基于SERS在线检测技术的CPC发酵生产过程监测及反馈控制优化研究

Optimization of operational conditions and parameters is the most straightforward pathway to improve the efficiency of fermentation process. Compared to the optimization process based on the modeling and prediction of macroscopic environment conditions and kinetic parameters of fermentation, real-time online detection of intermediates, by-products and product along with concurrently monitoring the fermentation at molecular level in chemical reaction usually provides the superior accuracy. Therefore, in this proposal, a molecular spectroscopy technology – surface enhanced Raman spectroscopy (SERS) is introduced to detect the components in fermentation liquor. An online detection platform will be developed for continuous and real-time SERS data collection from the fermentation liquor of cephalosporin C (CPC) produced by Cephalosporins acremonium. Moreover, based on the obtained SERS data, a prediction model will be built for predicting the concentrations of intermediates, by-products and product in fermentation liquor, and thus realizing continuous online detection of the components concentrations during fermentation process. The optimized fermentation conditions and parameters at different physiological stages of Cephalosporins acremonium will be generalized and rationalized based on the real-time detection data of the concentrations of target components. It will serve as accurate references for regulating operational conditions and parameters, and the fermentation productivity of CPC can be significantly improved at low costs. With the real-time and online detection of intermediates, by-products and product in fermentation liquor, mass equilibrium algebraic equation and metabolic network model of CPC production by Cephalosporins acremonium will be formulated, and the inherent mechanism and characteristics will be studied. The whole research work would provide a new perspective and technology for the on-line detection and feedback-control in biochemical engineering, which could promote and facilitate the studies on biochemical engineering and related subjects.

发酵过程操作条件及参数优化是提高发酵生产效率最直接的途径。相比基于宏观环境条件和动力学参数建模预测及寻优的优化方法，基于中间体、副产物及产物浓度实时在线检测、从化学分子反应水平对发酵过程进行监控，从而实现操作条件及参数优化的方法具有更高的准确性。基于此，本项目将分子光谱测试技术-表面拉曼增强(SERS)引入至发酵液的检测中，构建顶头孢霉菌发酵生产头孢菌素C(CPC)发酵液实时、连续、在线SERS数据采集平台，建立基于SERS数据预测发酵中间体、副产物及产物浓度的化学计量学模型，实现发酵液成分浓度的连续在线检测；归纳最优发酵操作条件及宏观环境参数，提供反馈补料及参数优化控制，以提高发酵生产效率；并依此构建发酵生产CPC的物料平衡代数方程及代谢网络模型，把握发酵生产CPC过程的内在本质和特征。本项目的完成将为生物化工过程在线检测与反馈控制提供新思路与新技术，促进生化工程及相关学科的发展。

发酵过程操作条件控制及参数优化是提高发酵生产效率最直接的途径。相比基于宏观环境条件和动力学参数建模预测及寻优的过程控制优化方法，基于中间体、副产物及产物浓度实时在线检测，从生物化学分子反应水平对发酵过程进行监控，从而实现操作条件及参数优化的方法具有更高的准确性。因此，本项目提出了基于表面增强拉曼光谱(SERS)在线检测技术的发酵生产过程监测及反馈控制优化策略。以自过滤复合SERS基底构建连续在线SERS检测系统，将SERS检测结果与化学计量学建模预测耦合，实现发酵液的实时在线检测方法；基于此，项目首先设计制备了“石榴状”等离子体纳米反应器、等离子体胶体金超颗粒、接枝三种“捕获器”的等离激元超表面等新型功能化SERS基底，并以此构建了复杂体系中多组分痕量物质的检测平台；同时结合高效液相色谱和高速逆流色谱等分离分析技术，建立了筛选和分离复杂发酵液样本中微量成分的实时在线联用技术；利用SERS检测平台建立光谱数据集，建立了基于机器学习算法的发酵液成分识别与定量预测模型，反馈最优操作条件及发酵参数。上述研究结果为多功能SERS基底的制备、实时在线联用检测技术的建立及其应用于发酵生产过程监测与反馈控制优化提供了有益的参考，促进了化工分离分析及SERS实时在线联用新技术的探索与发展。依托于项目主体研究内容，项目还发展了复杂天然产物组分分离纯化技术和复杂生物体系中生物小分子及环境样本中有害物质的荧光、电化学、可视化快速检测方面的研究工作，促进了分析化学、制药工程等相关学科的发展。项目实施期间总计在Anal. Chem., Chem. Commun. 和Chem. Eng. J.等期刊上发表SCI论文33篇(均已标注资助)，获得2020年湖南省自然科学二等奖，培养博士生5名、硕士生9名。