Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary; Biotechnology Development Department, Gedeon Richter Plc., Gyömrői út 19-21, H-1103, Budapest, Hungary.
Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary.
J Pharm Biomed Anal. 2018 Sep 5;158:269-279. doi: 10.1016/j.jpba.2018.06.005. Epub 2018 Jun 7.
In-situ Raman spectroscopy is frequently applied to monitor and even control the glucose concentration of monoclonal antibody producing mammalian cell cultivations. Previous studies used the PLSR algorithm only, however other multivariate algorithms were applied successfully for different protein production processes. In this study, four mammalian cell cultivation runs were followed with Raman spectroscopy and the spectra were analysed quantitatively and qualitatively as well. The PCA analysis showed that one of the most dominant factors in the Raman spectra were the concentration of glucose, which strongly correlated with the score values of the eighth principal component. This observation further substantiated that Raman spectroscopy is an excellent tool for bioprocess monitoring and induced the test of the Multivariate Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) algorithms, using the results of the PCA as one of the variable selection techniques, to determine the glucose concentration during cultivation. However, the novel variable selection technique of PCA correlation enhanced only the model accuracy when it was applied with MLR and only model robustness was increased when it was used with PCR and PLSR because the relatively strong Raman signal of glucose concentration seemed to be enough to build an accurate model on. Therefore, PLSR, the most advanced algorithm of the three, delivered the lowest 2.21 mM RMSEP but it was demonstrated that in certain cases PCR could also produce satisfactorily results.
原位拉曼光谱技术常用于监测甚至控制单克隆抗体生产哺乳动物细胞培养物的葡萄糖浓度。先前的研究仅使用了偏最小二乘回归（PLSR）算法，但其他多元算法已成功应用于不同的蛋白质生产过程。在这项研究中，我们跟踪了四个哺乳动物细胞培养实验，同时使用拉曼光谱进行监测，并对光谱进行了定量和定性分析。主成分分析（PCA）表明，拉曼光谱中最主要的因素之一是葡萄糖浓度，它与第八个主成分的得分值强烈相关。这一观察结果进一步证实了拉曼光谱是生物过程监测的极好工具，并促使我们测试多元线性回归（MLR）、主成分回归（PCR）和偏最小二乘回归（PLSR）算法，将 PCA 的结果作为其中一种变量选择技术，以确定培养过程中的葡萄糖浓度。然而，PCA 相关系数这种新颖的变量选择技术仅在与 MLR 一起使用时提高了模型的准确性，仅在与 PCR 和 PLSR 一起使用时提高了模型的稳健性，因为葡萄糖浓度的相对较强的拉曼信号似乎足以建立一个准确的模型。因此，作为这三种算法中最先进的算法，PLSR 产生了最低的 2.21mM RMSEP，但也证明了在某些情况下 PCR 也可以产生令人满意的结果。
