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Raman spectroscopic based chemometric models to support a dynamic capacitance based cell culture feeding strategy

机译:基于拉曼光谱的化学计量型,支持动态电容的细胞培养策略

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摘要

Multiple process analytical technology (PAT) tools are now being applied in tandem for cell culture. Research presented used two in-line probes, capacitance for a dynamic feeding strategy and Raman spectroscopy for real-time monitoring. Data collected from eight batches at the 15,000 L scale were used to develop process models. Raman spectroscopic data were modelled using Partial Least Squares (PLS) by two methods-(1) use of the full dataset and (2) split the dataset based on the capacitance feeding strategy. Root mean square error of prediction (RMSEP) for the first model method of capacitance was 1.54 pf/cm and the second modelling method was 1.40 pf/cm. The second Raman method demonstrated results within expected process limits for capacitance and a 0.01% difference in total nutrient feed compared to the capacitance probe. Additional variables modelled using Raman spectroscopy were viable cell density (VCD), viability, average cell diameter, and viable cell volume (VCV).
机译:现在,多工艺分析技术(PAT)工具正在串联中适用于细胞培养物。研究呈现了两种在线探头,用于动态馈送策略的电容和用于实时监测的拉曼光谱。从15,000 L级别收集的数据从八个批次收集,用于开发过程模型。使用部分最小二乘(PL)通过两种方法(1)使用完整数据集和(2)基于电容馈送策略来分离数据集的使用模式,使用拉曼光谱数据。用于第一模型电容方法的预测(RMSEP)的均方根误差为1.54pf / cm,第二型造型方法为1.40pf / cm。第二拉曼方法在与电容探针相比的预期过程限度和总营养源的差异内显示出导致的结果和0.01%。使用拉曼光谱建模的附加变量是活细胞密度(VCD),活力,平均细胞直径和活细胞体积(VCV)。

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