Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, Germany.
J Biotechnol. 2013 Dec;168(4):636-45. doi: 10.1016/j.jbiotec.2013.08.002. Epub 2013 Aug 12.
Process analytical technology (PAT) is a guide to improve process development in biotech industry. Optical sensors such as near and mid infrared spectrometers fulfill an essential part for PAT. NIRS and MIRS were investigated as non-invasive on line monitoring tools for animal cell cultivations in order to predict critical process parameters, like cell parameters as well as substrate and metabolite concentrations. Eight cultivations were performed with frequent sampling. Variances between cultivations were induced by spiking experiments with intent to break correlations between analytes; to keep causality of the models; and to increase model robustness. Calibration models were built for each analyte using partial least-squares regression method. Cultivations chosen for validation were not part of the calibration set. Glucose concentration, cell density and viability were predicted by NIRS with a root mean square error of prediction (RMSEP) of 0.36 g/L, 3.9 10(6)cells/mL and 3.62% respectively. Based on MIR spectra glucose and lactate concentrations were predicted with a RMSEP of 0.16 and 0.14 g/L respectively. Results show that MIRS has higher accuracy regarding the prediction of single analytes. For prediction of a main course of a cultivation, NIRS is much better suited than MIRS.
过程分析技术（PAT）是指导生物技术行业改进工艺开发的指南。近红外和中红外光谱仪等光学传感器是 PAT 的重要组成部分。本研究旨在考察近红外和中红外光谱作为非侵入式在线监测工具，用于预测动物细胞培养的关键过程参数，如细胞参数以及底物和代谢物浓度。进行了 8 次频繁取样的培养实验。通过有目的的冲击实验来引入培养实验之间的差异，以打破分析物之间的相关性；保持模型的因果关系；并提高模型的稳健性。使用偏最小二乘回归方法为每个分析物建立校准模型。用于验证的培养物不包含在校准集中。NIRS 预测葡萄糖浓度、细胞密度和活力的 RMSEP 分别为 0.36 g/L、3.9×10(6)cells/mL 和 3.62%。基于 MIR 光谱，葡萄糖和乳酸浓度的 RMSEP 预测值分别为 0.16 和 0.14 g/L。结果表明，MIRS 在单个分析物的预测方面具有更高的准确性。对于培养过程的主要阶段预测，NIRS 比 MIRS 更适合。
