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Multivariate Curve Resolution-Alternating Least Squares Analysis of High-Resolution Liquid Chromatography–Mass Spectrometry Data

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journal contribution
posted on 18.10.2016, 00:00 authored by Melanie M. Sinanian, Daniel W. Cook, Sarah C. Rutan, Dayanjan S. Wijesinghe
Methods such as liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) are crucial for differentiating compounds with highly similar masses. This is a necessity when analyzing highly complex samples; however, the size of high-resolution LC-HRMS data sets can cause difficulties when applying advanced data analysis techniques. In this work, LC-HRMS analyses of known amphetamine samples and unknown bacterial lipid samples were carried out, and multivariate curve resolution-alternating least squares (MCR-ALS) was applied to the data to obtain mathematical separation of overlapped analyte signals. In order to minimize computational strain, a novel strategy was developed which minimizes the number of irrelevant masses analyzed at full resolution. To do this, data were first binned to unit mass resolution, and MCR-ALS was performed. This provided mathematical components for each analyte present plus background components. In the resolved spectral profiles of analyte components, masses above a preset intensity threshold were extracted, discarding all other masses, and expanded to successively higher levels of resolution, applying MCR-ALS at each level. These steps were repeated until 0.001 amu resolution was achieved, as dictated by the resolution of the instrumentin this case, a time-of-flight mass spectrometer. This strategy allowed for the accurate recovery of all known amphetamine compounds and select bacterial lipid extracts while minimizing the size of the data, therefore minimizing computational analysis time and data storage requirements. This relatively simple strategy enables the effective coupling of LC-HRMS with MCR-ALS.