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 BV]
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
蔡从安发布了新的文献求助10
2秒前
廿三完成签到,获得积分10
3秒前
JamesPei应助兰先生采纳,获得10
5秒前
李老头完成签到,获得积分10
8秒前
duoduo完成签到 ,获得积分10
9秒前
辛勤的囧完成签到,获得积分10
15秒前
18秒前
甘乐完成签到 ,获得积分10
23秒前
马登完成签到,获得积分10
24秒前
yhyhyhyh完成签到,获得积分10
31秒前
Nexus应助Star1983采纳,获得10
32秒前
illusion完成签到,获得积分10
33秒前
zhaoxiaonuan完成签到,获得积分10
37秒前
38秒前
BaronR完成签到,获得积分10
38秒前
贝贝完成签到 ,获得积分10
38秒前
你才是小哭包完成签到 ,获得积分10
39秒前
40秒前
淳于语薇完成签到 ,获得积分10
41秒前
DY发布了新的文献求助10
41秒前
2075完成签到,获得积分10
41秒前
xzy998应助海盗船长采纳,获得10
43秒前
大力的灵雁应助拓跋雨梅采纳,获得10
52秒前
精明凡雁完成签到,获得积分10
55秒前
Patience完成签到,获得积分10
56秒前
Sulphide完成签到,获得积分10
57秒前
Skyllne完成签到 ,获得积分10
58秒前
舒心谷雪完成签到 ,获得积分10
1分钟前
勤恳的板凳完成签到 ,获得积分10
1分钟前
反对比较完成签到,获得积分10
1分钟前
luokm完成签到,获得积分10
1分钟前
沫荔完成签到 ,获得积分10
1分钟前
xm完成签到,获得积分10
1分钟前
英姑应助燕子采纳,获得10
1分钟前
萧晓完成签到 ,获得积分10
1分钟前
科研通AI6.4应助如沐春风采纳,获得10
1分钟前
无尘完成签到 ,获得积分0
1分钟前
HMethod完成签到 ,获得积分10
1分钟前
jinyu发布了新的文献求助10
1分钟前
小二郎应助小舟采纳,获得10
1分钟前
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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Various Faces of Animal Metaphor in English and Polish 800
Signals, Systems, and Signal Processing 610
Photodetectors: From Ultraviolet to Infrared 500
On the Dragon Seas, a sailor's adventures in the far east 500
Yangtze Reminiscences. Some Notes And Recollections Of Service With The China Navigation Company Ltd., 1925-1939 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6353195
求助须知:如何正确求助?哪些是违规求助? 8168047
关于积分的说明 17191530
捐赠科研通 5409231
什么是DOI,文献DOI怎么找? 2863646
邀请新用户注册赠送积分活动 1840978
关于科研通互助平台的介绍 1689834