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Mass spectrometry-based targeted proteomics employs heavy isotope-labeled proteins or peptides as standards to improve accuracy and precision. The input sample amount is often determined by the total quantity of endogenous proteins or peptides, as defined by spectrophotometric assays, before the heavy-isotope standards are spiked into the samples. Errors in spectrophotometric measurements, which may be due to low sensitivity or chemical or biological interference, have a direct impact on the quantitative mass spectrometry results. Currently used targeted proteomics workflows cannot identify or correct deviations that arise from differences in the input sample amount. We have developed a workflow, global extraction from parallel reaction monitoring (PRM), to identify and quantify thousands of background peptides that are inherently acquired by PRM experiments. These background peptides were used to identify differences in the input sample amount and to reduce this variance by intensity-based, post-acquisition normalization. This approach was then applied to a xenograft study to improve the quantification of human proteins in the presence of mouse tissue contamination. In addition, these background peptides also provided a direct source of quality control metrics related to sample handling and preparation.