期刊
ANALYTICA CHIMICA ACTA
卷 1026, 期 -, 页码 62-68出版社
ELSEVIER
DOI: 10.1016/j.aca.2018.04.055
关键词
Metabolomics; QC-SVRC; Within-batch effects; Experimental design
资金
- Spanish Ministry of Health, Social Services and Equality [EC11-246]
- Agencia Estatal de Investigacion (AEI)
- Fondo Europeo de Desarrollo Regional [CTQ2016-79561-P]
- Conselleria de Educacion, Investigacion, Cultura y Deporte (Generalitat Valenciana) [GV/2016/062]
- Instituto de Salud Carlos III, Ministry of Economy and Competitiveness, Spain [CP16/00034]
- Instituto de Salud Carlos III [FI16/00380]
Ultra performance liquid chromatography-mass spectrometry (UPLC-MS) is increasingly being used for untargeted metabolomics in biomedical research. Complex matrices and a large number of samples per analytical batch lead to gradual changes in the instrumental response (i.e. within-batch effects) that reduce the repeatability and reproducibility and limit the power to detect biological responses. A strategy for within-batch effect correction based on the use of quality control (QC) samples and Support Vector Regression (QC-SVRC) with a radial basis function kernel was recently proposed. QC-SVRC requires the optimization of three hyperparameters that determine the accuracy of the within-batch effects elimination: the tolerance threshold (epsilon), the penalty term (C) and the kernel width (gamma). This work compares three widely used strategies for QC-SVRC hyperparameter optimization (grid search, random search and particle swarm optimization) using a UPLC-MS data set containing 193 urine injections as model example. Results show that QC-SVRC is robust to hyperparameter selection and that a pre-selection of C and e, followed by optimization of g is competitive in terms of accuracy, precision and number of function evaluations with full grid analysis, random search and particle swarm optimization. The QC-SVRC optimization procedure can be regarded as a useful non-parametric tool for efficiently complementing alternative approaches such as QC-robust splines correction (RSC). (c) 2018 Elsevier B.V. All rights reserved.
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