Background Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition. Aim of review This tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported. Key scientific concepts of review System suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.

Fetal life evolves in a hypoxic environment. Changes in the oxygen content in utero caused by conditions such as pre-eclampsia or type I diabetes or by oxygen supplementation to the mother lead to increased free radical production and correlate with perinatal outcomes. In the fetal-to-neonatal transition asphyxia is characterized by intermittent periods of hypoxia ischemia that may evolve to hypoxic ischemic encephalopathy associated with neurocognitive, motor, and neurosensorial impairment. Free radicals generated upon reoxygenation may notably increase brain damage. Hence, clinical trials have shown that the use of 100% oxygen given with positive pressure in the airways of the newborn infant during resuscitation causes more oxidative stress than using air, and increases mortality. Preterm infants are endowed with an immature lung and antioxidant system. Clinical stabilization of preterm infants after birth frequently requires positive pressure ventilation with a gas admixture that contains oxygen to achieve a normal heart rate and arterial oxygen saturation. In randomized controlled trials the use high oxygen concentrations (90% to 100%) has caused more oxidative stress and clinical complications that the use of lower oxygen concentrations (30-60%). A correlation between the amount of oxygen received during resuscitation and the level of biomarkers of oxidative stress and clinical outcomes was established. Thus, based on clinical outcomes and analytical results of oxidative stress biomarkers relevant changes were introduced in the resuscitation policies. However, it should be underscored that analysis of oxidative stress biomarkers in biofluids has only been used in experimental and clinical research but not in clinical routine. The complexity of the technical procedures, lack of automation, and cost of these determinations have hindered the routine use of biomarkers in the clinical setting. Overcoming these technical and economical difficulties constitutes a challenge for the immediate future since accurate evaluation of oxidative stress would contribute to improve the quality of care of our neonatal patients.

Instrumental developments in sensitivity and selectivity boost the application of liquid chromatography-mass spectrometry (LC-MS) in metabolomics. Gradual changes in the LC-MS instrumental response (i.e. intra-batch effect) are often unavoidable and they reduce the repeatability and reproducibility of the analysis, decrease the power to detect biological responses and hinder the interpretation of the information provided. Because of that, there is interest in the development of chemometric techniques for the post-acquisition correction of batch effects. In this work, the use of quality control (QC) samples and support vector regression (QC-SVRC) and a radial basis function kernel is proposed to correct intra-batch effects. The repeated analysis of a single sample is used for the assessment of both the correction accuracy and the effect of the distribution of QC samples throughout the batch. The QC-SVRC method is evaluated and compared with a recently developed method based on QC samples and robust cubic smoothing splines (QC-RSC). The results show that QC-SVRC slightly outperformed QC-RSC and allows a straightforward fitting of the SVRC parameters to the instrument performance by using the ε-insensitive loss parameter.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.