To increase the process productivity and product quality of bioprocesses, the in-line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in-situ and non-invasively. However, evaluating bioprocess Raman spectra with a robust state-of-the-art statistical model requires effortful model calibration. In the present study, we in-line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics-based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra.

中文翻译：

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使用拉曼光谱和间接硬建模 (IHM) 的生物过程在线监测：简单的校准产生稳健的模型

为了提高生物过程的生产率和产品质量，关键过程参数的在线监测非常重要。为了监测底物、代谢物和产物浓度，拉曼光谱是一种常用的过程分析技术 (PAT) 工具，可以原位和非侵入性地应用。然而，使用强大的最先进统计模型评估生物过程拉曼光谱需要费力的模型校准。在本研究中，我们在线监测了酿酒酵母( S. cerevisiae ) 将葡萄糖发酵为乙醇的过程) 将拉曼光谱与基于物理的间接硬建模 (IHM) 结合使用，并成功表明 IHM 是统计模型的替代方法，校准工作量显着降低。IHM 预测模型的开发和校准总共只有 16 个拉曼光谱，其中不包括任何过程光谱。尽管如此，IHM 对葡萄糖 (3.68 g/L) 和乙醇 (1.69 g/L) 的预测均方根误差 (RMSEP) 与使用经过多个校准批次校准的统计模型的类似研究的预测质量相当。尽管我们进行了简单的校准，但我们成功地开发了一个用于评估生物过程拉曼光谱的稳健模型。