On-Capillary Surface-Enhanced Raman Spectroscopy: Determination of Glutathione in Whole Blood Microsamples.

Oxidative stress monitoring in the neonatal period supports early outcome prediction and treatment. Glutathione (GSH) is the most abundant antioxidant in most cells and tissues, including whole blood, and its usefulness as a biomarker has been known for decades. To date, the available methods for GSH determination require laborious sample processing and the use of sophisticated laboratory equipment. To the best of our knowledge, no tools suitable for point-of-care (POC) sensing have been reported. Surface-enhanced Raman spectroscopy (SERS), performed in a microvolume capillary measurement cell, is proposed in this study as a robust approach for the quantification of GSH in human whole blood samples. The use of a silver colloid allowed a highly selective signal enhancement for GSH providing analytical enhancement factors of 3 to 4 orders of magnitude. A highly accurate determination of GSH in whole blood samples with recoveries ranging from 99 to 107% and relative standard deviations less than or equal to 18% were achieved by signal normalization with the intensity of an isotopically labeled internal standard. GSH concentrations were retrieved within 4 min using small-volume blood samples (2 µL). The developed procedure was applied to the analysis of blood of 20 healthy adults and 36 newborns, obtaining comparable results between literature and those found by SERS and a reference method. The characteristics of this novel tool are suitable for its implementation in a portable optical sensor device enabling POC testing of oxidative stress levels in newborns.

Model selection for within-batch effect correction in UPLC-MS metabolomics using quality control - Support vector regression.

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 (ε), the penalty term (C) and the kernel width (γ). 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 ε, followed by optimization of γ 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).