Parameter-by-parameter method for steric mass action model of ion exchange chromatography: Theoretical considerations and experimental verification

化学 生物系统 校准 线性回归 色谱法 稳健性(进化) 算法 数学 统计 生物化学 生物 基因
作者
Yucheng Chen,Shan‐Jing Yao,Dong‐Qiang Lin
出处
期刊:Journal of Chromatography A [Elsevier]
卷期号:1680: 463418-463418 被引量:18
标识
DOI:10.1016/j.chroma.2022.463418
摘要

Ion exchange chromatography (IEC) is one of the most widely-used techniques for protein separation and has been characterized by mechanistic models. However, the time-consuming and cumbersome model calibration hinders the application of mechanistic models for process development. A new methodology called "parameter-by-parameter method (PbP)" was proposed with mechanistic derivations of the steric mass action (SMA) model of IEC. The protocol includes four steps: (1) first linear regression (LR1) for characteristic charge; (2) second linear regression (LR2) for equilibrium coefficient; (3) linear approximation (LA) for shielding factor; (4) inverse method (IM) for kinetic coefficient. Four SMA parameters could be one-by-one determined in sequence, reducing the number of unknown parameters per species from four to one, and predicting almost consistent retention. Numerical single-component experiments were investigated firstly, and the PbP method showed excellent agreement between experiments and simulations. The effects of loadings on the PbP and Yamamoto methods were compared. It was found that the PbP method had higher accuracy and robustness than the Yamamoto method. Moreover, a five-experiment strategy was suggested to implement the PbP method, which is straightforward to reduce the cost of calibration experiments. Finally, a real-world multi-component separation was challenged and further confirmed the feasibility of the PbP method. In general, the proposed method can not only reliably estimate the SMA parameters with comprehensive physical understanding but also accurately predict retention over a wide range of loading conditions.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
WW完成签到 ,获得积分10
刚刚
猪栏完成签到,获得积分20
刚刚
WIK完成签到,获得积分10
1秒前
cnspower完成签到,获得积分0
1秒前
Khuram应助璃纱采纳,获得10
2秒前
山月完成签到,获得积分10
2秒前
2秒前
3秒前
写论文的完成签到 ,获得积分10
3秒前
3秒前
toner发布了新的文献求助10
3秒前
4秒前
面包发布了新的文献求助20
4秒前
4秒前
4秒前
4秒前
高航飞发布了新的文献求助10
5秒前
潘女士完成签到,获得积分10
5秒前
5秒前
科研通AI6.3应助漏漏漏采纳,获得10
5秒前
5秒前
量子星尘发布了新的文献求助10
5秒前
6秒前
林知鲸落发布了新的文献求助20
6秒前
月关发布了新的文献求助10
6秒前
科研通AI6.2应助幽默盼柳采纳,获得10
6秒前
带善人完成签到,获得积分10
7秒前
7秒前
曹毅凯完成签到,获得积分10
7秒前
7秒前
天才小仙女完成签到,获得积分10
8秒前
KYT完成签到,获得积分10
8秒前
zmz完成签到,获得积分10
8秒前
copper发布了新的文献求助10
8秒前
aaa发布了新的文献求助10
9秒前
Mic应助achovy采纳,获得10
9秒前
9秒前
villain发布了新的文献求助10
9秒前
9秒前
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Terrorism and Power in Russia: The Empire of (In)security and the Remaking of Politics 1000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6046333
求助须知:如何正确求助?哪些是违规求助? 7821536
关于积分的说明 16251588
捐赠科研通 5191744
什么是DOI,文献DOI怎么找? 2778052
邀请新用户注册赠送积分活动 1761223
关于科研通互助平台的介绍 1644168