NOISE-REDUCTION OF GAS-CHROMATOGRAPHY MASS-SPECTROMETRY DATA USING PRINCIPAL COMPONENT ANALYSIS

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. A new matrix is then reconstructed as the difference between the original and residual matrices, which is then rescaled and a new data file created. 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 due to improved integration. Linearity and precision of chromatographic data were also improved.