Method for performing charge detection mass spectrometry with temporal resolution

Abstract

A charge-detection mass spectrometry process includes capturing a time-varying signal, representing a current induced by the oscillatory motion of an ion within a capture region at a detector; processing the time-varying signal to derive a frequency of the oscillatory motion; and generating, based on the amplitude of the time-varying signal and the derived frequency of the oscillatory motion, Selective Temporal Overview of Resonant Ion (STORI) data. Real -Values ​​over time and HISTORY Imag -Represent values ​​over time; regenerate the STORI data based on a variation of the frequency of the oscillation over time and determine a charge state of the ion based on the regenerated STORI data.

Description

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[0001] The present application claims priority over the preliminary US patent application No. 63 / 462,833 filed on April 28, 2023, which is hereby incorporated by reference in its entirety. BACKGROUND INFORMATION

[0002] Charge detection mass spectrometry (CDMS) is a technique used to determine the masses of individual ions by simultaneously measuring the mass-to-charge ratio (m / z) and the charge of each ion. Conventional techniques for performing CDMS include the use of Fourier transform mass spectrometry (FT-MS). In FT-MS, ions oscillate in an electrostatic field (e.g., in an orbital electrostatic trap mass analyzer such as an Orbitrap® analyzer), in a magnetic field (e.g., in an FT ion cyclotron resonance mass analyzer (FT-ICR mass analyzer)), or in a combination of both. A time-measured signal, called a transient, is subjected to a Fourier transform to obtain an FT spectrum. The vibrational frequency of an ion depends on its mass and therefore on the m / z of the ion.Thus, each spectral component (peak) in the FT spectrum corresponds to a specific vibrational frequency of a trapped ion, from which the m / z of the ion can be uniquely determined. The charge z of an ion can then be directly determined from the amplitude of the FT spectrum peak, based on the assumption that the induced current signal is proportional to the charge state of the ion. A simple CDMS approach would assume that the amplitude of the FT spectrum peak for a given frequency would be proportional to the charge of the ion.

[0003] However, the amplitude of the FT spectrum peak is not a reliable measure of the induced current signal, as the residence time (lifetime) of the ion in the analysis trap can be shorter than the signal acquisition period. An ion's lifetime can be shortened by a destructive collision with a molecule of residual gas or by other ion fragmentation mechanisms. Fragmentation either removes the ion from the totality of trapped ions or causes its vibrational frequency to change abruptly due to a mass change. In both cases, the induced current signal terminates at the original frequency. If a charged fragment with a different m / z remains trapped in the analyzer, a new FT component at a different frequency appears in the induced current signal at the same moment the original signal of the parent ion is lost.Although the new frequency differs from the original frequency, it may be indistinguishable from the original frequency in the FT spectrum. However, since the new signal component does not start at the beginning of the acquisition period, its peak height is also underestimated.

[0004] US patent application 2022 / 0246414A1, hereby incorporated by reference in its entirety, describes the conventional method "Selective Temporal Overview of Resonant Ion" (STORI), which can be used to estimate the actual lifetime of an ion within the signal acquisition period. Using an FT spectrum generated from a transient, the STORI method applies a special bandpass filter centered on a specific FT spectrum frequency (also referred to as the STORI frequency) and creates an incremental STORI signal as a function of time. The incremental STORI signal is typically a piecewise linear function of time that increases with a specific slope as the ion is trapped in the analysis and oscillates at a specific frequency.The slope of the STORI signal is proportional to the instantaneous value of the induced current at that frequency and therefore proportional to the charge state of the ion. If the ion current is absent for a given frequency, as after loss or fragmentation of the ion, the STORI signal remains constant (flat). Using the STORI method, the instantaneous amplitudes of the induced ion current can be estimated with temporal resolution, and thus the charge state of the ion can be accurately determined, regardless of whether the ion survives the entire detection period.

[0005] The conventional STORI method represents a significant improvement over previous techniques. However, two problems have arisen with the conventional STORI method. First, the conventional STORI method is sensitive to the choice of the STORI frequency on which the aforementioned integration bandpass filter is centered. The STORI frequency can deviate from the actual vibrational frequency of the ion. As a result, the incremental STORI signal is no longer linear, and its slope is underestimated. The STORI frequency can deviate from the actual vibrational frequency of the ion for several reasons. In some cases, the centroid of an FT spectrum peak is incorrectly determined, which often occurs in the presence of noise or a low signal-to-noise ratio. This can also occur even if the vibrational frequency of the ion remains constant over the acquisition period.In other cases, particularly with large molecules, the ion's vibrational frequency drifts over time due to changes in the ion's mass during the detection period. For example, desolvation of the ion (e.g., the detachment of water or solvent molecules from the ion) or the loss of other small neutral fragments from the ion in the vacuum environment leads to a change in the ion's mass, which in turn causes a change in the ion's vibrational frequency. This frequency drift renders the chosen STORI frequency meaningless.

[0006] The second problem is related to the fundamental issue of spectral uncertainty, which arises from the impossibility of having both frequency and temporal resolution simultaneously. The presence of closely spaced FT spectrum peaks, including those of fragmentation products, impairs the STORI integration band filter, resulting in an irregular, incremental STORI signal that is difficult to interpret in terms of a piecewise linear trend. Consequently, CDMS experiments may have low throughput and be inefficient, as only a few analytes of interest can be analyzed at the same time. SUMMARY

[0007] The following description is a simplified summary of one or more aspects of the procedures and systems described herein, intended to provide a basic understanding of these aspects. This summary does not constitute a comprehensive overview of all aspects considered, nor does it aim to identify the most important or crucial elements of any aspect, nor to define the scope of any single or all aspects. Its sole purpose is to present, in simplified form, some concepts of one or more aspects of the procedures and systems described herein, as an introduction to the more detailed description that follows.

[0008] In some illustrative examples, a non-volatile, computer-readable medium stores instructions which, when executed, direct at least one processor of a mass spectrometry computing device to perform a process comprising: capturing a time-varying signal representing a current induced by the oscillatory motion of an ion within a capture region at a detector; processing the time-varying signal to derive a frequency of the oscillatory motion; and generating Selective Temporal Overview of Resonant Ion (STORI) data according to equations (1) and (2), which define STORI Real -Values ​​over time and HISTORY Imag - Display values ​​over time: STORIReal(tn)=S(tn)∗cos(ω∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω∗tn)+STORIImag(tn−1) where S(t n) the amplitude of the time-varying signal and ω the derived frequency of the oscillation; regenerating the STORI data based on a variation of the frequency of the oscillation over time and determining a charge state of the ion based on the regenerated STORI data.

[0009] In some illustrative examples, a system for determining the charge state of an ion comprises: one or more processors and a memory containing executable instructions which, when executed by the one or more processor(s), cause a computing device to perform a process comprising: capturing a time-varying signal representing a current induced at a detector by an oscillatory motion of an ion within a capture region; processing the time-varying signal to derive a frequency of the oscillatory motion; generating according to equations (1) and (2).

[0010] In some illustrative examples, a system for performing charge-detection mass spectrometry comprises: an ion-trap mass analyzer that captures an ion within a capture region and establishes a capture field within the capture region that causes the ion to vibrate; and a computing system configured to perform a process comprising: acquiring a time-varying signal representing a current induced at a detector by a vibrational motion of an ion within a capture region; processing the time-varying signal to derive a frequency of the vibrational motion; and generating according to equations (1) and (2).

[0011] In some illustrative examples, a non-volatile, computer-readable medium stores instructions which, when executed, direct at least one processor of a mass spectrometry computing device to perform a process comprising: capturing a time-varying signal representing a current induced by the oscillatory motion of an ion within a capture region at a detector; processing the time-varying signal to derive a frequency of the oscillatory motion; and generating Selective Temporal Overview of Resonant Ion (STORI) data according to equation (8), which defines STORI Mag - Display values ​​over time: STORIMag(tn)=STORIReal(tn)2+STORIImag(tn)2+STORIMag(tn−1) where values ​​from STORI Real (t n ) and STORY Imag (t n ) currently t n can be determined according to equations (6) and (7): STORIReal(tn)=S(tn)∗cos(ω∗tn) STORIImag(tn)=−S(tn)∗sin(ω∗tn) where S(t n ) the amplitude of the time-varying signal and ω the derived frequency of the oscillatory motion; and determining a charge state of the ion based on the STORI data.

[0012] In some illustrative examples, a non-volatile, computer-readable medium stores instructions which, when executed, direct at least one processor of a mass spectrometry computing device to perform a process comprising: detecting a transient for one or more ion species that is / are trapped and oscillating within a capture region; generating a Fourier transform (FT) spectrum based on the transient; selecting a spectral interval within the FT spectrum that includes one or more FT components; estimating a frequency of each FT component within the spectral interval; and processing the FT spectrum to determine a time-resolved frequency for an FT component within the spectral interval based on an isolated contribution of the FT component to the spectral interval.and determining a charge state z of an ion species corresponding to the FT component, based on the time-resolved frequency for the FT component within the spectral interval. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings illustrate various embodiments and form part of the specification. The embodiments shown are merely examples and do not limit the scope of the disclosure. In all drawings, identical or similar reference numerals denote identical or similar elements. Fig. Figure 1A shows an illustrative STORI diagram for an ion that survives an entire detection period. Fig. Figure 1B shows an illustrative STORI diagram for an ion that does not survive an entire detection period. Fig. Figure 2A shows an illustrative FT spectrum for a sinusoidal signal with a fixed frequency and one peak. Fig. Figure 2B shows an illustrative STORI diagram, calculated using the correctly centroidized STORI frequency of Fig. 2A was generated. Fig. Figure 3A shows an illustrative FT spectrum for a sinusoidal signal with a fixed frequency and one peak. Fig. Figure 3B shows an illustrative STORI diagram, which uses the incorrectly centroidized STORI frequency of Fig. 3A was generated. Fig. Figure 4 shows an illustrative δSTORI diagram representing δSTORI data derived from the STORI data of Fig. 2B were obtained for an idealized case of a fixed frequency and a correct centroid. Fig. Figure 5 shows an illustrative δSTORI diagram representing δSTORI data derived from the STORI data of Fig. 3B was obtained in the case of an incorrectly centroided STORI frequency. Fig. Figure 6 shows an illustrative δSTORI phase diagram with a δSTORI phase curve representing the δSTORI phase angle over time during the transient. Fig. Figure 7 shows an illustrative procedure for carrying out a modified STORI procedure. Fig. Figure 8 shows an illustrative procedure for performing operation 708 of a procedure 700. Fig. Figure 9 shows an illustrative STORI diagram representing STORI data generated with an incorrectly centroided STORI frequency. Fig. Figure 10A shows an illustrative δSTORI diagram representing δSTORI data. Fig. Figure 10B shows an illustrative δSTORI phase diagram representing δSTORI phase data obtained using the data in Fig. 10A δSTORI data can be generated. Fig. Figure 11 shows an illustrative diagram with a frequency correction curve representing the time-variable estimated frequency correction. Fig. Figure 12 shows an illustrative STORI diagram representing the regenerated STORI data. Fig. Figure 13A shows an illustrative FT spectrum for a signal with a peak at 55 kHz. Fig. Figure 13B shows an illustrative STORI diagram representing STORI data obtained for the FT spectrum of Fig. Signal shown in 13A was generated. Fig. Figure 14A shows an illustrative δSTORI diagram representing δSTORI data. Fig. Figure 14B shows an illustrative δSTORI phase diagram representing δSTORI phase data obtained using the data in Fig. 14A δSTORI data can be generated. Fig. Figure 15A shows an illustrative diagram with a frequency correction curve representing the time-varying estimated frequency correction. Fig. Figure 15B shows an illustrative STORI diagram representing the regenerated STORI data. Fig. Figure 16 shows an illustrative method for determining the mass of an ion using the modified Stori method. Fig. Figure 17 shows an illustrative STORI diagram generated according to a frequency-insensitive STORI method. Fig. Figure 18 shows an illustrative method for determining a time-resolved frequency for FT components within an FT spectrum and for determining a charge state of ions represented by the FT components. Fig. Figure 19 shows an illustrative procedure for performing Operation 1810 of the procedure of Fig. 18. Fig. Figure 20A shows an example of a sequence of absolute values ​​of an amplitude of a model transient and a fitted piecewise constant amplitude function. Fig. Figure 20B shows the penalty function compared to trial values ​​of a series of holding points for the piecewise constant amplitude function of Fig. 20A. Fig. Figure 21 shows an illustrative diagram, including a curve representing an amplitude correction factor. Fig. Figure 22 shows an illustrative FT spectrum divided into a multitude of FT bins along the frequency domain, and a selected spectral interval with three FT components. Fig. Figure 23 shows the spectral interval and the superimposed filter functions for the FT components of the spectral interval for each of five different iterations of the procedure by Fig. 19. Fig. Figure 24 shows a spectral interval and a curve representing the amplitude values ​​over the transient acquisition period for the first FT component for each of the five different iterations. Fig. Figure 25 shows a spectral interval and a curve representing the amplitude values ​​over the transient acquisition period for the second FT component for each of the five different iterations. Fig. Figure 26 shows the spectral interval 2202 and a curve 2602 representing the amplitude values ​​over the transient acquisition period for the third FT component for each of the five different iterations. Fig. Figure 27A shows a spectral interval, the amplitude values, and the fitted piecewise constant amplitude functions obtained in the last iteration for the FT components of the spectral interval. Fig. 22 were received. Fig. Figure 27B shows the phase as a function of time and the fitted phase functions obtained in the last iteration for the FT components of the spectral interval of Fig. 22 were received. Fig. Figure 28 shows diagrams that show the frequency, corrected based on the time derivative of the respective phase functions, as a function of time, for the FT components of the spectral interval of Fig. 22. Fig. Figure 29 shows an illustrative implementation of the procedures of Fig. 18 and Fig. 19 using overlapping spectral intervals. Fig. Figure 30 shows an illustrative CDMS system. Fig. Figure 31 shows an illustrative computing device that can be specifically configured to perform one or more of the processes described herein. DETAILED DESCRIPTION

[0014] This document describes methods and systems for performing time-resolved CDMS. These methods and systems address the challenges of instantaneous amplitude and / or instantaneous frequencies of FT components with temporal resolution. The amplitudes are interpreted as the number of trapped charges determined over time during the signal acquisition period. Abrupt changes in amplitude are detected and interpreted as ion decay events (e.g., ion fragmentation or desolvation events). The instantaneous frequencies reflect the m / z of ions and their temporal evolution. Thus, the methods described here account for miscentroidal frequencies and / or shifting frequencies by determining a time-dependent frequency profile over the ion lifetime.In some examples, a modified STORI method generates a STORI signal using the time-varying STORI frequencies. In other examples, multiple FT components in an FT spectrum, which would otherwise interfere constructively or destructively and thus negatively affect the STORI methods, can be multiplexed and accurately analyzed by CMDS by reducing the influence of interfering components.

[0015] The methods described herein account for a non-constant m / z, which arises, for example, from solvent loss in a vacuum environment. Advantages include more detailed analytical information from mass spectra, improved amplitude and frequency precision, particularly in the case of fragmentation and mass loss of the ion during the acquisition period, and improved accuracy in estimating the charge state. Furthermore, the methods described herein can increase the throughput of a CDMS experiment and enhance the efficiency of CDMS by enabling the simultaneous analysis of multiple analytes.

[0016] The CDMS methods described herein can be performed using a system that includes an ion trap mass analyzer and a CDMS control system. The ion trap mass analyzer captures one or more ions within a capture region and establishes a capture field within the region, causing the ions to vibrate. In some examples, the ion trap mass analyzer is an orbital electrostatic ion trap mass analyzer that establishes a quadro-logarithmic capture field, such as an Orbitrap® mass analyzer (Thermo Fisher Scientific, Waltham, MA), as described in U.S. Patent Application Publication No. 2022 / 0246414A1.It is understood, however, that the methods described herein can be implemented in any ion trap analyzer or equivalent structure in which the trapped ions within a trapping region are set into oscillatory motion by the presence of an electrostatic trapping field (e.g., an electrostatic linear ion trap (ELIT) analyzer) or a magnetic trapping field (e.g., a Fourier transform ion cyclotron resonance (FT-ICR) analyzer), including ion traps in which the ions are not set into orbital motion. An example of a suitable non-orbital electrostatic trap is the Cassinian trap, described in Köster, “The Concept of Electrostatic Non-Orbital Harmonic Ion Trapping,” International Journal of Mass Spectrometry, Vol. 287, pp. 114–118 (2009), which is incorporated herein by reference.A CDMS control system is described in more detail below and includes software and / or hardware components configured to perform any of the operations described herein or to instruct another system, device, or facility to perform them.

[0017] To facilitate understanding of the principles of the modified STORI methods described, an overview of conventional CDMS techniques and the conventional STORI method is now given. Conventional CDMS techniques are based on the fundamental principle of the discrete Fourier transform (DFT), according to which a signal represented in the time domain (the transient) as a set of discrete pairs of time and value (signal intensity) can be represented in the frequency domain as a set of discrete pairs of frequency and complex number. The conventional DFT returns a single magnitude for each given frequency, representing how much signal buildup occurred at that frequency over the entire acquisition period. The value of the DFT is represented as a complex number at each frequency and can be obtained by calculating the correlation of the signal with a cosine wave (the real component) and a sine wave (the imaginary component) of the frequency.Conventional DFT returns a single magnitude value for each given frequency, representing the amount of signal buildup at that frequency over the entire transient. A simple CDMS approach would assume that the magnitude of the value returned by the DFT for a given frequency is proportional to the charge of the ion. Thus, an ion with twice the charge of another ion should induce a signal at the detection electrodes that is twice as strong as the signal induced by the other ion.

[0018] However, if an ion "dies" mid-transient, for example due to fragmentation, and no longer provides a signal, the single value returned by the DFT is lower than if the ion had survived the entire transient. Consequently, the single DFT value is insufficient to reliably determine the charge state. The conventional STORI method was developed to estimate the actual lifetime of the ion within the signal acquisition period and the instantaneous amplitudes of the induced ion current with temporal resolution.

[0019] In the conventional STORI method, the time-varying signal from the detector is processed to determine the m / z and the charge of the ion. The m / z is determined by applying an FFT to the time-varying signal to generate an FT spectrum, from which the frequency ω of the harmonic motion of the ion (the "STORI frequency") can be determined. The STORI frequency can be determined by centroiding the FT spectrum peak, such as by fitting a parabola or other curve to the points of the FT spectrum peak, or by performing a line search to optimize the maximum of the FT spectrum peak. The centroided STORI frequency ω can be correlated with m / z based on a known correlation. In some examples, the correlation between the STORI frequency ω and the m / z is represented by the following relationship: ω=km / z where k is a correlation parameter that can be predetermined and based on various factors, such as experimental conditions.

[0020] The determination of the ion charge z is based on the STORI signal over the lifetime of the ion during detection. The STORI signal at any given time is represented as a complex number and can be obtained by calculating the correlation of the time-varying signal (the transient) with a cosine wave (the real component) and a sine wave (the imaginary component) of the frequency. However, instead of calculating a value for the sum of the cosine and sine correlations over the entire length of the transient, as in DFT techniques, the conventional STORI method performs separate running counts for the cosine and sine correlations (real and imaginary components, respectively) during the transient. The magnitude of the complex number (the STORI signal, represented by STORI) Mag) is plotted at each point, and the slope of the time-variable STORI signal is used to determine the lifetime and charge state of the ion. Each point in a set of STORI data is the product of the discretized time-variable signal S(t). n ) (the transient) at time t n and either a cosine wave or a sine wave with the STORI frequency ω, summed with the previous STORI Real - or STORY Imag -value that was at the previous time t n-1 was obtained as expressed in equations (1) and (2) below, and each value of the STORI signal is obtained based on equation (3) below: STORIReal(tn)=S(tn)∗cos(ω∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω∗tn)+STORIImag(tn−1) STORIMag(tn)=STORIIReal(tn)2+STORIImag(tn)2 where S(t n ) the amplitude of the time-varying signal at time t nwhere ω is the Stori frequency of the oscillatory motion derived from the FT spectrum. In equations (1), (2) and (3), (t0, t1, ... t n ) represents a sequence of time points in a fixed interval of Δt such that ω ∗ Δt << 1.

[0021] The charge z of the ion is determined according to the measured slope of the STORI signal (STORI Mag (t n)) and correlation data that correlate the slope of the STORI signal with the charge of the ion are determined. In addition to determining the charge of the captured ion, the STORI signal can be used to identify and characterize ion decay events (e.g., when an ion species decays during the acquisition of the time-varying signal) and to identify and evaluate signals generated by two or more simultaneously captured ions. Accordingly, when determining the ion charge z, the slope of the STORI signal after an ion decay event can be neglected, or a transient during which an ion decays can be disregarded. Once the m / z and the charge z of an ion have been determined using these processing methods, the mass m of the ion can be easily calculated.

[0022] As mentioned previously, the conventional STORI method performs a continuous count of the sine and cosine correlations during the transient, instead of calculating a value for the sum of sine and cosine correlations over the entire transient duration, as in the conventional DFT method. Fig. 1A and Fig. Figure 1B shows STORI diagrams representing simulated STORI data for a frequency around 55 kHz, which is representative of a viral capsid. Fig. Figure 1A shows a STORI diagram 100A for an ion that survives the entire detection period. The STORI diagram 100A closes a curve 102A, which STORI Mag (the magnitude of the STORI signal or STORI value) as a function of time, a curve 104A, the STORI Real as a function of time (where the cosine correlation represents the real component), and a curve 106A, which STORI ImagThe curve represents the ion's behavior as a function of time (where the sinusoidal correlation represents the imaginary component). As curves 102A, 104A, and 106A show, the ion survives the entire detection period.

[0023] Fig. Figure 1B shows a STORI diagram 100B for an ion that does not survive the entire detection period. The STORI diagram 100B closes a curve 102B, which STORI Mag as a function of time, a curve 104B, the STORI Real represents as a function of time, and a curve 106B, which is STORI Imag as a function of time. The change in the slope of curves 102B, 104B and 106B at 600 ms indicates an ion decay event. In Fig. 1A and Fig. 1B, the slopes of curves 102A and 102B during the lifetime of the ion are proportional to the charge state of the ion, regardless of whether the ion survives the entire detection period.

[0024] The conventional STORI method assumes that the vibrational frequency of an ion is constant throughout the detection period and that the centroidal STORI frequency is correct (i.e., it matches the true frequency of the signal). This assumption is reasonable if the following conditions are met: (1) electric fields remain unchanged throughout the transient, (2) the ion trajectory is not significantly altered by any other forces (e.g., space charge repulsion), and (3) the m / z of the ion remains unchanged throughout the detection period (e.g., due to fragmentation, desolvation, etc.). In typical CDMS experiments, both conditions (1) and (2) are met, and for smaller analytes, condition (3) is also met. For larger ions (e.g.,However, in the case of viral capsids, condition (3) may not be met due to changes in the ion mass (and thus the m / z) during transient collection, such as those resulting from fragmentation and / or desolvation of the ion. Additionally, correctly determining the centroid of an FT spectrum peak is not always straightforward, especially in the presence of noise. A change in ion mass and / or an incorrectly centroided frequency peak can lead to errors in the reported STORI slopes (the slope of the STORI signal, e.g., the slope of curve 102A or 102B), resulting in an incorrect charge estimate for the ion.

[0025] Fig. Figure 2A shows an illustrative FT spectrum 200A for a sinusoidal signal with a fixed frequency and a peak 202. A correctly centroidal STORI frequency is indicated by the dashed line 204. Fig. Figure 2B shows an illustrative STORI diagram 200B, which uses the correctly centroidized STORI frequency of Fig. 2A was generated. The STORI diagram 200B closes a curve 206, which STORI Mag as a function of time, a curve 208, the STORI Real represents as a function of time, and a curve 210, which STORI Imag represents a function of time. As in Fig. As shown in Figure 2B, a conventional STORI calculation, which is represented by curve 206 (correlation to sine and cosine waves at the centroidal STORI frequency of Fig. As shown in Figure 2A), the signal builds up linearly over time. The real and imaginary parts (cosine and sine correlations, represented by curves 208 and 210, respectively) may have different slopes depending on the initial phase of the signal, but their slopes remain constant throughout the entire transient because the centroidal STORI frequency and the true frequency of the signal remain synchronous at all times.

[0026] Fig. Figure 3A shows an illustrative FT spectrum 300A for a sinusoidal signal with a fixed frequency and a peak 302. A miscentroidized STORI frequency is indicated by the dashed line 304. As in Fig. As shown in Figure 3A, the STORI frequency and the true frequency of the signal (indicated by an apex 306 of peak 302) are not identical (e.g. due to a poor centroid or a shift in the signal frequency during the transient). Fig. Figure 3B shows an illustrative STORI diagram 300B, which uses the incorrectly centroidized STORI frequency of Fig. 3A was generated. The STORI diagram 300B closes a curve 308, which STORI Mag as a function of time, a curve 310, the STORI Real as a function of time, a curve 312, the STORI Imag represents as a function of time, and a dashed ideal curve 314, the STORI Mag as a function of time for a correctly centroidized STORI frequency, as in Fig. As shown in Figure 3B, the sine and cosine correlations, represented by curves 310 and 312 respectively, do not build up the signal at the same rate throughout the transient. Instead, the STORI signal, represented by curve 308, begins to oscillate between the two correlations as the two frequencies go in and out of phase with each other. The mismatched frequencies result in a shallower slope of curve 308 compared to the ideal curve 314 and therefore an incorrect charge estimate.

[0027] As the magnitude of the centroid error increases, the resulting Stori slope decreases rapidly. In the example of Fig. 3A and Fig. 3B A centroid error of 4 ppm reduces the STORI slope by approximately 5%. The signal-to-noise ratio would have to be very low (<10) at this frequency to achieve a centroid deviation of 1 ppm, so a centroid error should be small for species with stable m / z in this frequency range. However, for species with unstable m / z, such as large molecules bound to solvent molecules, small centroid errors can lead to more than trivial slope errors.

[0028] The modified STORI method solves these problems of the conventional STORI method by using the STORI data (e.g., STORI Real (t n ) and STORY Imag (t n )) to determine a time-variable STORI frequency over the ion lifetime and regenerate the STORI signal (including STORI Mag (t n )) based on the time-variable STORI frequency, as will now be explained.

[0029] The rate at which the value of the STORI signal (STORI) Mag The rate at which the STORI signal switches between cosine and sine waves (real and imaginary components) depends on the discrepancy between the STORI frequency and the true frequency of the signal. Accordingly, the modified STORI method estimates a time-dependent frequency correction for the STORI frequency based on the rate at which the STORI signal changes between cosine and sine waves. The discrepancy between the STORI frequency and the true frequency of the signal is related to the time derivative of the STORI value, denoted as δSTORI. Since the value of the STORI signal is a complex number (sine and cosine correlations), its derivative (δSTORI) is also a complex number, where the real part indicates how quickly the STORI signal builds up in the cosine wave, and the imaginary part indicates how quickly the STORI signal builds up in the sine wave.

[0030] Fig. Figure 4 shows a δSTORI diagram 400, which represents δSTORI data derived from the STORI data of Fig. 2B for an idealized case of a fixed frequency and a correct centroid. The δSTORI diagram 400 closes a curve 402, which δSTORI Mag as a function of time, a curve 404, which represents δSTORI Real represents as a function of time, and a curve 406, which δSTORI Imag represents a function of time. As in Fig. As shown in Figure 4, the δSTORI values ​​(e.g., curves 402, 404, and 406) are all linear and constant. In contrast, Figure 4 shows that the δSTORI values ​​(e.g., curves 402, 404, and 406) are all linear and constant. Fig. 5 a δSTORI diagram 500, which represents δSTORI data derived from the STORI data of Fig. 3B for the case of an incorrectly centroided STORI frequency. The δSTORI diagram 500 closes a curve 502, which δSTORI Mag as a function of time, a curve 504, which represents δSTORI Realrepresents as a function of time, and a curve 406, which δSTORI Imag represents a function of time. As in Fig. As shown in Figure 5, the real and imaginary components of the δSTORI data (curves 504 and 506, respectively) are not linear and constant, but change with the progression of the transient, since the source of the STORI amplitude alternates between cosine and sine waves over time, because the STORI frequency and the true frequency of the signal do not coincide (see Figure 5). Fig. 3B).

[0031] The δSTORI phase angle (θ) δSTORI ) is a measure of how quickly a signal switches between sine and cosine waves, and is determined by the ratio of the imaginary and real values ​​of the δSTORI data (see Fig. 5) determined according to the following equation (4): θδSTORI(tn)=tan−1(δSTORIImag(tn)δSTORIReal(tn)) where θ δSTORI (t n ) the δSTORI phase angle is, δSTORI Imag (t n) the derivative of the imaginary component of the STORI data (STORI Imag (t n )) is and δSTORI Real (t n ) the derivation of the real component of the STORI data (STORI Real (t n )) is. Fig. Figure 6 shows a δSTORI phase diagram 600 with a δSTORI phase curve 602, which represents the δSTORI phase angle over time during the transient.

[0032] As can be seen, the δSTORI phase angle changes linearly over time due to a false centroid, provided both the STORI frequency and the true frequency of the signal remain unchanged. The slope of the time-varying δSTORI phase angle curve 602 allows for a direct measurement of the difference between the STORI frequency and the true frequency of the signal. In the example of Fig. 6. The centroid of the STORI frequency is mismatched at 0.27826 Hz above the true frequency of the signal. As a result, the phase of the STORI frequency gradually leads the true frequency of the signal at a rate of 1.75 rad / s, which is determined as follows: (0.278cyclesec)×(2πradcycle)=1.75radsec

[0033] In the example of Fig. 6. The slope of the δSTORI phase curve 602 is -1.75 rad / s, which agrees with the calculation. If the direction of the frequency centroid error is reversed, the slope of the δSTORI phase curve 602 takes the opposite sign. As in Fig. As shown in Figure 6, the slope of the δSTORI phase curve 602 at any point in the transient indicates a possible offset between the STORI frequency and the true frequency of the signal.

[0034] If the true frequency of the signal shifts during the transient (e.g., due to desolvation or other decay events) and is not fixed, as in the example of Fig. 6. The δSTORI phase curve is not linear. However, the slope of the δSTORI phase curve at any given time indicates the offset between the STORI frequency and the true frequency of the signal at that time. The modified STORI method provides this time-dependent frequency offset information to regenerate the STORI signal at any given time in order to correct any discrepancies between the STORI frequency and the true frequency of the signal, as explained in more detail below. The time-dependent frequency offset information can also be used (e.g., based on the relationship of ω to m / z described above) to determine the m / z of the ion at a specific time during the transient.

[0035] Fig. Figure 7 shows an illustrative procedure 700 for carrying out a modified STORI procedure. During Fig. Seven illustrative operations according to one embodiment are shown; other embodiments can be any of those shown. Fig. Omit, add, rearrange and / or modify the 7 operations shown.

[0036] Operation 702 captures a time-varying signal representing a current induced by the oscillatory motion of an ion within a capture region at a detector. In some examples, the capture region is that of an orbital electrostatic ion trap mass analyzer (e.g., an Orbitrap™ mass analyzer).

[0037] In Operation 704, the time-varying signal is processed to derive a frequency of the ion's oscillation. For example, a fast Fourier transform can be performed on the time-varying signal to generate an FT spectrum with a peak representing the ion's oscillation. Based on the FT spectrum, a centroidal frequency of the ion's oscillation (the derived frequency) is determined. The centroidal frequency can be derived in any suitable way, such as by fitting a parabola or other curve to the points of the FT spectrum peak, or by performing a line search to optimize the peak's maximum.

[0038] Operation 706 uses STORI data, which is part of STORI Real -Values ​​over time and HISTORY Imag-values ​​over time, according to equations (1) and (2) above, using the centroidal frequency of the ion's oscillation as the generated STORI frequency ω. In some examples, the STORI data also represent STORI Mag -values ​​over time, generated according to equation (3). As explained above, the STORI data are determined based on the time-varying signal and the centroidal frequency for the FT spectrum peak.

[0039] Operation 708 will use STORI data, which is part of STORI Real -Values ​​over time, STORY Imag -Values ​​over time and HISTORY Mag -Values ​​displayed over time, based on a variation in the frequency of the oscillatory motion regenerated over time. Operation 708 is described in more detail below.

[0040] In Operation 710, the charge z of the ion is determined based on the regenerated STORI data (e.g., based on the STORI signal). The charge z can be determined based on the slope of the STORI signal, as described above.

[0041] Operation 708 will now be described. Fig. Figure 8 shows an illustrative procedure 800 for performing operation 708 of procedure 700 (e.g., regeneration of STORI data based on a variation in the frequency of the oscillatory motion over time). During Fig. Eight illustrative operations according to one embodiment are shown; other embodiments can be any of those shown. Fig. Omit, add, rearrange and / or modify the 8 operations shown.

[0042] In Operation 802, δSTORI data are generated based on STORI data. The δSTORI data are obtained by taking the time derivative of the data obtained in Operation 706 ( Fig. 7) generated STORI data. Since the STORI data includes both a real component (STORI Real (t n )) as well as an imaginary component (STORI Imag (t n )) include, the δSTORI data includes both a real component (δSTORI Real (t n )) as well as an imaginary component (δSTORI Imag (t n )) one. In some examples, a smooth curve is fitted to the STORI data using a suitable regression, such as linear regression or second-order or higher-order polynomial regression, and the derivative is taken from the smoothed curve at each time point.

[0043] In Operation 804, δSTORI phase data are generated based on the δSTORI data. As explained above, the δSTORI phase data are determined based on the ratio of the imaginary and real values ​​of the δSTORI data at each time point during the transient according to equation (4).

[0044] In Operation 806, a frequency correction relative to the centroidal STORI frequency is determined based on the δSTORI phase data. The frequency correction is an adjustment to the centroidal STORI frequency (derived in Operation 704) at each time point during the transient. The frequency correction can be determined in any suitable way. In some examples, a smooth δSTORI phase curve is fitted to the δSTORI phase data using an appropriate regression, such as second-order or higher-order polynomial regression. For each time point, a slope of the δSTORI phase curve is determined, such as by taking the derivative of the δSTORI phase curve at that time point. The slope of the δSTORI phase curve at any given time represents the frequency correction to be applied to the centroidized STORI frequency at that time when the STORI data are regenerated at Operation 708.

[0045] During operation 808, the STORI data (e.g., STORI) is processed. Real (t n ), STORY Imag (t n ) and STORY Mag (t n )) regenerated using the STORI frequency correction determined in Operation 806. The STORI data are regenerated according to modified equations (1') and (2') shown below and the preceding equation (3): STORIReal(tn)=S(tn)∗cos(ω(tn)∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω(tn)∗tn)+STORIImag(tn−1) where the STORI frequency ω(t n ) for each point in time t n During the transient, the centroidal STORI frequency previously derived in Operation 704 is adjusted using the STORI frequency correction determined in Operation 806 for each time point during the transient. For example, the time-dependent STORI frequency ω(t n ) can be given by the following equation (5): ω(tn)=ωCentroid+ωCorrection(tn) where ω Zentroid the centroidal STORI frequency is derived from the FT spectrum in Operation 704, and ω Korrektur (t n ) the estimated frequency correction at time t n is determined as in Operation 806. In equations (1'), (2') and (5) (t0, t1, ... t n ) represents a sequence of time points in a fixed interval of Δt such that ω ∗ Δt « 1.

[0046] In some examples, procedures 700 and 800 are performed in real time while a transient progresses. In other examples, procedures 700 and 800 are performed after acquisition (e.g., after the transient has been acquired). Several illustrative examples of performing the modified STORI procedure will now be described.

[0047] In a first example, the modified STORI method is used to correct a centroid error with a fixed (unchanging) frequency. In this example, a transient has a signal-to-noise ratio of approximately 60, and the centroidized STORI frequency has an error of +3 ppm. Fig. Figure 9 shows an illustrative STORI diagram 900, which represents STORI data that were generated (e.g., in Operation 706) with the incorrectly centroidized STORI frequency. STORI diagram 900 closes a curve 902, which STORI Mag as a function of time, a curve 904, the STORI Real as a function of time, a curve 906, the STORI Imag represents as a function of time, and a dashed ideal curve 908, the STORI Mag as a function of time for a correctly centroidized STORI frequency, as in Fig. As shown in Figure 9, the sine and cosine correlations represented by curves 904 and 906, respectively, do not build up the signal at the same rate throughout the transient, indicating a frequency mismatch. Thus, curve 902 exhibits a slope error of -2.1% compared to the ideal curve 908 for a theoretical transient without centroid frequency error.

[0048] The derivation of the STORI data from Fig. 9 is used to generate δSTORI data. Fig. Figure 10A shows an illustrative δSTORI diagram 1000A, which represents δSTORI data that can be generated (e.g., in operation 802). The δSTORI diagram 1000A closes a curve 1002, which represents δSTORI Mag -data over time, a curve 1004, the δSTORI Real -data over time, and a curve 1006, which represents δSTORI Imag-represents data over time. Although not shown, the δSTORI data may include a smooth curve that fits the data represented by curves 1002, 1004, and 1006. As in Fig. As shown in Figure 10A, the real and imaginary components of the δSTORI data (curves 1004 and 1006, respectively) are not constant but change with the progression of the transient, since the source of the STORI amplitude alternates between cosine and sine waves over time, because the STORI frequency and the true frequency of the signal do not coincide (see Figure 10A). Fig. 9).

[0049] Fig. Figure 10B shows an illustrative δSTORI phase diagram 1000B, which represents δSTORI phase data obtained (e.g., during Operation 804) using the data in Fig. The δSTORI data shown in Figure 10A can be generated. The δSTORI phase diagram 1000B includes curve 1008, which represents the δSTORI phase data over time, and a smooth δSTORI phase curve 1010, which is fitted to the δSTORI phase data. As shown, the δSTORI phase data exhibit a consistent downward slope.

[0050] An estimated frequency correction as a function of time relative to the incorrectly centroidized STORI frequency is generated (e.g., in Operation 806) based on the δSTORI phase data (e.g., based on the fitted δSTORI phase curve 1008). As mentioned, the estimated frequency correction at any given time is the time derivative of the δSTORI phase data (e.g., the δSTORI phase curve 1010), which is in Fig. 10B are shown, and can be plotted as a function of time. Fig. Figure 11 shows an illustrative diagram 1100 with a frequency correction curve 1102, which represents the time-variable estimated frequency correction.

[0051] The STORI data are processed (e.g., in operation 708) according to the above equations (1'), (2') and (3) using the time-variable frequency correction of Fig. 11 regenerated. Fig. Figure 12 shows an illustrative STORI diagram 1200, which represents the regenerated STORI data. The STORI diagram 1200 closes a curve 1202, which STORI Mag depicted over time, a curve 1204, the STORI Real depicts the course of time, and a curve 1206, which STORI Imag over time, a. The slope of the regenerated STORI data from Fig. Curve 12 (e.g., curve 1202) exhibits an error of only 0.6% compared to the -2.1% slope error of curve 902 in the original STORI data. Thus, the slope accuracy, and consequently the ion charge estimation, is improved by compensating for the frequency centroid error.

[0052] In a second example, the modified STORI method is used to correct the frequency shift during the transient. As mentioned earlier, the frequency of an ion can shift during the transient, for example, due to desolvation or loss of neutral fragments. In this example, a captured transient has a signal-to-noise ratio of approximately 60 and a frequency jump of +27 ppm 300 ms into the transient (to simulate a sudden m / z change). It is important to note that in this simulated example, the signal maintains its continuity at the transition because the z-position of the ion in the mass analyzer would not shift instantaneously during a frequency change. Fig. Figure 13A shows an illustrative FT spectrum 1300A for a signal with a peak 1302 near 55 kHz. The FT spectrum peak 1302 has a front 1304 because the signal has a lower frequency in the first 300 ms of the transient before the frequency shift. Fig. Figure 13B shows an illustrative STORI diagram 1300B, which represents STORI data generated for the signal shown in the FT spectrum 1300A. The STORI diagram 1300B closes a curve 1306, which STORI Mag depicted over time, a curve 1308, the STORI Real depicted over time, a curve 1310, the STORI Imag depicts over time, and a dashed ideal curve 1312, the STORI MagThe curve represents the behavior of a signal with a fixed frequency (without shift) as a function of time. As shown, the real and imaginary components (curves 1308 and 1310, respectively) are curved, indicating a frequency mismatch. The STORI signal, represented by curve 1306, exhibits significant curvature and a slope error of -20.3% relative to the ideal curve 1312 for a theoretical transient without frequency shift.

[0053] The derivation of the STORI data from Fig. 13B is used to generate δSTORI data. Fig. Figure 14A shows an illustrative δSTORI diagram 1400A, which represents δSTORI data that can be generated (e.g., during operation 802). The δSTORI diagram 1400A closes a curve 1402, which represents δSTORI Mag -data over time, a curve 1404, the δSTORI Real -data over time, and a curve 1406, which represents δSTORI Mag-represents data over time. Although not shown, the δSTORI data may include a smooth curve fitted to the data represented by curves 1402, 1404, and 1406 for further processing of δSTORI data. As in Fig. As shown in Figure 14A, the real and imaginary components of the δSTORI data (curves 1404 and 1406, respectively) exhibit a significant change in slope due to a shift in the signal frequency at approximately 300 ms.

[0054] Fig. Figure 14B shows an illustrative δSTORI phase diagram 1400B, which represents δSTORI phase data obtained (e.g., during Operation 804) using the data in Fig. 14A shows that δSTORI data can be generated. The δSTORI phase diagram 1400B includes a curve 1408, which represents δSTORI phase data, and a smooth δSTORI phase curve 1410, which is fitted to the δSTORI phase data. As in Fig. As shown in Figure 14B, the δSTORI phase data exhibit two apparent linear sections that transition at approximately 300 ms. The signs and magnitudes of their slopes indicate that the centroidal STORI frequency is higher than the signal frequency (negative slope) in the range of 0 to 300 ms and lower than the signal frequency (positive slope) in the range of 300 to 1000 ms, and that the magnitude of the frequency error is greater (steeper slope) in the range of 0 to 300 ms than in the range of 300 to 1000 ms (flatter slope).

[0055] An estimated frequency correction as a function of time relative to the centroidized STORI frequency is generated (e.g., in Operation 806) based on the δSTORI phase data (e.g., based on the fitted δSTORI phase curve 1410). As mentioned, the estimated frequency correction at any given time is the time derivative of the δSTORI phase data (e.g., the δSTORI phase curve 1410) and can be plotted as a function of time. Fig. 15A shows an illustrative diagram 1500A with a frequency correction curve 1502, which represents a time-variable estimated frequency correction.

[0056] The STORI data are processed (e.g., in operation 708) according to the above equations (1'), (2') and (3) using the time-variable frequency correction of Fig. 15A regenerated. Fig. Figure 15B shows an illustrative STORI diagram 1500B, which represents the regenerated STORI data. The STORI diagram 1500B closes a curve 1504, which STORI Mag depicted over time, a curve 1506, the STORI Real depicts the course of time, and a curve 1508, which STORI Imag over time, a. The slope of the regenerated STORI data from Fig. 15B (e.g., curve 1504) exhibits an error of only -1.0%, in contrast to the -20.3% slope error of curve 1306 in the original STORI data. Thus, the slope accuracy, and consequently the ion charge estimation, is fundamentally improved by compensating for the frequency shift.

[0057] In the modified STORI method, the phase build-up of the shifted signal can be tracked during the transient and continuously compared with the phase that the frequency of the current time would accumulate over the same period. If a separation exists between these two phase build-ups, the phase of the reference sine and cosine waves could be adjusted accordingly before calculating the sine and cosine correlations to prevent phase discontinuities that would negate improvements that could be achieved through frequency correction.

[0058] It is understood that the modified STORI procedure can be performed to correct frequency mismatches even if multiple frequency shifts occur during the transient. Furthermore, the modified STORI procedure can be performed if both a centroid frequency error and one or more frequency shifts occur during the transient.

[0059] Fig. Figure 16 shows an illustrative method 1600 for determining the mass of an ion using the modified Stori method. During Fig. 16 illustrative operations according to one embodiment are shown; other embodiments can be any of those shown in Fig. Omit, add, rearrange and / or modify the 16 operations shown.

[0060] Operation 1602 captures a transient for an ion oscillating within a capture region. The transient is time-domain data representing a time-varying signal generated by a current induced at a detector by the ion's oscillation within the capture region.

[0061] In Operation 1604, a Fourier transform spectrum (FT spectrum) is generated based on the transient. The FT spectrum can be generated in any suitable way, such as by performing a fast Fourier transform of the transient to convert the time-domain data into frequency-domain data. The FT spectrum consists of frequency-domain data and has a peak that indicates the frequency of the ion's oscillation within the capture region.

[0062] In Operation 1606, a STORI frequency is determined based on the FT spectrum. The STORI frequency is the estimated frequency of the ion's oscillatory motion within the capture region. The STORI frequency can be determined in any suitable way. In some examples, the STORI frequency is determined by centrifuging the peak in the FT spectrum, such as by fitting a parabola or other curve to the points of the FT spectrum peaks, or by performing a line search to optimize the maximum of the FT spectrum peak.

[0063] In Operation 1608, STORI data for an FT spectrum peak are generated based on the transient and the STORI frequency determined in Operation 1606. The STORI data are generated according to equations (1), (2), and (3) above. The STORI data represent the cosine correlations (STORI Real (t n )), sinusoidal correlations (STORI Imag (t n)) and optionally the magnitude of the complex number (STORI Mag (t n )) dar.

[0064] In Operation 1610, a time-dependent frequency correction is determined based on the STORI data. The time-dependent frequency correction is determined as described herein (e.g., as described in Procedure 800).

[0065] In Operation 1612, the STORI data are regenerated based on frequency correction to correct any discrepancies between the STORI frequency determined in Operation 1606 and the true frequency of the signal during the transient. The STORI data are regenerated as described above (e.g., as in Operation 708) according to equations (1'), (2'), and (3) using a time-variable STORI frequency ω(t). n ) regenerates, which is determined during Operation 1606 based on the previously determined STORI frequency and frequency correction.

[0066] In Operation 1614, the charge z of the ion is determined based on the regenerated STORI data. For example, as described above, the charge z of the ion is determined based on a correlation of the slope of the regenerated STORI signal (STORI Mag (t n )) during the lifetime of the ion, the charge state is determined.

[0067] In operation 1616, the m / z of the ion is determined based on a corrected STORI frequency, which is based on a correlation between frequency and m / z. If the true frequency of the signal does not shift, but the centroidal STORI frequency is incorrect, the frequency correction should be constant (see, e.g., Fig. 6), and therefore the m / z should be constant throughout the transient. However, if the true frequency of the signal shifts during the transient (e.g., due to a decay event), the m / z for the ion also varies with time. Accordingly, the m / z of the ion at any given time can be determined based on the time-varying Stori frequency ω(t). n ) are determined.

[0068] In operation 1618, the mass m of the ion is determined based on the m / z and the charge z of the ion. If the m / z of the ion is time-varying, then the mass m of the ion is also time-varying, and the mass m can be determined for any point in time during the transient.

[0069] In the modified STORI method, separate running counts for the cosine and sine correlations (real and imaginary components, respectively) are kept as the transient progresses. These separate running counts are referred to as STORI. Realand STORY Imag represented. The magnitude of the complex number (the value of the STORI signal (STORI) Mag The magnitude is then plotted at each point, and the slope of the time-varying magnitude is used to determine the lifetime and charge state of the ion. As previously explained, with this approach, the running magnitude count can be distorted if the actual signal alternates between cosine and sine waves (e.g., due to frequency mismatches, as in relation to...). Fig. 3B shown and described).

[0070] In an alternative STORI method for determining the charge state z of an ion, also referred to herein as the "frequency-insensitive" STORI method, a continuous count of the STORI magnitude is maintained (in contrast to separate continuous counts of the sine and cosine correlations in the modified STORI method), while the real and imaginary STORI values ​​are discarded at each time point. The STORI magnitude (STORI Mag The STORI magnitude for each time point is obtained based on the sine and cosine correlations for that time, and the obtained STORI magnitude is added to the running STORI magnitude count. The STORI magnitude is determined according to the following equations (6), (7), and (8): STORIReal(tn)=S(tn)∗cos(ω∗tn) STORIImag(tn)=−S(tn)∗sin(ω∗tn) STORIMag(tn)=STORIReal(tn)2+STORIImag(tn)2+STORIMag(tn−1)

[0071] In equations (6), (7) and (8) (t0, t1, ... t n) represents a sequence of time points in a fixed interval of Δt such that ω ∗ Δt << 1.

[0072] Fig. Figure 17 shows an illustrative STORI diagram 1700, generated according to the frequency-insensitive STORI method. STORI diagram 1700 represents simulated STORI data for a frequency around 55 kHz. STORI diagram 1700 closes a curve 1702, which STORI Mag The curve represents the behavior as a function of time. The slope of curve 1702 is proportional to the charge state of the ion. Similar to the frequency correction shown by the modified STORI method, the frequency-insensitive STORI method is more tolerant of discrepancies between the STORI frequency and the true frequency of the signal than the conventional STORI method. However, the frequency-insensitive STORI method can also accumulate noise more quickly than the modified STORI method.

[0073] In the examples described above, the modified STORI method and the frequency-insensitive STORI method are used to determine the m / z, charge z, and mass m of a single ion oscillating within a capture region of a mass analyzer. However, the modified STORI method and the frequency-insensitive STORI method can also be used to determine the m / z, charge z, and mass m of each ion in a population of ions that are simultaneously trapped and oscillating within a capture region of a mass analyzer. In such examples, the modified STORI method or the frequency-insensitive STORI method is performed for each peak in the FT spectrum peak generated from the transient.

[0074] In some situations where multiple ions are analyzed for mass simultaneously, interference between closely adjacent signals in the FT spectrum leads to STORI plots with a step-like pattern that is difficult to interpret with a linear trend. Therefore, accurately determining the charge of the ions based on the STORI plots can be challenging. These interfering signals can be addressed in several ways. In some examples, all signals where adjacent signals within the FT spectrum are closer than a certain threshold (e.g., 100 Hz, 50 Hz, 25 Hz, etc.) are filtered out and not used in the modified STORI procedure.

[0075] In other examples, the signal of each individual FT peak within an FT spectrum interval is obtained by calculating each individual FT peak signal and subtracting the influence of all other FT peaks from the signal of the FT spectrum interval. This method determines time-resolved amplitudes and time-resolved vibrational frequencies from complex FT spectra generated by several different ion species simultaneously trapped within a capture region. The time-resolved amplitude of the signal corresponding to each ion is proportional to the charge state of the ion at each time point, and the time-resolved frequency correlates with the m / z of the ion at each time point. An illustrative implementation of this method is now described with respect to Fig. 18 described.

[0076] Fig. Figure 18 shows an illustrative method 1800 for determining a time-resolved frequency for FT components within an FT spectrum and for determining a charge state of ions represented by the FT components. Fig. The 18 exemplary operations shown according to one embodiment are all of which can be found in other embodiments. Fig. Omit, add, rearrange and / or modify the 18 operations shown.

[0077] Operation 1802 captures a transient for one or more ion species that are simultaneously trapped and oscillating within a capture region. The transient consists of time-domain data (signal S). n), which represent a time-varying signal generated by currents induced at a detector by the oscillatory motion of the ion population within the capture region. In some examples, the capture region is a capture region of an orbital electrostatic ion trap mass analyzer (e.g., an Orbitrap™ mass analyzer).

[0078] Operation 1804 generates a Fourier transform spectrum (FT spectrum) based on the transient. For example, a complex-valued FT spectrum C is generated. n generated by performing a fast Fourier transform (FFT) on the measured transient without apodization or zero-padding.

[0079] In Operation 1806, a spectral interval of the FT spectrum is selected for processing, where the selected spectral interval includes one or more FT components (e.g., peaks). The spectral interval of the FT spectrum is divided into a plurality of bins (FT bins) of fixed width. In some examples, the width of each FT bin is the inverse of the acquisition time for the transient. The selected spectral interval includes K FT bins and can be expressed by the expression c k = (C n0, ... , C n0+K} can be represented. In some examples, the selected spectral interval K=32 to K=1024 encloses FT bins, although any other suitable number can be used. Preferably, K is a power of two to facilitate FFT operations performed later in the procedure. The spectral interval is small relative to the full width of the FT spectrum to simplify processing. However, the spectral interval can be large or encompass the full width of the FT spectrum. The FT components enclosed in the spectral interval are local maxima of |C n | above the noise.

[0080] In Operation 1808, the frequency of each of the FT components within the spectral interval is estimated. The frequency can be estimated in any suitable way. In some examples, the frequency of each FT component is estimated as the maximum of the FT spectrum amplitude. Exact centroidation is not required, as it suffices to use an FT bin with the maximum amplitude |c k | to find for each FT component, which corresponds to a frequency accuracy down to one FT bin. Assume P peaks with estimated centroids f are used. (p) (p = 1..P) found, where the frequencies are defined in FT bins. Integer values ​​of f (p) would correspond to frequencies for which the discrete Fourier transform is defined.

[0081] Operation 1810 processes the FT spectrum to determine a time-resolved frequency for one or more FT components within the spectral interval based on an isolated contribution of that one or more FT components to the spectral interval. An isolated contribution of that one or more FT components excludes the contribution of all other FT components (or all other FT components with a signal level greater than a threshold) within the spectral interval. An illustrative procedure for performing Operation 1810 is given below with reference to Fig. 19 described in more detail.

[0082] In Operation 1812, a charge state z of one or more ion species corresponding to one or more FT components is determined based on the time-resolved frequency of one or more FT components within the spectral interval. The charge state can be determined in any suitable way. In some examples, the charge state is determined based on conventional STORI methods or the modified STORI concepts described above. For example, STORI data can be obtained according to equations (1'), (2'), and (3) using the time-resolved frequency determined in Operation 1810 as the time-resolved STORI frequency ω(t). n ) can be generated. Further methods for determining the charge state z based on the time-resolved frequency are described in more detail below.

[0083] Operation 1814 determines whether the processing of Procedure 1800 is complete. In some examples, the processing of Procedure 1800 is complete when the entire FT spectrum, or a region of interest within the FT spectrum, has been processed in Operations 1806 through 1812. If the processing is determined to be complete, Procedure 1800 terminates. If the processing is determined to be incomplete, the process returns to Operation 1806 to select the next spectral interval of the FT spectrum for processing. The next spectral interval may have the same or a different spectral width. In some examples, spectral intervals that do not include any FT components above a threshold level (such as a noise level or other minimum level) are not analyzed.

[0084] Fig. Figure 19 shows an illustrative procedure from 1900 for performing the operation from 1810. During Fig. 19 illustrative operations according to one embodiment show that other embodiments can be any of those shown in Fig. Omit, add, rearrange and / or modify the 19 operations shown.

[0085] In Operation 1902, for each FT component p that is included in the spectral interval, a value is calculated based on the estimated frequency f. (p) A centroidal filter (e.g., a bell function filter) is applied as follows: ck(p):=ck×ρ(k−f(p),w) where the parameter w is the width of the filter. For example, a bell function can be a Gaussian function, given by the following expression: ρk(w)=exp{−(k−f(p))22w2}

[0086] The filter width w is chosen to be sufficiently narrow to suppress FT components other than the selected FT components. In some optional examples, each FT component is assigned an individual filter width w. (p) assigned, and the initial value of the filter width w (p) is selected based on a minimum spectral distance to the nearest neighboring FT components.

[0087] In Operation 1904, a model transient is generated for each filtered FT component. The model transient for each FT component includes an amplitude function and a phase function for the FT component. The model transients are obtained by performing an inverse Fourier transform (iFT), such as an inverse fast Fourier transform (iFFT), on the filtered spectral data. ck(p) generated for each FT component, shifting the center frequency to the corresponding estimated centroid: st(p)=iFFT{ck(p)}×e2πi(n0−f(p))tK where the discrete time index t ranges from zero to K - 1. Optionally, the filtered spectra are ck(p) Q is padded with zeros, resulting in interpolation on a denser time grid t = 0..QK - 1.

[0088] Sequences of absolute values ​​of |st(p)| are generated from the iFT. Piecewise constant amplitude functions. a(p)(t)≈|st(p)| are fitted to the sequences of absolute values ​​for each FT component. The piecewise constant amplitudes a (p) are provisionally replaced by the absolute values ​​of the iFT data a(p)≈|st(p)| defined, since the phases are still unknown. The piecewise constant amplitude functions a (p)(t) model the evolution of signal intensities for each FT component during the acquisition period. An illustrative fitting procedure for the amplitude functions is described in more detail below.

[0089] A phase function φ p (t) is fitted for each FT component. Preferably, the phase function is fitted in the form of a piecewise polynomial whose holding points are the same as those in Operation 1904 for the piecewise constant amplitude function a. (p) (t) specific holding points. An illustrative fitting procedure for the phase functions is described in more detail below. Optionally, the piecewise constant amplitude functions are used as a(p)(t)≈Re{st(p)exp(−iφp(t))} using a projection of st(p) recalculated based on the estimated phases. In contrast to the original definition of a (p) through the absolute values ​​of st(p) This correction eliminates any positive distortion associated with noise.

[0090] Operation 1906 makes a spectral contribution dk(p) each FT component to the spectral interval c k determined. In some examples, the spectral contributions are determined by applying the direct FFT to the model transients (obtained in Operation 1904) using the amplitudes and phases determined in Operation 1904.

[0091] In Operation 1908, for each FT component, the spectral contributions of the other FT components within the spectral interval are calculated as follows from c k subtracted: ck(p)=ck−∑q≠pdk(p)

[0092] The spectra obtained through this subtraction operation are referred to herein as corrected spectra.

[0093] Operation 1910 determines whether the processing of procedure 1900 is complete. If the processing of procedure 1900 is not complete, procedure 1900 continues with operation 1912 and then returns to operation 1902 to perform another iteration loop from operations 1902 to 1910. In operation 1912, the width w of the bell function is increased. If the processing of the procedure is complete, procedure 1900 continues with operation 1914. An iteration loop comprises the successive execution of operations 1902 to 1912 until, at operation 1910, it is determined that the processing is complete (e.g., that an iteration loop condition is satisfied). However, operations 1910 and 1912 are optional and can be omitted in some examples, such as when the spectral interval includes only an FT component.

[0094] The 1900 procedure can be determined to be complete based on the fulfillment of a condition (e.g., an iteration loop condition). In some examples, the condition includes performing a threshold number of iteration loops. In some examples, the number of iteration loops is between 1 and 20, with gradually increasing filter widths w. In other examples, the processing of the 1900 procedure continues until the width w of the bell curve reaches a threshold value. In still other examples, the processing of the 1900 procedure continues until the temporal resolution satisfies a condition (e.g., reaches a threshold value). The temporal resolution of each iteration is constrained by the value δt ~ 1 / w according to the principle of spectral uncertainty. In the first iteration, the filter width is chosen to be sufficiently small to avoid interference between adjacent FT components.Preferably, w is smaller than the minimum distance between f. (p) and f (p+1) .

[0095] Therefore, the temporal resolution of the filtered iFFT transients is st(p) limited. In subsequent iterations, the filter width w is incrementally increased so that the filter bands can overlap and include other FT components. Nevertheless, peak interference remains suppressed due to the subtraction of spectra from other FT components evaluated in earlier iterations.

[0096] In the event that the spectral interval has only one FT component, namely P=1, only a single execution of operations 1902 to 1908 can be used with a sufficiently wide filter function or without filtering, and therefore operations 1910 and 1912 can be omitted.

[0097] In Operation 1914, a time-resolved frequency for one or more FT components within the spectral interval is determined based on the phase function of the model transients for the one or more FT components. As mentioned previously, the model amplitudes for FT components are expressed as piecewise constant amplitude functions of time a. p (t) evaluated, and the model phases φ p (t) are determined on each interval of the piecewise constant amplitude functions. Breakpoints between the constant values ​​of a pThe t values ​​are interpreted as the moments in time when an ion enters or leaves the ensemble of captured ions. The signal amplitudes between adjacent holding points are interpreted as the number of elementary charges present in a particular FT component at a specific interval of the signal acquisition time. For example, the signal amplitude may be proportional to, or indicate, the ion's charge state based on a known or predefined amplitude-charge state relationship. An intensity at the noise level is interpreted as the absence of an ion at a given time interval, such as when the ion has fragmented.

[0098] At a low signal-to-noise ratio, the absolute values ​​of the iFT transients are st(p) The estimated signal amplitudes may be somewhat exaggerated in the presence of noise, and a correction procedure, as described in more detail below, can be applied to remove the distortion. The projection of st(p) Signal amplitudes calculated on an estimated phase are free from noise-induced distortions, and the correction procedure can be omitted.

[0099] The model phases φ p (t) are the source of information about time-resolved oscillation frequencies, which differ from the originally estimated frequencies f (p) They may deviate. The time-resolved frequency F (p) The frequency at time t for an FT component is determined based on a time-resolved frequency correction (which is based on a time derivative of the phase function) applied to the estimated frequency of each FT component as follows: F(p)(t)=f(p)+12πdφpdt

[0100] To account for frequency drift, the phases φ are p (T) fitted with smooth nonlinear functions, e.g. piecewise polynomials of second or higher order.

[0101] As mentioned above, in some examples of Operation 1812, the charge state z of an ion species is determined based on the conventional STORI method or the modified STORI method described above, using the time-resolved frequency obtained in Operation 1912. In other examples, the charge state of an ion species is determined based on the corresponding piecewise constant amplitude function a. p (t) determined. As explained above, the signal amplitudes of a piecewise constant amplitude function a p(t) between adjacent holding points for an FT component are proportional or otherwise related (e.g. based on a known or empirically determined relationship) to the charge state of the ion species represented by the particular FT component at a particular segment of the signal acquisition time.

[0102] Now, an illustrative procedure for fitting a piecewise constant amplitude function to a sequence of real data points will be presented. a[t]=|st(p)|, The procedure described in Operation 1904 is based on the estimation of holding points h. i , which provide a minimum for the sum of variances in the intervals between them. The penalty function is calculated as R(h1,…hI)=∑i=1I+1(hi−hi−1)DISP(a[hi−1]…a[hi−1]) where the first interval always starts at h0 = 0, the last interval at h I+1= K ends. With the calculated cumulative sum A[h]=∑k=0h−1a[k] The penal function will become R=∑k=0K−1a[k]2−∑i=1I+1(A[hi]−A[hi−1])2hi−hi−1

[0103] Since the first sum is constant, the stopping points h1 < h2 < ... < h I searched in such a way that they maximize the second sum.

[0104] For a fixed set of breakpoints, the amplitudes of the intervals are evaluated as a¯i=a(hi−1≤t≤hi)=A[hi]−A[hi−1]hi−hi−1

[0105] The iterative search for h i It starts at a holding point (i=1). The number of holding points is then increased by one until one of two conditions is met: a) at least two breakpoints are closer together than an expected time resolution at a current value of the filter width (for Gaussian filter functions with parameter w, the condition can look like h i+1 - h i< K / 2w) or b) the average amplitudes of two adjacent intervals differ less than the noise level |a̅ i-1 - a̅ i | < Noise.

[0106] Fig. 20A and Fig. Figure 20B illustrates this method for fitting a piecewise constant amplitude function. Fig. Figure 20A shows an example of a sequence of absolute values ​​of an amplitude of a model transient and a fitted piecewise constant amplitude function, represented as line 2002. Fig. Figure 20B shows the penalty function versus trial values ​​of h1 and h2, represented as curves 2004 and 2006 respectively. The penalty function exhibits a marked minimum at the correct holding points.

[0107] An illustrative procedure for the piecewise polynomial approximation of the phase function, as referenced in Operation 1904, is now described. The problem is to determine the complex phase of a sequence s tin an interval = h i .. h i+1 between two stopping points on a polynomial function φ(t)=c0+c1tK+c2t2K2+⋯ adapting is formulated as minimizing the punitive function R(c1,c2…)=∑t|st|−|∑tste−i(c1tK+c2t2K2+⋯)| With respect to the coefficients c1, c2, .... Any suitable known global optimization method can be applied. The zero coefficient is then found as follows: c0=arg∑tst e−i(c1tK+c2t2K2+⋯)

[0108] The following describes an illustrative method for correcting signal amplitudes in noisy signals. A true signal amplitude a* is considered, which is defined for a time interval t = h. i .. h i+1 applies. The signal points found by the adjustment procedure a t = |a* + ζ k + iη k | are the absolute values ​​of a* plus a random noise component whose dispersion σ 2 = 2 < ζ2 >= 2 < η 2 > is. Assuming normally distributed white noise, the determined mean of a is t a=1πσ2∬(a*+ζ)2+η2 exp(−ζ2+η2σ2)dζdη=a=g(a*σ)a*

[0109] The true amplitude is related to the measured amplitude as a*=g(snr)a,snr=a* / σ where g is a correction factor: g=π snr×{∬(snr+x)2+y2e−x2−y2dxdy}−1

[0110] Fig. Figure 21 shows an illustrative diagram 2100 with a curve 2102 that represents the correction factor g as a function of snr.

[0111] Methods 1800 and 1900 can be used to determine a time-resolved frequency for one or more FT components within a selected spectral interval and / or within the FT spectrum, or for all FT components within the selected spectral interval and / or within the FT spectrum. Likewise, m / z, charge state z, and mass m can be determined based on methods 1800 and 1900 for any one or more arbitrary ion species that are simultaneously trapped and oscillating in the ion trap mass analyzer. Accordingly, the methods described herein can improve throughput compared to conventional CDMS techniques by enabling accurate analysis of multiple ion species simultaneously.

[0112] An illustrative application of the procedures from 1800 and 1900 will now be given in relation to Fig. Figures 22 to 28 describe a simulated analysis of Flock House Virus (FHV) particles using an Orbitrap™ mass analyzer. A transient is captured and processed by the mass analyzer to generate an FT spectrum.

[0113] Fig. Figure 22 shows an FT spectrum 2200, which is subdivided along the frequency domain into a multitude of FT bins. A selected spectral interval 2202 of the FT spectrum 2200, with a width of 128 FT bins, is magnified and contains three FT components 2204-1, 2204-2, and 2204-3 (together FT components 2204). The amplitudes of the FT components 2204 are significantly above the noise level 2206. The FT spectrum 2200 is displayed in magnitude mode as the absolute values ​​|c k | shown. Nevertheless, the complex values ​​Re (c k ) + i Im (c k ) used for all calculations.

[0114] Fig. Figure 23 shows the spectral interval 2202 and superimposed filter functions 2302-1, 2302-2, and 2302-3 (shown as dashed lines) for the FT components 2204 for each of five different iterations 2300 of the 1900 procedure (e.g., iterations 2300-1 to 2300-5). (For clarity, reference symbols are shown only in the fourth iteration, 2300-4.) The width w of the filter function for each successive iteration 2300 is 3, 6, 10, 15, and 20 FT bins. As shown, the filter functions 2302 are Gaussian bell curves that are sufficiently narrow and do not overlap significantly.

[0115] Fig. Figure 24 shows the spectral interval 2202 and a curve 2402, which shows the amplitude values |st(p)| This represents the transient detection for the FT component 2204-1, calculated at operation 1904 for each iteration 2300. Double zero-padding is applied, so the time parameter t ranges from zero to 256. The fitted piecewise constant amplitude function a (p) (t) is represented as a dashed line 2404 superimposed on the spectral interval 2202.

[0116] Fig. Figure 25 shows the spectral interval 2202 and a curve 2502, which shows the amplitude values |st(p)| The transient detection for the FT component 2204-2, calculated at operation 1904 for each iteration 2300, is represented. Double zero-padding is applied, so the time parameter t ranges from zero to 256. The fitted piecewise constant function a (p) (t) is represented as a dashed line 2504 superimposed on the spectral interval 2202.

[0117] Fig. Figure 26 shows the spectral interval 2202 and a curve 2602, which shows the amplitude values |st(p)| The transient detection for the FT component 2204-3, calculated at operation 1904 for each iteration 2300, is represented. Double zero-padding is applied, so the time parameter t ranges from zero to 256. The fitted piecewise constant function a (p) (t) is represented as a dashed line 2604 superimposed on the spectral interval 2202.

[0118] Fig. Figure 27A shows the spectral interval 2202, the constant amplitude functions 2404, 2504, and 2604, and the fitted piecewise constant amplitude functions 2404, 2504, and 2604 for the FT components 2204-1, 2204-2, and 2204-3, obtained in the last iteration (e.g., iteration 2300-5). The amplitude of the FT component 2204-1 drops from about 2.7 units to the noise level approximately in the middle of the acquisition time interval, while the amplitude of the FT component 2204-2 increases at the same instant. This behavior suggests that the FT components 2204-1 and 2204-2 originate from the same ion, which collides with a residual gas molecule and loses a neutral fragment in the process. The mass loss increases the vibrational frequency. The amplitude of the FT component 2204-3 remains constant throughout the entire detection period, indicating that the mass of the ion also remains constant.The signal amplitudes are presumably proportional to the charges of the ions involved. The near equality of the amplitudes of the FT components 2202-1 and 2202-2 (approximately 2.7 units) confirms the hypothesis that the FT component 2204-1 is generated by the same ion as the FT component 2204-2 after the loss of a neutral fragment.

[0119] Fig. Figure 27B shows the phase as a function of time and the fitted phase functions for the FT components 2204-1, 2204-2, and 2204-3, which were obtained in the last iteration (e.g., iteration 2300-5). The vibration phase array st(p) The parabolic functions, shown as dashed lines 2702-1, 2702-2, and 2702-3, are well fitted. The FT components 2204-1 and 2204-2 are only fitted in the high-amplitude intervals.

[0120] The time derivatives of the fitted phase functions are used to correct the estimated oscillation frequencies, as described above. Fig. Figure 28 shows diagrams 2800-1 and 2800-2, which depict the frequency as a function of time, corrected based on the time derivative of the respective phase functions. Diagram 2800-1 includes curve 2802-1, representing the time-resolved frequency for FT component 2204-1, and curve 2802-2, representing the time-resolved frequency for FT component 2202-2. Diagram 2800-2 includes curve 2802-3, representing the time-resolved frequency for FT component 2204-3. For all three FT components 2204, the frequency increases with time, presumably due to a continuous mass loss through desolvation. As curves 2802-1 and 2802-2 show, there is a rapid frequency jump between the FT components 2202-1 and 2202-2, presumably caused by the loss of a larger neutral fragment.

[0121] As can be seen from the example just described with regard to the FHV particles, the relative peak amplitudes in the original FT spectrum are 2200 (see Fig. 22) due to spectral interference by neighboring FT components, not representative of the relative signals generated by the vibration of the ions. Therefore, the peak amplitudes of the original FT spectrum 2200 are not suitable for assigning the ion charge state. However, the actual instantaneous amplitudes of the FT components, and thus the determination of the ion charge state, can be accurately determined using the methods described herein. For example, the ion charge state can be determined based on the modified STORI method using the time-resolved frequency, as shown by curves 2802-1, 2802-2, and 2802-3 of Fig. 28. Alternatively, the ion charge state can be determined based on the actual instantaneous amplitudes represented by the constant amplitude functions 2404, 2504 and 2604, as shown in Fig. 27A shown.

[0122] The procedures described herein are also applicable to dense spectra that may not be effectively partitioned into non-interfering spectral intervals. In these scenarios, Operation 1806 can select multiple spectral intervals such that each FT component of interest belongs to at least one interval and its estimated centroid is close to the center of the interval (e.g., within a predefined percentage or distance from the center frequency of the interval). In some examples, the spectral intervals overlap along the frequency axis.

[0123] In Operation 1906, the spectral contribution dk(p) Each of the FT components is calculated within its corresponding spectral interval. In the case of overlapping spectral intervals, the numbers are dk(p) The changes are transferred to the overlapping intervals via corresponding index shifts. In operation 1908, the spectral contributions of FT components in the adjacent overlapping intervals are also subtracted.

[0124] Fig. Figure 29 shows an illustrative implementation of methods 1800 and 1900 using overlapping spectral intervals. Fig. Figure 29 shows a continuous FT spectrum 2900 with four detected FT components 2902-1, 2902-2, 2902-3, and 2902-4. Each FT component 2902 is assigned an individual spectral interval 2904 (e.g., spectral interval 2904-1, 2904-2, 2904-3, or 2904-4) positioned in or near the center of the spectral interval 2904. Gaussian filter functions applied to the FT components 2902 are represented by dashed curves 2906-1 to 2906-4. The width of the Gaussian filter functions varies depending on the iteration. In the example of Fig. In 29, only a filtered model transient of the FT component 2902 of the corresponding spectral interval 2904 is calculated, and its spectral contribution d kThe spectral contribution of an FT component 2902 is estimated for the corresponding spectral interval 2904. This contribution is calculated on the associated spectral interval 2904 and subtracted, with appropriate index shifts, from the shared portions of the spectral data on overlapping spectral intervals 2904. Fig. Figure 29 shows a correction spectrum 2908 for the spectral interval 2904-1, which is calculated by subtracting the contributions of the FT components 2902-2 and 2902-3 (of the overlapping spectral intervals 2904-2 and 2904-3) from the contribution of the FT component 2902-1.

[0125] The spectral intervals that do not overlap, such as spectral intervals 2904-1 and 2904-4, do not exhibit direct interference. Therefore, the number of subtractions, which is twice the number of overlapping pairs, is proportional to P rather than P. 2 This has a positive effect on performance and enables efficient parallel computing.

[0126] Several additional advantages can be derived from the examples and principles described herein, as will now be described.

[0127] In some cases, ion fragmentation pathways can be determined. For example, if a pair of FT spectrum components is identified, the first of which terminates at a specific moment in the signal acquisition period and the second of which begins at essentially the same moment, and both components have the same or similar amplitudes, it is possible that the components were generated by the same captured ion that has undergone fragmentation. In this case, the m / z difference between the FT components (determined from their centroids) provides information about the lost fragment, whether it is neutral or charged, and therefore the fragmentation pathway can be determined (e.g., solvent loss or collision-induced fragmentation).

[0128] In another example, the frequency drift rate can provide information about the molecular conformation of trapped ions. Since the vibrational frequency is directly related to the m / z of a trapped ion, the temporal frequency change detected by the methods described herein provides an estimate of the ion's mass change over time. Mass reduction occurs either through solvent evaporation or collision-induced loss of small fragments (usually neutral). Ions with a larger external surface area are hypothesized to be more susceptible to both mass-loss mechanisms. Thus, ion conformations with smaller or larger surface areas can be distinguished based on the frequency drift rate.Furthermore, measuring the frequency drift under two or more different residual gas pressures can enable differentiation of the mechanism of mass losses, such as evaporation or collision-induced fragmentation.

[0129] As another example, frequency drift can also be determined using a simple method by short-time Fourier transform (or time-time Fourier transform), where the transient is divided into a number of shorter transients (parts), e.g., by recursive bisection. For smaller parts, the centroid and amplitude of an FT peak are determined, e.g., by fitting a sinc model to the corresponding FT spectra. The division of a transient into smaller parts continues until the sinc model exhibits a certain degree of fit, indicated, for example, by the smallness of a residual. The frequency / amplitude expansion is then obtained by piecewise interpolation of the values ​​determined from the transient parts. An example of a short-time Fourier transform that can be used to determine frequency / amplitude with time resolution is described in Miller, Zachary M., et al.“Apodization specific fitting for improved resolution, charge measurement, and data analysis speed in charge detection mass spectrometry”, Journal of the American Society for Mass Spectrometry 33.11 (2022): 2129 to 2137, which is incorporated herein by reference in its entirety.

[0130] One or more of the operations described herein, including any operations of procedures 700, 800, 1600, 1800 and 1900, can be performed by a CDMS system. Fig. Figure 30 shows an illustrative CDMS System 3000 (“System 3000”). System 3000 can be implemented wholly or partially by a mass spectrometer (e.g., by a mass spectrometer controller), including all mass spectrometers described herein. Alternatively, System 3000 can be implemented separately from a mass spectrometer (e.g., as a standalone computing system, as a remote computing system, or as a remote server communicatively coupled to a mass spectrometer via a network connection).

[0131] The System 3000 can, without restriction, include a storage device 3002 and a processing device 3004, which are selectively and communicatively coupled. Devices 3002 and 3004 can each include or be implemented by hardware and / or software components (e.g., processors, memory, communication interfaces, instructions stored in memory for execution by the processors, etc.). In some examples, devices 3002 and 3004 can be distributed across multiple devices and / or multiple locations, depending on the requirements of a particular implementation.

[0132] The storage device 3002 can maintain (e.g., store) executable data that can be used by the processing device 3004 to perform any of the operations described herein. For example, the storage device 3002 can store instructions 3006 that can be executed by the processing device 3004 to perform any of the operations described herein. The instructions 3006 can be implemented by any suitable application, software, code, and / or other executable data instance. The storage device 3002 can also maintain all data captured, received, generated, managed, used, and / or transmitted by the processing device 3004.

[0133] The processing unit 3004 can be configured to perform various processing operations described herein (for example, executing the instructions 3006 stored in the storage unit 3002 for execution). It should be noted that the operations and examples described herein are provided only to illustrate the many different types of operations that can be performed by the processing unit 3004. In the description contained herein, all references to operations performed by the system 3000 may be understood as being performed by the processing unit 3004 of the system 3000. Furthermore, the description contained herein may be understood to mean that all operations performed by the system 3000 include the system 3000 instructing or directing another system or device to perform the operations.

[0134] In certain embodiments, one or more of the systems, components, and / or processes described herein may be implemented and / or executed by one or more suitably configured computing devices. For this purpose, one or more of the systems and / or components described above may include or be implemented by computer hardware and / or computer-implemented instructions (e.g., software) embodied on at least one non-volatile, computer-readable medium configured to execute one or more of the processes described herein. In particular, system components may be implemented on one physical computing device or on more than one physical computing device. Accordingly, system components may include any number of computing devices and employ any number of computer operating systems.

[0135] In certain embodiments, one or more of the processes described herein may be implemented, at least partially, as instructions embodied in a non-volatile, computer-readable medium and executable by one or more computing devices. Generally, a processor (e.g., a microprocessor) receives instructions from a non-volatile, computer-readable medium (e.g., memory, etc.) and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions may be stored and / or transmitted using various known computer-readable media.

[0136] A computer-readable medium (also called a processor-readable medium) includes any non-volatile medium involved in providing data (e.g., instructions) that can be read by a computer (e.g., by a computer's processor). Such a medium can take many forms, including, but not limited to, non-volatile and / or volatile media. Non-volatile media can include, for example, optical or magnetic storage media and other permanent storage devices. Volatile media can include, for example, dynamic random-access memory (DRAM), which typically represents main memory.Common forms of computer-readable media include, for example, floppy disks, hard disks, magnetic tapes and other magnetic media, CD-ROMs (Compact Discs) and DVDs (Digital Video Discs), other optical media, RAMs (Random Access Memory), PROMs (Programmable Read Memory), EPROMs (Electrically Erasable Programmable Read Memory), FLASH EEPROMs, other memory chips or cartridges, and other tangible media that a computer can read.

[0137] Fig. Figure 31 shows an illustrative computing device 3100, which can be specifically configured to perform one or more of the processes described herein. As in Fig. As shown in Figure 31, the computing device 3100 can include a communication interface 3102, a processor 3104, a storage device 3106, and an input / output module (“I / O” module) 3108, which are communicatively connected to each other via a communication infrastructure 3110. While in Fig. 31 an illustrative calculating device 3100 is shown, are those in Fig. The components illustrated in Figure 31 are not to be considered limiting. Additional or alternative components may be used in other embodiments. Fig. The computing device 3100 shown in Figure 31 will now be described in more detail. The communication interface 3102 can be configured to communicate with one or more computing devices. Examples of the communication interface 3102 include, without limitation, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, an audio / video connection, and any other suitable interface.

[0138] The processor 3104 generally represents any type or form of processing unit capable of processing data and / or interpreting, executing, and / or instructing the execution of one or more of the instructions, processes, and / or operations described herein. The processor 3104 can perform operations by executing computer-executable instructions 3112 (e.g., an application, software, code, and / or other executable data instance) stored in the storage device 3106.

[0139] The storage device 3106 may include one or more data storage media, devices, or configurations and may employ any type, shape, and combination of data storage media and / or devices. For example, the storage device 3106 may include, but is not limited to, any combination of the non-volatile and / or volatile media described herein. Electronic data, including data described herein, may be stored temporarily and / or permanently in the storage device 3106. For example, data representative of the computer-executable instructions 3112, which are configured to instruct the processor 3104 to perform any of the operations described herein, may be stored in the storage device 3106.In some examples, data may be arranged in one or more database(s) located in storage device 3106.

[0140] The 3108 I / O module can include one or more I / O modules configured to receive user input and provide user output. One or more I / O modules can be used to receive input for a single virtual experience. The 3108 I / O module can include any hardware, firmware, software, or combination thereof that supports input and output functionality. For example, the 3108 I / O module can include hardware and / or software for capturing user input, including, but not limited to, a keyboard or keypad, a touchscreen component (such as a touchscreen display), a receiver (such as an RF or infrared receiver), motion sensors, and / or one or more input buttons.

[0141] The I / O module 3108 can include, but is not limited to, one or more devices for presenting output to a user, including a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more speakers, and one or more audio drivers. In certain embodiments, the I / O module 3108 is configured to provide graphical data to a display for presentation to a user. The graphical data can be representative of one or more graphical user interfaces and / or any other graphical content that may serve a particular implementation.

[0142] In some examples, any of the systems, computing devices, and / or other components described herein can be implemented by the computing device 3100. For example, the storage device 3002 can be implemented by the storage device 3106, and the processing device 3004 can be implemented by the processor 3104.

[0143] The preceding description described various illustrative embodiments with reference to the accompanying drawings. However, various modifications and changes can be made to these, and additional embodiments can be implemented without deviating from the scope of protection of the invention as set forth in the following claims. For example, certain features of one embodiment described herein can be combined or replaced with features of another embodiment described herein. Accordingly, the description and the drawings should be considered illustrative rather than limiting.

[0144] The advantages and features of the present disclosure can be further described by the following examples: Example 1. A non-volatile, computer-readable medium storing instructions which, when executed, instruct at least one processor of a mass spectrometry computing device to perform a process comprising: capturing a time-varying signal representing a current induced by an oscillatory motion of an ion within a capture region at a detector; processing the time-varying signal to derive a frequency of the oscillatory motion; generating Selective Temporal Overview of Resonant Ion (STORI) data according to equations (1) and (2), which define STORI Real -Values ​​over time and HISTORY Imag - Display values ​​over time: STORIReal(tn)=S(tn)∗cos(ω∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω∗tn)+STORIImag(tn−1) where S(t n) the amplitude of the time-varying signal and ω the derived frequency of the oscillation; regenerating the STORI data based on a variation of the frequency of the oscillation over time and determining a charge state of the ion based on the regenerated STORI data. Example 2. The non-volatile, computer-readable medium of Example 1, wherein regenerating the STORI data includes: determining a frequency correction as a function of time relative to the derived frequency and regenerating the STORI data based on the derived frequency and the frequency correction. Example 3. The non-volatile, computer-readable medium of Example 2, wherein the STORI data are regenerated according to equations (1') and (2'): STORIReal(tn)=S(tn)∗cos(ω(tn)∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω(tn)∗tn)+STORIImag(tn−1) where ω(t n) is a frequency of the oscillatory motion as a function of time, which is determined based on the derived frequency and the frequency correction as a function of time. Example 4. The non-volatile, computer-readable medium of Example 2 or 3, wherein the process further comprises, based on the derived frequency and frequency correction, determining the m / z of the ion at a given time during the acquisition of the time-variable signal. Example 5. The non-volatile computer-readable medium of Example 4, wherein the process further includes determining a mass of the ion at a given time based on the charge state of the ion and the m / z of the ion at a given time. Example 6. The non-volatile, computer-readable medium of one of Examples 2 to 5, wherein determining the frequency correction as a function of time comprises: generating δSTORI data based on the STORI data, wherein the δSTORI data represent a time derivative of the STORI data; generating, based on the δSTORI data and according to equation (4), δSTORI phase data representing a variation of a δSTORI phase angle θδSTORI over time: θδSTORI(tn)=tan−1(δSTORIImag(tn)δSTORIReal(tn)) where δ STORI Real (t n ) the temporal derivative of STORI Real (t n ) is and δ STORI Imag (t n ) the temporal derivative of STORI Imag (t n ) is; and determining the frequency correction as a function of time based on a slope of θδSTORI(t) n ) depending on time. Example 7. The non-volatile, computer-readable medium of one of Examples 1 to 6, wherein determining the charge state of the ion based on the regenerated STORI data includes: Determining STORI Mag -values ​​over time based on the regenerated STORI data according to equation (3): STORIMag(tn)=STORIIReal(tn)2+STORIImag(tn)2 and determining the charge state of the ion based on a slope of STORI Mag (t n ) and a relationship between ion charge and the slope of STORI Mag (t n ). Example 8. The non-volatile, computer-readable medium of Example 7, wherein: the process further comprises determining a lifetime of the ion based on the regenerated STORI data and determining the charge state of the ion on the slope of STORI Mag (t n ) during the lifetime of the ion. Example 9. A system for determining the charge state of an ion, comprising: one or more processor(s) and a memory containing executable instructions which, when executed by the one or more processor(s), cause a computing device to perform a process comprising: capturing a time-varying signal representing a current induced at a detector by an oscillatory motion of an ion within a capture region; processing the time-varying signal to derive a frequency of the oscillatory motion; generating Selective Temporal Overview of Resonant Ion (STORI) data according to equations (1) and (2), which define STORI Real -Values ​​over time and HISTORY Imag - Display values ​​over time: STORIReal(tn)=S(tn)∗cos(ω∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω∗tn)+STORIImag(tn−1) where S(t n) the amplitude of the time-varying signal and ω the derived frequency of the oscillation; regenerating the STORI data based on a variation of the frequency of the oscillation over time and determining a charge state of the ion based on the regenerated STORI data. Example 10. The system of Example 9, where regenerating the STORI data includes: determining a frequency correction as a function of time relative to the derived frequency and regenerating the STORI data based on the derived frequency and the frequency correction. Example 11. The system from Example 10, where the STORI data are regenerated according to equations (1') and (2'): STORIReal(tn)=S(tn)∗cos(ω(tn)∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω(tn)∗tn)+STORIImag(tn−1) where ω(t n) is a frequency of the oscillatory motion as a function of time, which is determined based on the derived frequency and the frequency correction as a function of time. Example 12. The system of Example 10 or 11, wherein the process further includes determining, based on the derived frequency and frequency correction, the m / z of the ion at a given time during the acquisition of the time-variable signal. Example 13. The system of Example 12, wherein the process further includes determining a mass of the ion at a given time based on the charge state of the ion and the m / z of the ion at a given time. Example 14. The system of one of Examples 10 to 13, wherein determining the frequency correction as a function of time comprises: generating δSTORI data based on the STORI data, where the δSTORI data represent a time derivative of the STORI data; generating, based on the δSTORI data and according to equation (4), δSTORI phase data, which represent a variation of a δSTORI phase angle θ δSTORI represent over time: θδSTORI(tn)=tan−1(δSTORI>Imag(tn)δSTORIReal(tn)) where δSTORI Real (t n ) the temporal derivative of STORI Real (t n ) is and δSTORI Imag (t n ) the temporal derivative of STORI Imag (t n ) is; and determining the frequency correction as a function of time based on a slope of θ δSTORI (t n ) depending on time. Example 15. The system of one of Examples 9 to 14, where determining the charge state of the ion based on the regenerated STORI data includes: Determining STORI Mag -values ​​over time based on the regenerated STORI data according to equation (3): STORIMag(tn)=STORIIReal(tn)2+STORIImag(tn)2 and determining the charge state of the ion based on a slope of STORI Mag (t n ) and a relationship between ion charge and the slope of STORI Mag (t n ). Example 16. The system of Example 15, wherein: the process further includes determining a lifetime of the ion based on the regenerated STORI data and determining the charge state of the ion on the slope of STORI Mag (t n ) during the lifetime of the ion. Example 17. A system for performing charge-detection mass spectrometry, comprising: an ion-trap mass analyzer that captures an ion within a capture region and establishes a capture field within the capture region that causes the ion to vibrate; and a computing system configured to perform a process comprising: acquiring a time-varying signal representing a current induced at a detector by a vibrating motion of an ion within a capture region; processing the time-varying signal to derive a frequency of the vibrating motion; generating Selective Temporal Overview of Resonant Ion (STORI) data according to equations (1) and (2), which define STORI Real -Values ​​over time and HISTORY Imag - Display values ​​over time: STORIReal(tn)=S(tn)∗cos(ω∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω∗tn)+STORIImag(tn−1) where S(t n ) the amplitude of the time-varying signal and ω the derived frequency of the oscillation; regenerating the STORI data based on a variation of the frequency of the oscillation over time and determining a charge state of the ion based on the regenerated STORI data. Example 18. The system according to claim 17, wherein the ion trap mass analyzer comprises an orbital electrostatic ion trap mass analyzer. Example 19. A non-volatile, computer-readable medium storing instructions which, when executed, instruct at least one processor of a mass spectrometry computing device to perform a process comprising: capturing a time-varying signal representing a current induced by an oscillatory motion of an ion within a capture region at a detector; processing the time-varying signal to derive a frequency of the oscillatory motion; generating Selective Temporal Overview of Resonant Ion (STORI) data according to equation (8), which STORI Mag - Display values ​​over time: STORIMag(tn)=STORIReal(tn)2+STORIImag(tn)2+STORIMag(tn−1) where values ​​from STORI Real (t n ) and STORY Imag (t n ) at time tn according to equations (6) and (7): STORIReal(tn)=S(tn)∗cos(ω∗tn) STORIImag(tn)=−S(tn)∗sin(ω∗tn) where S(t n) the amplitude of the time-varying signal and ω the derived frequency of the oscillatory motion; and determining a charge state of the ion based on the STORI data. Example 20. The non-volatile, computer-readable medium from Example 19, where determining the charge state of the ion on a slope of STORI Mag (t n ) and a relationship between ion charge and the slope of STORI Mag (t n ) is based. Example 21. A non-volatile, computer-readable medium storing instructions which, when executed, instruct at least one processor of a mass spectrometry computing device to perform a process comprising: capturing a transient for one or more ion species that is / are trapped and oscillating within a capture region; generating a Fourier transform (FT) spectrum based on the transient; selecting a spectral interval within the FT spectrum that includes one or more FT components; estimating a frequency of each FT component within the spectral interval; processing the FT spectrum to determine a time-resolved frequency for an FT component within the spectral interval based on an isolated contribution of the FT component to the spectral interval;and determining a charge state z of an ion species corresponding to the FT component, based on the time-resolved frequency for the FT component within the spectral interval. Example 22. The computer-readable medium of Example 21, wherein the process further comprises: determining a mass-to-charge ratio (m / z) of an ion species corresponding to the FT component, based on the time-resolved frequency of the FT component. Example 23. The computer-readable medium of Example 21, wherein the time-resolved frequency for the FT component within the spectral interval is determined based on a time-resolved frequency correction applied to the estimated frequency of the FT component. Example 24. The computer-readable medium of Example 21, wherein processing the FT spectrum to determine the time-resolved frequency for the FT component comprises: applying a filter of width w to each FT component within the spectral interval based on the estimated frequency of each FT component to generate filtered spectral data; generating a model transient for each FT component within the spectral interval based on the filtered spectral data, wherein the model transient includes an amplitude function and a phase function; determining a spectral contribution of each FT component to the spectral interval for each FT component based on the amplitude function and the phase function for the FT component;for the FT component, subtract the spectral contributions of other FT components within the spectral interval and determine, based on the phase function of the model transient, a time-resolved frequency for the FT component within the spectral interval. Example 25. The computer-readable medium of Example 24, wherein generating the model transient involves performing an inverse Fourier transform on the filtered spectral data. Example 26. The computer-readable medium of Example 24, wherein the amplitude function comprises a piecewise constant amplitude function. Example 27. The computer-readable medium of Example 26, wherein the piecewise constant amplitude function has a plurality of breakpoints. Example 28. The computer-readable medium of Example 27, wherein the determination of the charge state z of the ion species is based on an amplitude of the piecewise constant amplitude function between adjacent holding points. Example 29. The computer-readable medium of Example 24, where the phase function comprises a piecewise polynomial function. Example 30. The computer-readable medium of Example 24, wherein determining the spectral contribution of each FT component to the spectral interval involves performing an FT on the model transient for each FT component based on the amplitude function and phase function for each respective FT component. Example 31. The computer-readable medium of Example 24, further comprising the iterative determination of a filter width. Example 32. The computer-readable medium of Example 31, wherein: an iteration loop comprises: applying the filter to each FT component within the spectral interval; generating the model transient for each FT component within the spectral interval; determining the spectral contribution of the FT component to the spectral interval and subtracting the spectral contributions of other components within the spectral interval; and wherein iteratively determining the width of the filter comprises performing the iteration loop until an iteration loop condition is satisfied. Example 33. The computer-readable medium of Example 32, where the iteration loop condition is satisfied when a temporal resolution reaches a threshold. Example 34. The computer-readable medium of Example 32, wherein the iteration loop condition includes performing a threshold number of iteration loops. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] US 63 / 462,833

[0001] US 2022 / 0246414A1 [0004, 0016] Cited non-patent literature

[0000] Köster, “The Concept of Electrostatic Non-Orbital Harmonic Ion Trapping”, International Journal of Mass Spectrometry, Vol. 287, pages 114 to 118 (2009

[0016] Iterations 2300-1 to 2300-5

[0114] Miller, Zachary M., et al. „Apodization specific fitting for improved resolution, charge measurement, and data analysis speed in charge detection mass spectrometry“, Journal of the American Society for Mass Spectrometry 33.11 (2022): 2129 bis 2137

[0129] Compact Discs) und DVDs (Digital Video Discs), andere optische Medien, RAMs (Random Access Memory), PROMs (programmierbare Lesespeicher

[0136]

Claims

[1] Non-volatile computer-readable medium storing instructions which, when executed, instruct at least one processor of a mass spectrometry computing device to perform a process comprising: Capturing a time-varying signal representing a current induced at a detector by an oscillating motion of an ion within a capture region; Processing the time-varying signal to derive a frequency of the oscillation; Generating Selective Temporal Overview of Resonant Ion data (STORI data) according to equations (1) and (2), the STORI Real -Values ​​over time and HISTORY Imag - Display values ​​over time: STORIReal(tn)=S(tn)∗cos(ω∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω∗tn)+STORIImag(tn−1) where S(t n ) the amplitude of the time-varying signal and ω the derived frequency of the oscillatory motion; Regenerating STORI data based on a variation in the frequency of the oscillation over time and Determining the charge state of the ion based on the regenerated STORI data. [2] Non-volatile computer-readable medium according to claim 1, wherein regenerating the STORI data comprises: Determining a frequency correction as a function of time relative to the derived frequency and Regenerating the STORI data based on the derived frequency and frequency correction. [3] Non-volatile computer-readable medium according to claim 2, wherein the STORI data are regenerated according to equations (1') and (2'): STORIReal(tn)=S(tn)∗cos(ω(tn)∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω(tn)∗tn)+STORIImag(tn−1) where ω(t n) is a frequency of the oscillatory motion as a function of time, which is determined based on the derived frequency and the frequency correction as a function of time. [4] Non-volatile computer-readable medium according to claim 2, wherein the process further comprises determining, based on the derived frequency and frequency correction, the m / z of the ion at a given time during the acquisition of the time-variable signal. [5] Non-volatile computer-readable medium according to claim 4, wherein the process further comprises determining a mass of the ion at a given time based on the charge state of the ion and the m / z of the ion at a given time. [6] Non-volatile computer-readable medium according to claim 2, comprising determining the frequency correction as a function of time: Generating δSTORI data based on the STORI data, where the δSTORI data represent a temporal derivative of the STORI data; Generate, based on the δSTORI data and according to equation (4), δSTORI phase data that represent a variation of a δSTORI phase angle θ δSTORI represent over time: θδSTORI(tn)=tan−1(δSTORIImag(tn)δSTORIReal(tn)) where δSTORI Real (t n ) the temporal derivative of STORI Real (t n ) is and δSTORI Imag (t n ) the temporal derivative of STORI Imag (t n ) is; and Determining the frequency correction as a function of time based on a slope of θ δSTORI (t n ) depending on time. [7] Non-volatile computer-readable medium according to claim 1, wherein determining the charge state of the ion based on the regenerated STORI data comprises: Determining STORYMag -values ​​over time based on the regenerated STORI data according to equation (3): STORIMag(tn)=STORIIReal(tn)2+STORIImag(tn)2 Determining the charge state of the ion based on a slope of STORI Mag (t n ) and a relationship between ion charge and the slope of STORI Mag (t n ). [8] Non-volatile computer-readable storage medium according to claim 7, wherein: The process further includes determining, based on the regenerated STORI data, the lifetime of the ion and Determining the charge state of the ion is based on the slope of STORIMag(tn) during the lifetime of the ion. [9] System for determining the charge state of an ion, comprising: one or more processors; and a memory in which executable instructions are stored which, when executed by one or more processor(s), cause a computing device to carry out a process, comprising: Capturing a time-varying signal representing a current induced at a detector by an oscillating motion of an ion within a capture region; Processing the time-varying signal to derive a frequency of the oscillation; Generating Selective Temporal Overview of Resonant Ion data (STORI data) according to equations (1) and (2), the STORI Real -Values ​​over time and HISTORY Imag - Display values ​​over time: STORIReal(tn)=S(tn)∗cos(ω∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω∗tn)+STORIImag(tn−1) where S(t n ) the amplitude of the time-varying signal and ω the derived frequency of the oscillatory motion; Regenerating STORI data based on a variation in the frequency of the oscillation over time and Determining the charge state of the ion based on the regenerated STORI data. [10] System according to claim 9, comprising regenerating the STORI data: Determining a frequency correction as a function of time relative to the derived frequency and Regenerating the STORI data based on the derived frequency and frequency correction. [11] System according to claim 10, wherein the STORI data are regenerated according to equations (1') and (2'): STORIReal(tn)=S(tn)∗cos(ω(tn)∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω(tn)∗tn)+STORIImag(tn−1) where ω(t n ) is a frequency of the oscillatory motion as a function of time, which is determined based on the derived frequency and the frequency correction as a function of time. [12] System according to claim 10, wherein the process further comprises determining, based on the derived frequency and the frequency correction, the m / z of the ion at a specific time during the acquisition of the time-variable signal. [13] System according to claim 12, wherein the process further comprises determining a mass of the ion at a given time based on the charge state of the ion and the m / z of the ion at a given time. [14] System according to claim 10, wherein determining the frequency correction as a function of time comprises: Generating δSTORI data based on the STORI data, where the δSTORI data represent a temporal derivative of the STORI data; Generate, based on the δSTORI data and according to equation (4), δSTORI phase data that represent a variation of a δSTORI phase angle θ δSTORI represent over time: θδSTORI(tn)=tan−1(δSTORIImag(tn)δSTORIReal(tn)) where δSTORIReal (t n ) the temporal derivative of STORI Real (t n ) is and δSTORI Imag (t n ) the time derivative of STORIImag(tn); and Determining the frequency correction as a function of time based on a slope of θ δSTORI (t n ) depending on time. [15] System according to claim 9, wherein determining the charge state of the ion based on the regenerated STORI data comprises: Determining STORY Mag -values ​​over time based on the regenerated STORI data according to equation (3): STORIMag(tn)=STORIIReal(tn)2+STORIImag(tn)2 Determining the charge state of the ion based on a slope of STORI Mag (t n ) and a relationship between ion charge and the slope of STORI Mag (t n ). [16] System according to claim 15, wherein: The process further includes determining, based on the regenerated STORI data, the lifetime of the ion and Determining the charge state of the ion on the slope of STORI Mag (t n ) during the lifetime of the ion. [17] System for performing charge detection mass spectrometry, the system comprising: an ion trap mass analyzer that captures an ion within a capture region and establishes a capture field within the capture region that causes the ion to be set into an oscillating motion; and a computing system configured to perform a process, comprising: Capturing a time-varying signal representing a current induced at a detector by an oscillating motion of an ion within a capture region; Processing the time-varying signal to derive a frequency of the oscillation; Generating Selective Temporal Overview of Resonant Ion data (STORI data) according to equations (1) and (2), the STORI Real -Values ​​over time and HISTORY Imag - Display values ​​over time: STORIReal(tn)=S(tn)∗cos(ω∗tn)+STORIReal(tn−1) STORIImag(tn)=−S(tn)∗sin(ω∗tn)+STORIImag(tn−1) where S(t n ) the amplitude of the time-varying signal and ω the derived frequency of the oscillatory motion; Regenerating STORI data based on a variation in the frequency of the oscillation over time and Determining the charge state of the ion based on the regenerated STORI data. [18] System according to claim 17, wherein the ion trap mass analyzer comprises an orbital electrostatic ion trap mass analyzer. [19] Non-volatile computer-readable medium storing instructions which, when executed, instruct at least one processor of a mass spectrometry computing device to perform a process comprising: Capturing a time-varying signal representing a current induced at a detector by an oscillating motion of an ion within a capture region; Processing the time-varying signal to derive a frequency of the oscillation; Generating Selective Temporal Overview of Resonant Ion data (STORI data) according to equation (8), the STORI Mag - Display values ​​over time: STORIMag(tn)=STORIReal(tn)2+STORIImag(tn)2+STORIMag(tn−1) where values ​​from STORI Real (t n ) and STORY Imag (t n ) currently t n can be determined according to equations (6) and (7): STORIReal(tn)=S(tn)∗cos(ω∗tn) STORIImag(tn)=−S(tn)∗sin(ω∗tn) where S(t n ) the amplitude of the time-varying signal and ω the derived frequency of the oscillatory motion; and Determining the charge state of the ion based on the STORI data. [20] Non-volatile computer-readable medium according to claim 19, wherein the determination of the charge state of the ion on a slope of STORI Mag (t n ) and a relationship between ion charge and the slope of STORI Mag (t n ) is based.

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