On-line monitoring of antibody concentration during protein A affinity chromatography using Raman spectroscopy

被引：0

作者：

Fan, Yu ^{[1
]}

Qiao, Liangzhi ^{[1
]}

Ji, Kaidi ^{[2
]}

Yan, Xu ^{[3
]}

Gao, Dong ^{[3
]}

Wang, Haibin ^{[3
]}

Ruan, Yinlan ^{[2
]}

Yao, Shanjing ^{[1
]}

Lin, Dongqiang ^{[1
]}

机构：

[1] Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou

[2] School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin

[3] Bioray Pharmaceutical (Hangzhou) Co., Ltd., Hangzhou

来源：

Gao Xiao Hua Xue Gong Cheng Xue Bao/Journal of Chemical Engineering of Chinese Universities | 2025年 / 39卷 / 02期

关键词：

affinity chromatography; online monitor; partial least squares; Raman spectroscopy; ridge regression;

D O I：

10.3969/j.issn.1003-9015.2025.02.009

中图分类号：

学科分类号：

摘要：

To determine the antibody concentration in cell culture broth during protein A affinity chromatography, Raman spectrum was used and a model was established for online measurement of antibody concentration in complex feedstock. The partial least squares (PLS) regression and ridge regression models were investigated with the Raman spectral data under different loading conditions. The results indicate that the PLS model had a minimum Root Mean Square Error of Cross-Validation (RMSECV) of 0.088 g.L-l and a Root Mean Square Error of Prediction (RMSEP) of 0.112 g.L. In comparison, the ridge regression model achieved an RMSECV of 0.097 g.L-1 and an RMSEP of 0.055 g.L-'. The ridge regression model could balance the model complexity and accuracy, reduce the risk of overfitting and improve predictive performance. Additionally, with various data splits, the ridge regression was always better than PLS in both fitting and prediction. The results demonstrate that the ridge regressionbased Raman spectrum model can accurately predict antibody concentration in complex feedstock during the protein A affinity chromatography, offering a promising tool for online monitoring in continuous antibody capture process. © 2025 Zhejiang University. All rights reserved.

引用

页码：263 / 272

页数：9

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