Department of Chemical Engineering, University of Massachusetts Lowell, One University Avenue, Lowell, MA 01854, USA.
Biotechnol Bioeng. 2012 Nov;109(11):2819-28. doi: 10.1002/bit.24548. Epub 2012 May 28.
In mammalian cell culture producing therapeutic proteins, one of the important challenges is the use of several complex raw materials whose compositional variability is relatively high and their influences on cell culture is poorly understood. Under these circumstances, application of spectroscopic techniques combined with chemometrics can provide fast, simple, and non-destructive ways to evaluate raw material quality, leading to more consistent cell culture performance. In this study, a comprehensive data fusion strategy of combining multiple spectroscopic techniques is investigated for the prediction of raw material quality in mammalian cell culture. To achieve this purpose, four different spectroscopic techniques of near-infrared, Raman, 2D fluorescence, and X-ray fluorescence spectra were employed for comprehensive characterization of soy hydrolysates which are commonly used as supplements in culture media. First, the different spectra were compared separately in terms of their prediction capability. Then, ensemble partial least squares (EPLS) was further employed by combining all of these spectral datasets in order to produce a more accurate estimation of raw material properties, and compared with other data fusion techniques. The results showed that data fusion models based on EPLS always exhibit best prediction accuracy among all the models including individual spectroscopic methods, demonstrating the synergetic effects of data fusion in characterizing the raw material quality.
在生产治疗性蛋白的哺乳动物细胞培养中，一个重要的挑战是使用几种复杂的原材料，这些原材料的组成变异性相对较高，其对细胞培养的影响也知之甚少。在这种情况下，应用光谱技术结合化学计量学可以提供快速、简单和非破坏性的方法来评估原材料质量，从而实现更一致的细胞培养性能。在这项研究中，研究了一种综合的多光谱技术数据融合策略，用于预测哺乳动物细胞培养中的原材料质量。为了实现这一目的，采用了近红外、拉曼、2D 荧光和 X 射线荧光光谱四种不同的光谱技术，对常用于培养基补充物的大豆水解物进行全面表征。首先，分别比较了不同光谱在预测能力方面的差异。然后，通过组合所有这些光谱数据集，进一步采用集成偏最小二乘法（EPLS），以产生更准确的原材料特性估计，并与其他数据融合技术进行比较。结果表明，基于 EPLS 的数据融合模型在所有模型（包括单个光谱方法）中始终表现出最佳的预测准确性，这证明了数据融合在表征原材料质量方面的协同作用。
