Process in‐line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in‐line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state‐of‐the‐art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non‐invasive in‐line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM‐based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in‐line information as input of a simple on–off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

中文翻译：

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使用拉曼光谱和间接硬建模 (IHM) 进行生物过程在线监测和控制

过程在线监测和控制对于优化生物过程的生产力至关重要。用于生物过程在线监测的一种经常应用的过程分析技术 (PAT) 工具是拉曼光谱。然而，评估生物过程拉曼光谱很复杂，校准最先进的统计评估模型也很费力。为了克服这一挑战，我们在之前的研究中开发了间接硬建模（IHM）预测模型。拉曼光谱和 IHM 预测模型的结合能够对酵母发酵过程中的葡萄糖和乙醇质量分数进行非侵入性在线监测，与基于统计模型的同类方法相比，校准工作量显着减少。在本研究中，我们推进了这种基于 IHM 的方法，并成功证明拉曼光谱和 IHM 的结合不仅能够进行生物过程监测，还能够进行生物过程控制。为此，我们使用该组合的在线信息作为简单开关葡萄糖控制器的输入来控制葡萄糖质量分数酿酒酵母发酵。当我们使用不同的预定义葡萄糖设定点进行两次发酵时，我们实现了与使用统计模型的方法类似的过程控制质量，尽管校准工作要小得多。因此，本研究再次证实拉曼光谱与 IHM 的结合是生物过程的强大 PAT 工具。