Abstract
Principal component analysis has been evaluated as a digital filter to improve the overall quality of gas chromatography/mass spectrometry (GC/MS) data sets. Data are initially read into a matrix, scaled, and then processed by using the NI-PALS algorithm, which is used to separate signal from the matrix. By use of a six-component solvent mixture with samples of from 0.5 to 150 pg of each component, significant improvements in mass spectral quality and spectral matches were observed. Signal to noise was improved by a factor of from 2 to 100
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