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Journals / International Journal of Agriculture Environment and Food Sciences / 2019 / Cilt: 3 - Sayı: 3

Astaxanthin biosynthesis: A two-step optimization approach and model construction with Response Surface Methodology and Artificial Neural Network

Derya DURSUN SAYDAM
Ali Coşkun DALGIÇ
Pages
171–181
Publication Date
2019-09-27
DOI
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AI Summary & Key Takeaways

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

It was found that R2=0.9869 was the highest value with the maximum predicted ASX amount of 109.23 µg of ASX/g wheat bran with X. dendrorhous using Artificial Neural Network modeling and the moisture content was the most significant production parameter.

📌 3 Key Takeaways

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Objective & Hypothesis:The first part of this research is investigating and comparing yield of a synthetic medium submerged three sugars (glucose, fructose and sucrose) at four different concentrations and solid fermentation systems with wheat bran and lentil waste for biosynthesis of astaxanthin (ASX) pigment by Xanthophyllomyces dendrorhous ATCC 24202 and Sporidiobolus salmonicolor ATCC 24259 microorganisms.
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Key Findings & Data:The yields produced by X. dendrorhous were 51.88 µg of ASX/g glucose for the submerged medium with the least glucose, and 210.49 µg of ASX/g glucose for the wheat bran fermentation system.
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Conclusion & Impact:It was found that R2=0.9869 was the highest value with the maximum predicted ASX amount of 109.23 µg of ASX/g wheat bran with X. dendrorhous using Artificial Neural Network modeling and the moisture content was the most significant production parameter.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Astaxanthin biosynthesis · Neural network · Optimization

Abstract

The first part of this research is investigating and comparing yield of a synthetic medium submerged three sugars (glucose, fructose and sucrose) at four different concentrations and solid fermentation systems with wheat bran and lentil waste for biosynthesis of astaxanthin (ASX) pigment by Xanthophyllomyces dendrorhous ATCC 24202 and Sporidiobolus salmonicolor ATCC 24259 microorganisms. The second part is modeling and optimizing the most efficient biosynthesis depending on waste, yeast and production variables consisted of moisture content, pH and temperature using a design matrix. The yields produced by X. dendrorhous were 51.88 µg of ASX/g glucose for the submerged medium with the least glucose, and 210.49 µg of ASX/g glucose for the wheat bran fermentation system. It was understood that the yield values of the submerged systems were lower and there was no requirement for the addition of any supplement to the waste systems. It was found that R2=0.9869 was the highest value with the maximum predicted ASX amount of 109.23 µg of ASX/g wheat bran with X. dendrorhous using Artificial Neural Network modeling and the moisture content was the most significant production parameter.

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Cite This Article

SAYDAM, D. D., DALGIÇ, A. C. (2019). Astaxanthin biosynthesis: A two-step optimization approach and model construction with Response Surface Methodology and Artificial Neural Network. International Journal of Agriculture Environment and Food Sciences, Cilt: 3 - Sayı: 3, 171–181. https://ajindex.com/articles/765347

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