Noise reduction of fast, repetitive GC/MS measurements using principal component analysis (PCA) Original Research Article

Principal component analysis (PCA) was applied to the noise reduction of low ppb level benzene, toluene, ethyl benzene, xylene (BTEX) type gas chromatography/mass spectrometry (GC/MS) measurements (i.e. BTEX) with a fast, repetitive GC/MS system. The first three principal components (PCs) accounting for approximately 60–80% of the total variance in the original data could be attributed to chemical components, whilst the remaining PCs were found to be due to noise. Reconstruction of the data from the first three PCs resulted in noise reduction with improved signal fidelity. The results of PCA were comparable with those achieved by a Fourier transform method.