Using Procedural Blanks to Generate Analyte-Specific Limits of Detection for Persistent Organic Pollutants Based on GC-MS Analysis

Several methods are used to generate a limit of detection for organic pollutants measured by gas chromatography–mass spectroscopy (GC-MS); all have theoretical and practical drawbacks. The current project investigated two common existing methods (statistical and empirical) for applicability to chromatographic properties from real samples, comparing these with a new proposed method using procedural blanks to estimate a minimum detectable peak area. Weaknesses of all three methods are discussed. The proposed method was superior to other examined methods in that it provided analyte-specific limits of detection linked to the recovery of mass-labeled internal standards for every analyte within every sample. Other identified quality assurance benefits included the following: enhanced protection against false positives; providing a sensitivity performance metric across batch, analyst, and instrument; enabling chemists with discretionary decisions specific to every analyte regarding detectability and interferences; and some strengths of both statistical and empirical techniques without major drawbacks of either. In marine sediment samples, the proposed method of calculating the limit of detection increased reporting of trace level (low- to subppb) GC-MS data for polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), organochlorine pesticides (OCPs), and polycyclic aromatic hydrocarbons (PAHs) by up to 400% compared with the statistical method

LipidQC: Method Validation Tool for Visual Comparison to SRM 1950 Using NIST Interlaboratory Comparison Exercise Lipid Consensus Mean Estimate Values

As advances in analytical separation techniques, mass spectrometry instrumentation, and data processing platforms continue to spur growth in the lipidomics field, more structurally unique lipid species are detected and annotated. The lipidomics community is in need of benchmark reference values to assess the validity of various lipidomics workflows in providing accurate quantitative measurements across the diverse lipidome. LipidQC addresses the harmonization challenge in lipid quantitation by providing a semiautomated process, independent of analytical platform, for visual comparison of experimental results of National Institute of Standards and Technology Standard Reference Material (SRM) 1950, “Metabolites in Frozen Human Plasma”, against benchmark consensus mean concentrations derived from the NIST Lipidomics Interlaboratory Comparison Exercise