Least-Squares Fitting of Time-Domain Signals for Fourier Transform Mass Spectrometry

To advance Fourier transform mass spectrometry (FTMS)-based molecular structure analysis, corresponding development of the FTMS signal processing methods and instrumentation is required. Here, we demonstrate utility of a least-squares fitting (LSF) method for analysis of FTMS time-domain (transient) signals. We evaluate the LSF method in the analysis of single- and multiple-component experimental and simulated ion cyclotron resonance (ICR) and Orbitrap FTMS transient signals. Overall, the LSF method allows one to estimate the analytical limits of the conventional instrumentation and signal processing methods in FTMS. Particularly, LSF provides accurate information on initial phases of sinusoidal components in a given transient. For instance, the phase distribution obtained for a statistical set of experimental transients reveals the effect of the first data-point problem in FT-ICR MS. Additionally, LSF might be useful to improve the implementation of the absorption-mode FT spectral representation for FTMS applications. Finally, LSF can find utility in characterization and development of filter-diagonalization method (FDM) MS.

Fourier transform mass spectrometry at the uncertainty principle limit for improved qualitative and quantitative molecular analyses

Anton Kozhinov

Nowadays, among the instrumentation park of mass spectrometry, Fourier transform mass spectrometers (FTMS), including ion cyclotron resonance (ICR) and Orbitrap FTMS, provide the highest analytical performance for accurate measurements and mass resolution. Nevertheless, molecular analysis in currently challenging research areas, such as life and environmental sciences, necessitates further improvement of these analytical characteristics. In recent decades, a regular approach to address the problem behind the analytical performance was using increased electromagnetic fields. However, per a given increase of their magnitudes, that approach is currently requiring more and more resources, hence demonstrating its limited feasibility for tomorrow. Therefore, only a qualitative breakthrough in the underlying methodology may then lead to the next series of developments enabling improved molecular analysis. The present research is dedicated to the fundamental question behind the analytical performance in FTMS. Specifically, this thesis represents an interdisciplinary study aimed at improved molecular analysis in FTMS-based applications, achieved via better comprehension of the uncertainty principle in FTMS, viz. its dependence on the measurement scheme, including ion traps, signal processing, and data analysis, as well as its influence on achievable analytical characteristics in FTMS. To start, the uncertainty principle for measurements in FTMS has been investigated, asserting a limit to the precision with which complementary physical quantities in FTMS, e.g. detection period and scale of frequency details to resolve, can be measured. Specifically, two corollaries of the uncertainty principle are considered, viz. resolution performance and performance for accurate measurements in FTMS. Importantly, the uncertainty principle shows dependence on the particular measurement scheme employed. For instance, signal detection, signal processing, and data analysis of the standard measurement scheme impose their own restrictions to the resulting uncertainty principle, thus leading to the current limitations. However, the ultimate limitations in the analytical characteristics in question are defined by the uncertainty principle limit due to physics of ion motion in the mass analyzer. Hence, with corresponding developments in data analysis methods, methods for signal processing, and designs of ion traps, novel measurement schemes have been implemented, where the quantitative form of the uncertainty principle is modified towards the ultimate limit. Finally, the implemented solutions have been evaluated in the context of FTMS-based analysis of crude oil fractions, protein identification and characterization, quantitative proteomics, and analysis of isotopic fine structures of peptides. To conclude, the achieved success of this work should considerably contribute to the currently challenging analytical applications of FTMS.

EPFL2015

Distinguishing analyte from noise components in mass spectra of complex samples: where to cut the noise?

Luca Fornelli, Anton Kozhinov, Yury Tsybin, Konstantin Zhurov

Fourier transform mass spectrometry (FTMS) enables comprehensive analysis of complex molecular mixtures. Given the broad intensity ranges of components in the mass spectra, it is imperative to accurately determine a noise threshold level above which peak assignments will be made. Conventionally, to find the threshold level, the "N sigma" approach or an equivalent rule is used. However, the "N sigma" approach cannot be applied to mass spectra stored with partially removed noise (reduced-profile mode). It is also not directly applicable to mass spectra acquired in the absorption mode with removed negative spectral amplitudes. Moreover, N value selection is normally made based on a rule of thumb, meaning that the calculated threshold level may be biased. Here, we present a noise thresholding method which addresses these limitations for analysis of mass spectra of complex molecular mixtures. The introduced data-dependent thresholding method involves analysis of the distribution of logarithmic intensity of all peaks, including noise and analyte, for a given mass spectrum. Selected method applications include FTMS analysis of crude oil fractions as well as tandem MS analysis of intact proteins.

Amer Chemical Soc2014

Fourier transform mass spectrometry at the uncertainty principle limit for improved qualitative and quantitative molecular analyses

Anton Kozhinov

EPFL2015