The quality of biological samples used in metabolomics research is significantly influenced by preanalytical factors, such as the timing of centrifugation and freezing. This study aimed to evaluate how preanalytical factors, like delays in centrifugation and freezing, affect metabolomics research. Blood samples, collected in various tube types, were subjected to controlled pre- and postcentrifugation delays. Metabolite levels were quantified using NMR spectroscopy and fitted in linear mixed models used to predict changes in metabolite concentrations over time. The results showed that some metabolites, such as lactic acid, were significantly affected by even short delays, while others remained stable for longer. The study introduced the concept of a "stability time point", marking when a metabolite's concentration changes by 20%. These predictive models were validated in a separate cohort. To apply these findings, the authors developed the PRIMA Panel, an open-source R Shiny tool. This tool allows researchers to assess the impact of preanalytical variations on their samples, predict metabolite stability, and generate performance reports. The PRIMA Panel was tested using samples from the Dresden Integrated Liquid Biobank, proving its utility in a real-world biobank setting. The study emphasizes the importance of tracking preanalytical factors to improve the reliability of metabolomics analyses. The PRIMA Panel is available online and for local deployment, providing a practical solution for quality control in metabolomics research. The results of the study underscore the importance of tracking preanalytical factors in biobanking. A versatile tool for assessing their impact on metabolic data is introduced, improving the reliability of future analyses.