Quantitative Structure Retention Relationship Models in an Analytical Quality by Design Framework: Simultaneously Accounting for Compound Properties, Mobile-Phase Conditions, and Stationary-Phase Properties
| Autor: | Gang Xue, David Fortin, George L. Reid, Koji Muteki, Jeffrey W. Harwood, James E. Morgado, Ian J. Miller, Frank Riley, Jian Wang |
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| Rok vydání: | 2013 |
| Předmět: |
Analyte
Chromatography General Chemical Engineering Mean squared prediction error Quantitative structure General Chemistry Linear discriminant analysis Industrial and Manufacturing Engineering Quality by Design Stationary phase Phase (matter) Partial least squares regression Biological system Mathematics |
| Zdroj: | Industrial & Engineering Chemistry Research. 52:12269-12284 |
| ISSN: | 1520-5045 0888-5885 |
| Popis: | Quantitative structure retention relationships (QSRRs) can play an important role in enhancing the speed and quality of chromatographic method development. This paper presents a novel (compound-classification-based) QSRR modeling strategy that simultaneously accounts for the analyte properties, mobile-phase conditions, and stationary-phase properties. It involves the adoption of two models: (A) partial-least-squares discriminate analysis (PLS-DA) to classify compounds into subclasses having similar interactive relationships between the mobile-phase conditions and stationary phase; (B) L partial least squares (L-PLS) to predict the compound’s retention time based on the mobile-phase conditions, stationary phase, and compound properties. For the retention time of a compound to be modeled, the most favorable compound class is identified in an optimization framework that simultaneously minimizes both the compound misclassification rate (based on PLS-DA) and the retention time prediction error (based on L-PLS)... |
| Databáze: | OpenAIRE |
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