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The magnitude of the error in quantification is highly dependent on the number of peptides available for quantification.
It must also be mentioned that the computational methods used in SMLM, i.e., the image processing and localization algorithms, can be another source of error in quantification (Deschout et al. 2014; Small and Stahlheber 2014).
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This can result in motion artifacts and errors in quantification.
This underestimation can be contributed to errors in quantification that originate from delineation of the liver volume.
In copolymers, interactions between LCB and SCB (short chain branching) have been described and lead to errors in quantification.
This may indicate that errors in quantification have a similar effect on stress and rest perfusion MBF values but are cancelled by calculation of the MFR.
During dynamic PET imaging, the introduction of gadolinium-based MR contrast agents (GBCA) at high concentrations during a dual injection of GBCA and PET radiotracer may cause increased attenuation effects of the PET signal, and thus errors in quantification of PET images.
These can lead to even larger errors in quantification.
Gouw et al. (2008) investigated the influence of N partial enrichment on the number of identifications and errors in quantification.
In addition, any errors in quantification due to variations in ionization and detection efficiency of modified versus unmodified peptide species cancel once protection factors are calculated.
Large errors in quantification will occur for small proteins as they generate too few tryptic peptides and therefore are underrepresented in a typical tryptic digest.
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