2023
DOI: 10.1016/j.chroma.2023.464443
|View full text |Cite
|
Sign up to set email alerts
|

Experimental design and re-parameterization of the Neue-Kuss model for accurate and precise prediction of isocratic retention factors from gradient measurements in reversed phase liquid chromatography

Sarah C. Rutan,
Kathryn Cash,
Dwight R. Stoll
Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(3 citation statements)
references
References 20 publications
0
3
0
Order By: Relevance
“…where kw, S1 and S2 are solute/condition-specific model parameters. The fitting was carried out using a re-parameterization of the NK model where the model parameters were calculated based on the retention factor at  = 0.30 as a reference point (kref) instead of the more conventional kw, as described in a recent publication [31]. The model is then given in revised form as…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…where kw, S1 and S2 are solute/condition-specific model parameters. The fitting was carried out using a re-parameterization of the NK model where the model parameters were calculated based on the retention factor at  = 0.30 as a reference point (kref) instead of the more conventional kw, as described in a recent publication [31]. The model is then given in revised form as…”
Section: Discussionmentioning
confidence: 99%
“…However, in 72 of the 1157 cases, the retention factor at 40% ACN was not measured experimentally, in most cases because the retention factor was too large to be practically determined at this mobile phase composition. Therefore, these missing values were estimated by fitting the available data for those column/solute combinations to the NK model as described above [31]. This methodology allowed for the rejection of outliers [31], and provided stable estimates for the NK parameters.…”
Section: Initial Construction Of Datasetmentioning
confidence: 99%
See 1 more Smart Citation