This study presents a novel approach for developing generic metabolic Raman calibration models for in-line cell culture analysis using glucose and lactate stock solution titration in an aqueous phase and data augmentation techniques. First, a successful set-up of the titration method was achieved by adding glucose or lactate solution at several different constant rates into the aqueous phase of a bench-top bioreactor. Subsequently, the in-line glucose and lactate concentration were calculated and interpolated based on the rate of glucose and lactate addition, enabling data augmentation and enhancing the robustness of the metabolic calibration model. Nine different combinations of spectra pretreatment, wavenumber range selection, and number of latent variables were evaluated and optimized using aqueous titration data as training set and a historical cell culture data set as validation and prediction set. Finally, Raman spectroscopy data collected from 11 historical cell culture batches (spanning four culture modes and scales ranging from 3 to 200 L) were utilized to predict the corresponding glucose and lactate values. The results demonstrated a high prediction accuracy, with an average root mean square errors of prediction of 0.65 g/L for glucose, and 0.48 g/L for lactate. This innovative method establishes a generic metabolic calibration model, and its applicability can be extended to other metabolites, reducing the cost of deploying real-time cell culture monitoring using Raman spectroscopy in bioprocesses.

中文翻译：

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使用水相中的溶液滴定和数据增强来开发通用代谢拉曼校准模型，用于在线细胞培养分析

本研究提出了一种使用水相中的葡萄糖和乳酸储备溶液滴定和数据增强技术来开发用于在线细胞培养分析的通用代谢拉曼校准模型的新方法。首先，通过将葡萄糖或乳酸溶液以几种不同的恒定速率添加到台式生物反应器的水相中，成功建立了滴定方法。随后，根据葡萄糖和乳酸的添加速率计算并插值在线葡萄糖和乳酸浓度，从而实现数据扩充并增强代谢校准模型的稳健性。使用水滴定数据作为训练集和历史细胞培养数据集作为验证和预测集来评估和优化光谱预处理、波数范围选择和潜在变量数量的九种不同组合。最后，利用从 11 个历史细胞培养批次（涵盖 4 种培养模式和 3 至 200 L 规模）收集的拉曼光谱数据来预测相应的葡萄糖和乳酸值。结果表明预测精度很高，葡萄糖的平均预测均方根误差为 0.65 g/L，乳酸的平均预测误差为 0.48 g/L。这种创新方法建立了通用的代谢校准模型，其适用性可以扩展到其他代谢物，从而降低在生物过程中使用拉曼光谱技术部署实时细胞培养监测的成本。