Abstract
Modeling and optimization of high value-added astaxanthin pigment bioproduction statistically by Sporidiobolus salmonicolor ATCC 24259 from two substantial wastes, rice bran (RB) and apple pomace (AP) was aimed in this study. The experimental data was obtained at constant inoculum rate (2%) and particle size (0.85 mm) for both wastes by conducting 17 runs, which were generated by Box-Behnken design. 33.41 µg astaxanthin gRB- and 77.31 µg astaxanthin gAP- were produced as the maximum amount at the end of fermentation period, 10 days. Apple pomace was concluded as the optimized waste for the production of astaxanthin based upon the highest yield. Predicted response results of response surface methodology (RSM) and radial basis function-neural network (RBF-NN) were compared in order to evaluate the accuracy of two methodologies on non-linear behavior of the astaxanthin bioproduction. RBF-NN became prominent with its well-suited to apple pomace fermentation system by resulting in quite low 0.8495, root mean square error (RMSE), 0.3349, mean absolute error (MAE), and 0.9985, correlation coefficient (CC) as best measures of a model performance.
Highlights
The wastes of the cereals and fruits are obtained after post-harvesting and processing of them industrially, which are mayor columns of solid agro-industrial wastes
Bio-production of astaxanthin from rice bran (RB) and apple pomace (AP) was accomplished by Sporidiobolus salmonicolor applying solid state fermentation (SSF) technology
Rn2dition0.s7636and 0.8r4e9s3ourc0e.9145in th0.e9969scope of an experimental design were achieved for a valuable bio-product, astaxanthin
Summary
The wastes of the cereals and fruits are obtained after post-harvesting and processing of them industrially, which are mayor columns of solid agro-industrial wastes. Bioprocesses are alternative and good way of utilization of the wastes, not just owing to their rich organic content, for good anchorage feature, easy accessibility, abundantly available, low cost what kind of properties solid state fermentation (SSF) system requires (Pandey et al 2000; Couto & Sanromán 2006; Couto 2008). SSF of the wastes is a highly attractive process to study due to providing environmental solutions of solid wastes, technique performance, wide usage and execution areas, feasibility, low cost and labor, product manifoldness and so on. Revealing certain fermentation conditions and productivity of the system is important by applying an experimental design, modeling and optimization statistically. In this manner, response surface methodology and artificial neural network (ANN) methodologies show
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