Analysis of Liquid Chromatography-Mass Spectrometry Data with an Elastic Net Multivariate Curve Resolution Strategy for Sparse Spectral Recovery

Analysis of liquid chromatography-mass spectrometry (LC-MS) data requires the differentiation between a small number of relevant chemical signals and a larger amount of noise. This is often done based, at least partially, on a threshold which assumes that low intensity m/z signals arise from the noise. This eliminates low-intensity fragments, isotopes, and adducts and may exclude relevant low-intensity compounds all together. This work describes the use of multivariate curve resolution alternating least-squares with an additional sparse regression step using elastic net (MCRENALS) to distinguish relevant m/z signals without the use of a harsh thresholding step, thus allowing for discovery of low intensity m/z signals corresponding to the analytes. This strategy is demonstrated first on a unit mass analysis of amphetamines in which relevant m/z signals are found at as low as a 0.1% intensity relative to the molecular m/z peak. The incorporation of MCR-ENALS into our previously reported data reduction strategy for analysis of high-resolution LC-MS is also demonstrated. Analysis based on only 0.3% of the original data set, while retaining low-intensity isotope peaks, was accomplished without the use of thresholding, allowing for the application of MCRENALS to the high-resolution LC-MS data.