2020
DOI: 10.1021/acs.analchem.0c04190
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Prediction of Analyte Retention Time in Liquid Chromatography

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Cited by 93 publications
(66 citation statements)
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“…Other machine-learning methods such as multiple linear regression [ 21 ], partial least squares regression [ 22 ], support vector machine [ 23 ] also have been tested and achieved a considerable level of accuracy. A distinction between global and local modeling is important and has been clarified in a review paper [ 24 ]. Local modeling can relate to a limited range of solvent compositions or to a limited range of analytes for which the model is suited.…”
Section: Related Workmentioning
confidence: 99%
“…Other machine-learning methods such as multiple linear regression [ 21 ], partial least squares regression [ 22 ], support vector machine [ 23 ] also have been tested and achieved a considerable level of accuracy. A distinction between global and local modeling is important and has been clarified in a review paper [ 24 ]. Local modeling can relate to a limited range of solvent compositions or to a limited range of analytes for which the model is suited.…”
Section: Related Workmentioning
confidence: 99%
“…Despite the success of CQSRR, the often large number of partly 'mysterious' descriptors [38] required to predict retention accurately makes the underlying retention mechanisms difficult to understand. Retention depends on the physiochemical properties of the solutes, which cannot be deduced from their atomic composition; that is, solutes with the same molecular formula can have very different retention behaviours [39]. Predictive models require training with representative (in terms of the physiochemical properties) solutes, a task that is challenging for large molecules owning to their intrinsic complexity.…”
Section: Retention Modelsmentioning
confidence: 99%
“…[3][4][5][6] The investigation of factors governing chromatographic separation mechanisms remains an area of active research, but often the prediction of retention using a retention equation derived only from full understanding of the separation mechanism is not a viable prospect. [7] For this reason, a key objective in the development of a fast and reliable analytical chromatographic methodology as a routine tool in pharmaceutical analysis is to use a priori computational tools whereby the chemical structure of an analyte can be used to make a retention prediction of sufficient accuracy to identify broad chromatographic conditions that meet the optimal level of performance requirements for the separation of compounds in interest. Thus, the goal of the retention prediction process is to enable the operator to choose which stationary phase is to be used and the approximate composition of the mobile phase.…”
Section: Introductionmentioning
confidence: 99%
“…In terms of computational methods in analytical method development, the application of quality by design (QbD) concepts is promising. [7][8][9][10][11] Most recent method optimisations have indicated that modern QbD strategies are indeed capable of making a prediction about the method operable design region with a high degree of fidelity. [7] Such investigations have mainly been made possible by the incorporation of a design of experiments (DoE) [12,13] philosophy into the QbD methodology, which allows rapid determination of multiple optimal assay parameters while leading to a minimised assay development and optimisation timeline.…”
Section: Introductionmentioning
confidence: 99%
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