Baseline Correction Method Using an Orthogonal Basis for Gas Chromatography/Mass Spectrometry Data

Abstract

A baseline correction method that uses basis set projection to estimate spectral backgrounds has been developed and applied to gas chromatography/mass spectrometry (GC/MS) data. An orthogonal basis was constructed using singular value decomposition (SVD) for each GC/MS two-way data object from a set of baseline mass spectra. A novel aspect of this baseline correction method is the regularization parameter that prevents overfitting that may produce negative peaks in the corrected mass spectra or ion chromatograms. The number of components in the basis, the regularization parameter, and the mass spectral range from which the spectra were sampled to construct the basis were optimized so that the projected difference resolution (PDR) or signal-to-noise ratio (SNR) was maximized. PDR is a metric similar to chromatographic resolution that indicates the separation of classes in a multivariate data space. This new baseline correction method was evaluated with two synthetic data sets and a real GC/MS data set. The prediction accuracies obtained by using the fuzzy rule-building expert system (FuRES) and partial least-squares-discriminant analysis (PLS-DA) as classifiers were compared and validated through bootstrapped Latin partition (BLP) between data before and after baseline correction. The results indicate that baseline correction of the two-way GC/MS data using the proposed methods resulted in a significant increase in average PDR values and prediction accuracies.