods such as RSM (Prakash Maran and Priya 2015 ).…”

Early Detection of Endobacteria in Rhizopus spp.

“…such as Rhizopus oligosporus, R. oryzae, R. arhizus, R. stolonifer and R. microsporus (Zygomycota, Mucorales, Mucoraceae) (Schipper and Stalpers, 1984) were abundant in various substrates in nature, often used as inoculants in the soybean tempe industry (Nout and Rombouts, 1990;Astuti et al, 2000;Tamam et al, 2019). In addition, those species also play an essential role in other bio-industries, such as steroids (Donova, 2021), amylase (Ait Kaki El-Hadef El-Okki et al (2017), protease (Negi et al, 2020), lipase (Satomura et al, 2015) and various organic acids production (Ghosh and Ray, 2011).…”