Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification
Author:
Affiliation:
1. Drug Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
2. Department of Pharmacy and Biotechnology, University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
Publisher
American Chemical Society (ACS)
Subject
Analytical Chemistry
Link
http://pubs.acs.org/doi/pdf/10.1021/acs.analchem.6b02075
Reference33 articles.
1. Advantages of ultra performance liquid chromatography over high-performance liquid chromatography: Comparison of different analytical approaches during analysis of diclofenac gel
2. Comparison of ultra-performance liquid chromatography and high-performance liquid chromatography for the determination of priority pesticides in baby foods by tandem quadrupole mass spectrometry
3. Evaluation of the repeatability of ultra-performance liquid chromatography–TOF-MS for global metabolic profiling of human urine samples
4. Quantitative structure–retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: Endogenous metabolites and banned compounds
5. Gradient retention prediction of acid–base analytes in reversed phase liquid chromatography: A simplified approach for acetonitrile–water mobile phases
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