Department of Chemical and Biochemical Engineering, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
Biotechnol Prog. 2009 Nov-Dec;25(6):1561-81. doi: 10.1002/btpr.280.
Near-infrared (NIR) spectroscopy can potentially provide on-line information on substrate, biomass, product, and metabolite concentrations in fermentation processes, which could be useful for improved monitoring or control. However, several factors can negatively influence the quality of chemometric models built for interpretation of the spectra, thus impairing the analyte concentration predictions. The aim of this review was to provide an overview of necessary conditions and challenges that one has to face when developing a NIR application for monitoring of cell culture or fermentation processes. Important practical aspects are introduced, such as sampling, modeling of biomass concentration, influence of microorganism morphology on the spectra, effects of the hydrodynamic conditions in the fermenter, temperature influence, instrument settings, and signal optimization. Several examples from the literature are provided, which will hopefully guide the reader interested in the topic. Furthermore, the general procedure used for the development of calibration models is presented, and the influence of microorganism metabolism-potential source of correlation between analytes-is commented. Other important issues such as wavelength selection and evaluation of robustness are shortly introduced. Finally, some examples of potential applications of NIR monitoring are provided, including the implementation of control strategies, the combination with other monitoring tools (the so-called sensor fusion), and the description of process trajectories. On the basis of the review, we conclude that acceptance of NIR spectroscopy as a standard monitoring tool by the fermentation industry will necessitate considerably more on-line studies using industrially relevant-and highly challenging-fermentation conditions (high aeration intensity, high biomass concentration and viscosity, and filamentous production strain).
近红外（NIR）光谱技术可以为发酵过程中的底物、生物量、产物和代谢物浓度提供在线信息，这对于改进监测或控制可能非常有用。然而，有几个因素会对用于解释光谱的化学计量模型的质量产生负面影响，从而影响分析物浓度的预测。本综述的目的是提供一个概述，介绍在开发用于监测细胞培养或发酵过程的 NIR 应用时必须面对的必要条件和挑战。介绍了一些重要的实际方面，例如采样、生物量浓度建模、微生物形态对光谱的影响、发酵罐中流体力学条件的影响、温度影响、仪器设置和信号优化。提供了来自文献中的几个示例，希望能为有兴趣的读者提供指导。此外，还介绍了开发校准模型一般使用的程序，并对分析物之间相关性的潜在来源——微生物代谢的影响进行了评论。还简要介绍了其他一些重要问题，如波长选择和稳健性评估。最后，提供了一些 NIR 监测的潜在应用示例，包括控制策略的实施、与其他监测工具（所谓的传感器融合）的结合以及过程轨迹的描述。基于综述，我们得出结论，NIR 光谱技术要被发酵行业接受为标准监测工具，就需要使用更具工业相关性且极具挑战性的发酵条件（高通气强度、高生物量浓度和高粘度以及丝状生产菌株）进行更多的在线研究。
