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Rice Bran or Apple Pomace? Comparative Data Analysis of Astaxanthin Bioproduction

Year 2018, Volume: 24 Issue: 3, 366 - 373, 05.09.2018
https://doi.org/10.15832/ankutbd.456661

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. 

References

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  • Arvanitoyannis I S (2010). Waste Management for the Food Industries. Charon Tec Ltd, First Edition, Chennai, India
  • Aslan N & Cebeci Y (2007). Application of Box-Behnken design and response surface methodology for modeling of some Turkish coals. Fuel 86(1): 90-97
  • Babitha S, Soccol C R & Pandey A (2007). Solidstate fermentation for the production of Monascus pigments from jackfruit seed. Bioresource Technology 98(8): 1554-1560
  • Baino A (2014). Recovery of biomolecules from food wastes--a review. Molecules 19(9): 14821-14842
  • Baş D & Boyacı I H (2007). Modeling and optimization I: Usability of response surface methodology. Journal of Food Engineering 78(3): 836-845
  • Couto S R (2008). Exploitation of biological wastes for the production of value-added products under solidstate fermentation conditions. Biotechnology Journal 3(7): 859-870
  • Couto S R & Sanromán M A (2006). Application of solid state fermentation to food industry- A review. Journal of Food Engineering 76: 291-302
  • Desai K M, Survase S A, Saudagar P S, Lele S S & Singhal R S (2008). Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan. Biochemical Engineering Journal 41(3): 266-273
  • Deshmukh S C, Senthilnath J, Dixit R M, Malik S N, Pandey R A, Vaidya A N, Omkar S N & Mudliar S N (2012). Comparison of radial basis function neural network and response surface methodology for predicting performance of biofilter treating toluene. Journal of Software Engineering and Applications 5(8): 595-603
  • Dutta J R, Dutta P K & Banerjee R (2004). Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomona ssp. using response surface and artificial neural network models. Process Biochemistry 39(12): 2193-2198
  • Fang H & Horstemeyer M F (2007). Global response approximation with radial basis functions. Engineering Optimization 38(4): 407-424
  • Gupta C, Garg A P, Prakash D, Goyal S & Gupta S (2011). Microbes as potential source of biocolors. Pharmacology 2: 1309-1318
  • Joshi V K & Attri D (2006). Solid state fermentation of apple pomace for the production of value added products. Natural Product Radiance 5(4): 289-296
  • Laufenberg G, Kunz B & Rosato P (2004). Adding value to vegetable waste: oil press cakes as substrates for microbial decalactone production. European Journal of Lipid Science and Technology 106(4): 207-217
  • Liu L, Jun Sun J, Zhang D, Du G, Chen J & Xu W (2009). Culture conditions optimization of hyaluronic acid production by Streptococcus zooepidemicus based on radial basis function neural network and quantumbehaved particle swarm optimization algorithm. Enzyme and Microbial Technology 44: 24-32
  • López S, Davies D R, Giráldez F J, Dhanoa M S, Dijkstra J & France J (2005). Assessment of nutritive value of cereal and legume straws based on chemical composition and in vitro digestibility. Journal of the Science of Food and Agricultural 85(9): 1550-1557
  • Pandey A, Soccol C R & Mitchell D (2000). New developments in solid state fermentation: I-bioprocesses and products. Process Biochemistry 35(10): 1153-1169
  • Panesar R, Kaur S & Panesar P S (2015). Production of microbial pigments utilizing agro-industrial waste: a review. Current Opinion in Food Science 1: 70-76
  • Warnes M R, Glassey J, Montague G A & Kara B (1998). Application of radial basis function and feed forward artificial neural networks to the Escherichia coli fermentation process. Neurocomputing 20: 67-82
  • Willmott C J (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society 63(11): 1309-1313
Year 2018, Volume: 24 Issue: 3, 366 - 373, 05.09.2018
https://doi.org/10.15832/ankutbd.456661

Abstract

References

  • Ambati R R, Phang S M, Ravi S & Aswathanarayana R G (2014). Astaxanthin: sources, extraction, stability, biological and its commercial applications-A review. Marine Drugs 12(1): 128-152
  • Arvanitoyannis I S (2010). Waste Management for the Food Industries. Charon Tec Ltd, First Edition, Chennai, India
  • Aslan N & Cebeci Y (2007). Application of Box-Behnken design and response surface methodology for modeling of some Turkish coals. Fuel 86(1): 90-97
  • Babitha S, Soccol C R & Pandey A (2007). Solidstate fermentation for the production of Monascus pigments from jackfruit seed. Bioresource Technology 98(8): 1554-1560
  • Baino A (2014). Recovery of biomolecules from food wastes--a review. Molecules 19(9): 14821-14842
  • Baş D & Boyacı I H (2007). Modeling and optimization I: Usability of response surface methodology. Journal of Food Engineering 78(3): 836-845
  • Couto S R (2008). Exploitation of biological wastes for the production of value-added products under solidstate fermentation conditions. Biotechnology Journal 3(7): 859-870
  • Couto S R & Sanromán M A (2006). Application of solid state fermentation to food industry- A review. Journal of Food Engineering 76: 291-302
  • Desai K M, Survase S A, Saudagar P S, Lele S S & Singhal R S (2008). Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan. Biochemical Engineering Journal 41(3): 266-273
  • Deshmukh S C, Senthilnath J, Dixit R M, Malik S N, Pandey R A, Vaidya A N, Omkar S N & Mudliar S N (2012). Comparison of radial basis function neural network and response surface methodology for predicting performance of biofilter treating toluene. Journal of Software Engineering and Applications 5(8): 595-603
  • Dutta J R, Dutta P K & Banerjee R (2004). Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomona ssp. using response surface and artificial neural network models. Process Biochemistry 39(12): 2193-2198
  • Fang H & Horstemeyer M F (2007). Global response approximation with radial basis functions. Engineering Optimization 38(4): 407-424
  • Gupta C, Garg A P, Prakash D, Goyal S & Gupta S (2011). Microbes as potential source of biocolors. Pharmacology 2: 1309-1318
  • Joshi V K & Attri D (2006). Solid state fermentation of apple pomace for the production of value added products. Natural Product Radiance 5(4): 289-296
  • Laufenberg G, Kunz B & Rosato P (2004). Adding value to vegetable waste: oil press cakes as substrates for microbial decalactone production. European Journal of Lipid Science and Technology 106(4): 207-217
  • Liu L, Jun Sun J, Zhang D, Du G, Chen J & Xu W (2009). Culture conditions optimization of hyaluronic acid production by Streptococcus zooepidemicus based on radial basis function neural network and quantumbehaved particle swarm optimization algorithm. Enzyme and Microbial Technology 44: 24-32
  • López S, Davies D R, Giráldez F J, Dhanoa M S, Dijkstra J & France J (2005). Assessment of nutritive value of cereal and legume straws based on chemical composition and in vitro digestibility. Journal of the Science of Food and Agricultural 85(9): 1550-1557
  • Pandey A, Soccol C R & Mitchell D (2000). New developments in solid state fermentation: I-bioprocesses and products. Process Biochemistry 35(10): 1153-1169
  • Panesar R, Kaur S & Panesar P S (2015). Production of microbial pigments utilizing agro-industrial waste: a review. Current Opinion in Food Science 1: 70-76
  • Warnes M R, Glassey J, Montague G A & Kara B (1998). Application of radial basis function and feed forward artificial neural networks to the Escherichia coli fermentation process. Neurocomputing 20: 67-82
  • Willmott C J (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society 63(11): 1309-1313
There are 21 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Derya Dursun This is me

Ali Çoşkun Dalgıç This is me

Publication Date September 5, 2018
Submission Date April 11, 2017
Acceptance Date December 31, 1899
Published in Issue Year 2018 Volume: 24 Issue: 3

Cite

APA Dursun, D., & Dalgıç, A. Ç. (2018). Rice Bran or Apple Pomace? Comparative Data Analysis of Astaxanthin Bioproduction. Journal of Agricultural Sciences, 24(3), 366-373. https://doi.org/10.15832/ankutbd.456661

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