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Journals / Tarım Bilimleri Dergisi / 2018 / Cilt: 24 - Sayı: 3

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

Derya DURSUN
Ali Çoşkun DALGIÇ
Pages
366–373
Publication Date
2018-09-05
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

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Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Apple pomace · Rice bran · Astaxanthin

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.

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Cite This Article

DURSUN, D., DALGIÇ, A. Ç. (2018). Rice Bran or Apple Pomace? Comparative Data Analysis of Astaxanthin Bioproduction. Tarım Bilimleri Dergisi, Cilt: 24 - Sayı: 3, 366–373. https://ajindex.com/articles/178618

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References (11)

  1. 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
  2. Arvanitoyannis I S (2010). Waste Management for the Food Industries. Charon Tec Ltd, First Edition,
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. Laufenberg G, Kunz B & Rosato P (2004). Adding value to vegetable