This study introduces an approach to enhance the Raman calibration model cross-scale prediction capabilities in cell cultures. Our investigation centers on the improvement of Raman calibration models along with the scaling-up of mammalian cell culture processes. Initially, we observed that integrating data from a 50 L run into the original dataset at lab-scale was an effective strategy for improving prediction accuracy on 1000 L run. Further investigation indicated that this improvement could be attributed to the similarity between the spectra of 50 L with 1000 L. Subsequent investigation unveiled that the spectral variations primarily stemmed from differences between the instruments. This was verified through a direct comparison of two Raman analyzers. Then two pairwise spectral transformation methods, direct standardization (DS) and piecewise DS, both exhibited superior performance in predicting. This research underscores the importance of Raman spectral transfer method to alleviate instrument-to-instrument variations in cross-scale monitoring of cell culture processes.

中文翻译：

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增强大规模生产中细胞培养过程的跨尺度拉曼在线监测能力


本研究介绍了一种在细胞培养中增强拉曼校准模型跨尺度预测能力的方法。我们的研究集中在拉曼校准模型的改进以及哺乳动物细胞培养过程的放大。最初，我们观察到，在实验室规模将 50 L 运行的数据整合到原始数据集中是提高 1000 L 运行预测准确性的有效策略。进一步的研究表明，这种改进可归因于 50 L 和 1000 L 光谱之间的相似性。随后的调查发现，光谱变化主要源于仪器之间的差异。通过直接比较两台拉曼分析仪，验证了这一点。然后，直接标准化 （DS） 和分段 DS 两种成对谱变换方法在预测方面都表现出优异的性能。这项研究强调了拉曼光谱转移方法在减轻细胞培养过程跨尺度监测中仪器间差异的重要性。