All publications herein are incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Mass spectrometry addresses two key questions: (1) “what's in the sample?” and (2) “how much is there?”. Both questions are addressed in the instant application. Several of the embodiments described herein focus on the first question; that is, identification of the components in a mixture. Embodiments of the present invention relate to software that has demonstrated substantial improvements in mass accuracy, sensitivity and mass resolving power. Certain of these gains follow directly from estimation and modeling of ion resonances using a physical model described by Marshall and Comisarow. Other embodiments described herein focus upon applications of estimation and modeling of the phases of ion resonances. Such methods can be divided into functional groups: phase-based methods, calibration, adaptive data-collection strategies, and miscellaneous auxiliary functions.
The traditional approach to analysis of Fourier transform mass spectrometry (“FTMS”) spectra is bottom-up. Resonances are detected in the spectra, from which inferences are made about the composition of the analyzed sample. Most of the embodiments described herein involve approaches to bottom-up analysis. Key steps in bottom-up analysis of FTMS data are detection and estimation of ion resonances, mass calibration, and identification. Various embodiments of the present invention involve reducing the 4 MB of data representing an FTMS (MS-1) spectrum to a list of candidate elemental compositions for each detected peak with probabilities assigned to these identities and abundance estimates. The essential information represents a data reduction of roughly three orders of magnitude relative to the unprocessed spectrum. In the bottom-up approach to data analysis, peaks are detected and characterized by estimation first, and then knowledge about the sample is used to calibrate and identify the components. The ability to perform these calculations in real-time creates exciting possibilities for adaptive workflows that actively direct acquisition of optimally informative data.