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For data analysis algorithms to take advantage of the higher accuracy of newer mass spectrometers, it is essential to remove systematic bias in mass measurement. Mass measurement error may originate from a variety of sources, e.g.: power supply voltage/temperature drift, space charge effects, temperature/humidity variation in the laboratory, vacuum system stability, etc.
(Gibbons et al., 2015) Correcting systematic bias and instrument measurement drift with mzRefinery. Bioinformatics.