bio-metabolomics-targeted-analysis

To back-calculate absolute metabolite concentrations from MRM peak area data, fit a calibration series with known concentrations using weighted regression, then apply internal standard normalization and dilution factors to derive final sample values.

Yes, you can build weighted calibration curves from Skyline export files for targeted metabolomics by fitting peak area or internal standard response data using 1/x or 1/x^2 weighting and computing R² for quality assessment.

To compute LOD and LOQ for targeted LC-MS/MS assays, derive method validation metrics from calibration residuals or blank samples, then determine the lower limits of detection and quantitation alongside accuracy, precision, and bias.

The best way to validate assay performance and flag QC failures in targeted metabolomics is to compute accuracy and precision across low, medium, and high QC levels, then automatically flag QC failures in the exported results table.

Yes, targeted metabolite quantification supports internal standard normalization and dilution factors to back-calculate sample concentrations from instrument export files, ensuring reliable and reproducible absolute quantitative results.