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首页> 外文期刊>Biotechnology Progress >Optimization of pH and Nitrogen for Enhanced Hydrogen Production by Synechocystis sp. PCC 6803 via Statistical and Machine Learning Methods
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Optimization of pH and Nitrogen for Enhanced Hydrogen Production by Synechocystis sp. PCC 6803 via Statistical and Machine Learning Methods

机译：优化pH和氮，以提高集胞藻的产氢量。 PCC 6803通过统计和机器学习方法

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摘要

The nitrogen (N) concentration and pH of culture media were optimized for increased fermentative hydrogen (H2) production from the cyanobacterium,Synechocystis sp. PCC 6803. The optimization was conducted using two procedures,response surface methodology (RSM),which is commonly used,and a memory-based machine learning algorithm,Q2,which has not been used previously in biotechnology applications. Both RSM and Q2 were successful in predicting optimum conditions that yielded higher H2 than the media reported by Burrows et al.,Int J Hydrogen Energy. 2008;33:6092-6099 optimized for N,S,and C (called EHB-1 media hereafter),which itself yielded almost 150 times more H2 than Synechocystis sp. PCC 6803 grown on sulfer-free BG-11 media. RSM predicted an optimum N concentration of 0.63 mM and pH of 7.77,which yielded 1.70 times more H2 than EHB-1 media when normalized to chlorophyll concentration (0.68 ± 0.43 μmol H2 mg Chl~(-1) h~(-1)) and 1.35 times more when normalized to optical density (1.62 ± 0.09 nmol H2 OD_(730)~(-1) h~(-1)). Q2 predicted an optimum of 0.36 mM N and pH of 7.88,which yielded 1.94 and 1.27 times more H2 than EHB-1 media when normalized to chlorophyll concentration (0.77 ± 0.44 μmol H2 mg Chl~(-1) h~(-1)) and optical density (1.53 ± 0.07 nmol H2 OD_(730)~(-1)),respectively. Both optimization methods have unique benefits and drawbacks that are identified and discussed in this study.
机译：优化培养基的氮（N）浓度和pH以增加蓝细菌Synechocystis sp。的发酵氢（H2）产量。 PCC6803。使用两种方法进行了优化：常用的响应表面方法（RSM）和基于记忆的机器学习算法Q2，该算法以前未在生物技术应用中使用。 RSM和Q2均成功地预测了最佳条件，该条件产生的H2高于Burrows等人（Int J Hydrogen Energy）报道的介质。 2008; 33：6092-6099针对N，S和C（以下称为EHB-1培养基）进行了优化，其自身产生的H2比Synechocystis sp。高出150倍。 PCC 6803在无硫的BG-11培养基上生长。 RSM预测最佳氮浓度为0.63 mM，pH为7.77，当将其标准化为叶绿素浓度（0.68±0.43μmolH2 mg Chl〜（-1）h〜（-1）时，产生的H2比EHB-1培养基高1.70倍归一化为光密度（1.62±0.09 nmol H2 OD_（730）〜（-1）h〜（-1））时要多1.35倍。 Q2预测最适氮为0.36 mM，pH为7.88，当将其标准化为叶绿素浓度（0.77±0.44μmolH2 mg Chl〜（-1）h〜（-1）时，产生的H2比EHB-1培养基高1.94和1.27倍）和光密度（1.53±0.07 nmol H2 OD_（730）〜（-1））。两种优化方法都有独特的优点和缺点，这些优点和缺点已在本研究中确定和讨论。

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