Abstract
Melanins are indolic polymers produced by many genera included among plants, animals and microorganisms and targeted mainly for their wide range of applications in cosmetics, agriculture and medicine. An approach to analyse the cumulative effect of parameters for enhanced melanin production was carried out using response surface methodology. In this present study, optimization of media and process parameters for melanin production from Aspergillus fumigatus AFGRD105 (GenBank: JX041523; NFCCI accession number: 3826) was carried out by an initial univariate approach followed by statistical response surface methodology. The univariate approach was used to standardise the parameters that can be used for the 12-run Plackett–Burman design that is used for screening for critical parameters. Further optimization of parameters was analysed using Box–Behnken design. The optimum conditions observed were temperature, moisture and sodium dihydrogen phosphate concentration. The yield of every run of both designs were confirmed to be melanin by laboratory tests of analysis in the presence of acids, base and water. This is the first report confirming an increase in melanin production A. fumigatus AFGRD105 without the addition of costly additives.
Highlights
Melanin has been reported to be produced by various bacteria (Lagunas-Munoz et al 2006), fungi and many members of the plant kingdom
The optimized time for growth was set at 5 days for optimizing the carbon and nitrogen sources as the melanin production is the same after day 5 and the conidial samples tend to become a dry mass of spores (Fig. 1)
Of the carbon sources tested, it is found that Dextrose, which is the standard sugar used in Sabourauds Dextrose Agar medium, is the best carbon source for the growth of A. fumigatus AFGRD105 and for melanin production
Summary
Melanin has been reported to be produced by various bacteria (Lagunas-Munoz et al 2006), fungi and many members of the plant kingdom. The variables that are found significant in this initial screening can be further optimized using response surface methodology (RSM) which is a collection of statistical techniques that uses design of experiments (DoE) for building models, Raman et al AMB Expr (2015) 5:72 evaluating the effects of factors and predicting optimum conditions (Abdel-Nabi et al 1998). It is extensively applied in the optimization of medium composition, conditions of enzymatic hydrolysis, fermentation and food manufacturing processes. RSM which results from using Box–Behnken design was used for further study of the influences of major factors and interaction between them on the response value, which is based on the results of sole-experiment and Plackett–Burman (PB) design
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