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Ergosterol,(1→3)-α-D-glucan and chitosan are important biomaterials. In this research, a process has been developed to integratively extract ergosterol, (1→3)-α-D-glucan, and chitosan from Penicillium chrysongenum mycelium. First, the mycelia are pretreated with 0.1mol·L-1 of NaOH. After recovery by centrifugation, the solid portion is made to undergo saponification and deacetylation reactions by addition of 2mol·L-1 NaOH and ethanol. After reaction, extraction is carried out by addition of petroleum ether, which separates the reaction mixture into two phases. The upper layer of petroleum ether contains extracted ergosterol, and the bottom layer of NaOH solution contains (1→3)-α-D-glucan; the chitosan is on the mycelia residuum. After isolation, the recovery yield of ergosterol is 0.52% of dry mycelium. That of (1→3)-α-D-glucan is about 8.2%; and chitosan is 5.7% with 86?acetylation. The compositions have been characterized by IR, HPLC analyses. 相似文献
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采用“两步法”合成了二醋酸纤维素与β-环糊精接枝共聚物(CDA-g-β-CD).首先以二醋酸纤维素(CDA)与1,6-己二异氰酸酯(HDI)反应,得到端异氰酸酯预聚物(NCOCDA),然后将该预聚物与β-环糊精(β-CD)反应得到接枝共聚物CDA-g-β-CD.傅里叶红外(FT-IR)和核磁氢谱(1H-NMR)分析结果... 相似文献
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发酵过程优化问题通常包含有互相冲突的多重优化目标,另外反应本身具有诸多复杂性。提出一种基于Pareto的分布式Q学习多目标策略,用以求解赖氨酸分批补料发酵过程流加速率轨迹的Pareto最优解。该策略中,Q学习算法和Pareto排序法将结合来产生非支配解集,并使之逼近真实的Pareto前沿,利用奖赏机制来描述多重目标之间的关系,并同时使用多组含有随机初始值的agent共同作用改善搜索能力。将所提出的方法应用于赖氨酸分批补料发酵过程的优化中,并与粒子群优化进行了对比,验证策略的性能。 相似文献