Method for performing charge detection mass spectrometry with time resolution

Abstract

The process for charge detection mass spectrometry involves obtaining a time-varying signal representing the current induced on the detector by the vibrational motion of ions within the trap region, processing the time-varying signal to derive the frequency of the vibrational motion, and determining the time-varying relationship between the amplitude of the time-varying signal and the derived frequency of the vibrational motion. real STORI for values ​​and time imag This includes generating selective temporal overview (STORI) data of resonant ions that represent values, regenerating STORI data based on the frequency variation of the vibrational motion over time, and determining the charge state of the ions based on the regenerated STORI data.

Description

[Technical Field]

[0001] (Related applications) This application claims priority to U.S. Provisional Patent Application No. 63 / 462,833, filed on 28 April 2023, which is incorporated herein by reference in its entirety. [Background technology]

[0002] Charge Detection Mass Spectrometry Spectrometry (CDMS) is a technique in which the mass of individual ions is determined from the simultaneous measurement of the mass-to-charge ratio (m / z) and charge of each ion. Conventional techniques for performing CDMS include Fourier transform (FT) mass spectrometry (FT mass spectrometry). This includes the use of spectrometry (FT-MS). In FT-MS, ions oscillate in an electrostatic field (e.g., an orbital electrostatic trap mass spectrometer such as an Orbitrap® analyzer), a magnetic field (e.g., an FT-Ion Cyclotron Resonance (FT-ICR) mass spectrometer), or a combination of both. The signal measured over time, called the transient, is Fourier transformed to obtain the FT spectrum. The ion's oscillation frequency is a function of the ion's mass, and therefore m / z. Thus, each spectral component (peak) of the FT spectrum corresponds to a specific oscillation frequency of the trapped ion, from which the ion's m / z can be clearly determined. The ion's charge z can then be directly determined from the amplitude of the FT spectral peak, based on the assumption that the induced current signal is proportional to the ion's charge state. A simple CDMS method assumes that the amplitude of the FT spectral peak for a given frequency is proportional to the ion's charge.

[0003] However, the FT spectral peak amplitude is not a good measure of the induced current signal because the residence time (lifetime) of ions in the analytical trap may be shorter than the signal acquisition period. The lifetime of an ion can be shortened by destructive collisions with residual gas molecules or by other mechanisms of ion fragmentation. As a result of fragmentation, ions are removed from the trapped ion population or their vibrational frequency changes abruptly due to a change in mass. In either case, the induced current signal ends at the original frequency. If charged fragments with different m / z remain trapped in the analyzer, the original signal from the parent ion is lost, and at the same time, a new FT component appears in the induced current signal at a different frequency. The new frequency is different from the original frequency, but may be indistinguishable from the original frequency on the FT spectrum. However, since the new signal component does not begin at the start of the acquisition period, its peak height is also underestimated.

[0004] U.S. Patent Application Publication No. 2022 / 0246414(A1), which is incorporated in its entirety herein by reference, describes a conventional Selective Temporal Overview of Resonant Ion (STORI) method that can be used to estimate the actual lifetime of an ion within a signal acquisition period. Using an FT spectrum generated from transients, the STORI method applies a special bandfilter centered on a specific FT spectral frequency (also referred to as the STORI frequency) to construct an incremental STORI signal as a function of time. The incremental STORI signal is typically a piecewise linear function of time that rises with a specific slope when an ion remains in the analysis trap and oscillates at a specific frequency. The slope of the STORI signal is proportional to the instantaneous value of the induced current at this frequency and, therefore, proportional to the charge state of the ion. When an ion current at a specific frequency is missing, such as after ion loss or fragmentation, the STORI signal remains constant (flat). The STORI method allows for the estimation of the instantaneous amplitude of the induced ion current with temporal resolution, and therefore, the charge state of the ion can be accurately determined regardless of whether the ion persists throughout the entire acquisition period.

[0005] The conventional STORI method represents a significant improvement over prior art. However, the conventional STORI method suffers from two problems. Firstly, the conventional STORI method is sensitive to the selection of the STORI frequency, which is centered on the integrated band filter mentioned above. The STORI frequency may differ from the true vibrational frequency of the ion. As a result, the incremental STORI signal is no longer linear, and its slope is underestimated. The STORI frequency may differ from the true vibrational frequency of the ion for various reasons. In some cases, the centroid of the FT spectral peak is inaccurately determined, which often occurs in the presence of noise or a low signal-to-noise ratio. This can occur even if the vibrational frequency of the ion remains constant over the acquisition period. In other cases, especially for large molecules, the vibrational frequency of the ion drifts over time due to changes in the ion's mass during the acquisition period. For example, desolvation of ions in a vacuum environment (e.g., desorption of water or solvent molecules from the ion) or loss of other small neutral fragments from the ion results in a change in the ion's mass, which in turn results in a change in the ion's vibrational frequency. Frequency drift renders the selected STORI frequency meaningless.

[0006] The second problem relates 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 spectral peaks, including peaks from fragmentation products, affects the STORI integral band filter, resulting in irregular incremental STORI signals that are difficult to interpret in terms of piecewise linearity. Consequently, CDMS experiments can be inefficient, as they may have low throughput and most target analytes cannot be analyzed simultaneously. [Overview of the project]

[0007] The following description presents a simplified overview of one or more aspects of the methods and systems described herein to provide a basic understanding of such aspects. This overview is not an extensive overview of all contemplated aspects, and is not intended to identify key or critical elements of all aspects nor to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects of the methods and systems described herein in a simplified form as a prelude to the more detailed description that is presented below.

[0008] In some exemplary examples, a non - transient computer - readable medium stores instructions that, when executed, cause at least one processor of a computing device for mass spectrometry to obtain a time - varying signal representing a current induced on a detector by the oscillatory motion of ions within a trap region, process the time - varying signal to derive the frequency of the oscillatory motion, and generate selected time - resolved ion (STORI) data representing the STORI value with respect to time and the STORI value with respect to time in accordance with equations (1) and (2). real value and the STORI imag value, of resonant ions, STORI real (t n ) = S(t n ) * cos(ω * t n ) + STORI real (t n-1 ) (1) STORI imag (t n ) = - S(t n ) * sin(ω * t n ) + STORI imag (t n-1 ) (2) Where S(t nThe instructions specify that ω is the amplitude of a time-varying signal, and ω is the derived frequency of the oscillation; that the system should perform a process that includes regenerating STORI data based on the variation in the frequency of the oscillation over time; and that the system should determine the charge state of the ions based on the regenerated STORI data.

[0009] In some exemplary examples, a system for determining the charge state of an ion comprises one or more processors and a memory for storing executable instructions, which, when executed by one or more processors, instruct a computing device to carry out a process including: obtaining a time-varying signal representing a current induced on a detector by the oscillating motion of an ion in a trap region; processing the time-varying signal to derive the frequency of the oscillating motion; and generating according to equations (1) and (2).

[0010] In some exemplary examples, a system for performing charge-detection mass spectrometry comprises an ion-trap mass spectrometer that traps ions within a trap region and establishes a trap electric field within the trap region that causes the ions to undergo oscillatory motion, and a computing system configured to perform a process which includes obtaining a time-varying signal representing a current induced on a detector by the oscillatory motion of ions within the trap region, processing the time-varying signal to derive the frequency of the oscillatory motion, and generating according to equations (1) and (2).

[0011] In some illustrative examples, a non-temporary computer-readable medium stores instructions, and when the instructions are executed, at least one processor of a computing device for mass spectrometry obtains a time-varying signal representing the current induced on the detector by the vibrational motion of ions in the trap region, processes the time-varying signal to derive the frequency of the vibrational motion, and processes the STORI signal against time according to equation (8). mag This involves generating selective temporal overview (STORI) data of resonant ions that represent values,

[0012]

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[0013] In some exemplary cases, non-transient computer-readable media, when executed, instructs at least one processor of a computing device for mass spectrometry to perform a process that includes: acquiring transient phenomena of one or more ionic species trapped and oscillating within a trap region; generating a Fourier transform (FT) spectrum based on the transient phenomena; selecting a spectral interval within the FT spectrum that contains one or more FT components; estimating the frequency of each FT component within the spectral interval; processing the FT spectrum to determine the time-resolved frequencies for the FT components within the spectral interval based on the separated contributions of the FT components to the spectral interval; and determining the charge state z of the ionic species corresponding to the FT components based on the time-resolved frequencies of the FT components within the spectral interval. [Brief explanation of the drawing]

[0014] The accompanying drawings illustrate various embodiments and are part of this specification. The illustrated embodiments are merely examples and do not limit the scope of this disclosure. Throughout the drawings, the same or similar reference numerals indicate the same or similar elements. [Figure 1A] An exemplary STORI plot of ions remaining throughout the entire acquisition period is shown. [Figure 1B] The STORI plot shows an example of ions that do not persist throughout the entire acquisition period. [Figure 2A] An example FT spectrum of a fixed-frequency sinusoidal signal with a peak is shown. [Figure 2B] Figure 2A shows an exemplary STORI plot generated using the precisely centroidal STORI frequencies. [Figure 3A] An example FT spectrum of a fixed-frequency sinusoidal signal with a peak is shown. [Figure 3B] Figure 3A shows an exemplary STORI plot generated using the inaccurately centroidal STORI frequencies. [Figure 4] Figure 2B shows an exemplary δSTORI plot representing the δSTORI data obtained from the STORI data, for the ideal case with fixed frequency and correct centroid. [Figure 5] For the case of an inaccurately centroidal STORI frequency, an exemplary δSTORI plot representing δSTORI data obtained from the STORI data in Figure 3B is shown. [Figure 6] An exemplary δSTORI phase plot is shown, which has a δSTORI phase curve representing the δSTORI phase angle over time during a transient phenomenon. [Figure 7] This section provides an example of how to implement the modified STORI method. [Figure 8] An exemplary method for performing operation 708 of method 700 is shown. [Figure 9] An exemplary STORI plot is shown, representing STORI data generated using inaccurately centroidal STORI frequencies. [Figure 10A]An exemplary δSTORI plot representing δSTORI data is shown. [Figure 10B] Figure 10A shows an exemplary δSTORI phase plot representing the δSTORI phase data that can be generated using the δSTORI data shown. [Figure 11] An exemplary plot is shown that has a frequency correction curve representing the time-varying estimated frequency correction. [Figure 12] An exemplary STORI plot representing the regenerated STORI data is shown. [Figure 13A] An example FT spectrum of a signal with a peak around 55 kHz is shown. [Figure 13B] Figure 13A shows an exemplary STORI plot representing the STORI data generated for the signal shown in the FT spectrum. [Figure 14A] An exemplary δSTORI plot representing δSTORI data is shown. [Figure 14B] Figure 14A shows an exemplary δSTORI phase plot representing the δSTORI phase data that can be generated using the δSTORI data shown. [Figure 15A] An exemplary plot is shown that has a frequency correction curve representing the time-varying estimated frequency correction. [Figure 15B] An exemplary STORI plot representing the regenerated STORI data is shown. [Figure 16] An exemplary method for determining the mass of an ion using a modified STORI method is shown. [Figure 17] An exemplary STORI plot generated according to the frequency-insensitive STORI method is shown. [Figure 18] This document describes an exemplary method for determining the time-resolved frequencies of FT components within an FT spectrum and for determining the charge state of the ions represented by these FT components. [Figure 19] Figure 18 shows an exemplary method for performing operation 1810 of the method. [Figure 20A] Examples of the absolute value sequence of amplitudes for model transient phenomena and fitted piecewise constant amplitude functions are shown. [Figure 20B] Figure 20A shows the trial values ​​of the penalty function versus breakpoint set for the piecewise constant amplitude function. [Figure 21] An exemplary plot is shown, including a curve representing the amplitude correction coefficient. [Figure 22] An exemplary FT spectrum divided into multiple FT bins along the frequency domain is shown, along with a selected spectral interval having three FT components. [Figure 23] For each of the five different iterations of the method in Figure 19, the spectral interval and the superimposed filter function of the FT component of the spectral interval are shown. [Figure 24] For each of the five different iterations, curves are shown representing the spectral interval and amplitude values ​​of the first FT component over the transient acquisition period. [Figure 25] For each of the five different iterations, curves are shown representing the spectral interval and amplitude values ​​of the second FT component over the transient acquisition period. [Figure 26] The spectral interval 2202 and curve 2602, which represents the amplitude values ​​of the third FT component over the transient acquisition period for each of the five different iterations, are shown. [Figure 27A] The spectral interval, amplitude value, and the fitted piecewise constant amplitude function obtained in the last iteration of the FT component of the spectral interval in Figure 22 are shown. [Figure 27B] The phase as a function of time and the fitted phase function obtained in the last iteration for the FT component of the spectral interval in Figure 22 are shown. [Figure 28] Figure 22 shows a plot of the FT components of the spectral interval, plotted as a function of time, with the frequency corrected based on the time derivative of each phase function. [Figure 29] Figures 18 and 19 show exemplary implementations of the method using overlapping spectral intervals. [Figure 30] An exemplary CDMS system is shown. [Figure 31]This document describes an exemplary computing device that may be specifically configured to perform one or more of the processes described herein. [Modes for carrying out the invention]

[0015] Methods and systems for performing CDDS with time resolution are described herein. The methods and systems described herein address these problems by finding the instantaneous amplitude and / or instantaneous frequency of FT components with time resolution. Amplitude is interpreted as the number of trapped charges determined over time over the signal acquisition period. Abrupt changes in amplitude are detected and interpreted as ion decay events (e.g., ion fragmentation or desolvation events). Instantaneous frequencies reflect the m / z of the ions and their time evolution. Thus, the methods described herein account for inaccurately centroidal and / or shifting frequencies by determining the time-dependent frequency profile over the ion lifetime. In some examples, modified STORI methods generate STORI signals using time-varying STORI frequencies. In further examples, multiple FT components in an FT spectrum that may interfere with each other in different ways, constructively or destructively, and thereby adversely affect the STORI method can be multiplexed and accurately analyzed by CMDS by subtracting the effects of the interfering components.

[0016] The method described herein allows for non-constant m / z values, for example, due to solvent loss in a vacuum environment. Advantages include more detailed analytical information obtained from the mass spectrum, particularly improved amplitude and frequency accuracy in the case of ion fragmentation and mass loss during the acquisition period, as well as improved accuracy in charge state estimation. Furthermore, the method described herein can increase the throughput of CDMS experiments and increase the efficiency of CDMS by enabling the simultaneous analysis of multiple analytes by CDMS.

[0017] The CDMS methods described herein may be carried out using a system comprising an ion trap mass spectrometer and a CDMS control system. The ion trap mass spectrometer traps one or more ions within a trap region and establishes a trap electric field within the trap region that causes the ions to undergo oscillatory motion. In some examples, the ion trap mass spectrometer is an orbital electrostatic ion trap mass spectrometer that establishes a quadrupole logarithmic trap electric field, such as the Orbitrap® mass spectrometer (Thermo Fisher Scientific, Waltham, MA), as described in U.S. Patent Application Publication 2022 / 0246414(A1). However, it will be understood that the methods described herein may be implemented in any ion trap analyzer or equivalent structure in which the confined ions undergo oscillatory motion within a trap region in the presence of an electrostatic trap electric field (e.g., an electrostatic linear ion trap (ELIT) analyzer) or a magnetic trap electric field (e.g., a Fourier transform ion cyclotron resonance (FT-ICR) analyzer), including ion traps in which ions do not undergo orbital motion. An example of a suitable non-orbital electrostatic trap is the Cassinian trap described in Koster, "The Concept of Electrostatic Non-Orbital Harmonic Ion Trapping," International Journal of Mass Spectrometry, V.287, pp.114-118 (2009), which is incorporated herein by reference. The CDMS control system includes software and / or hardware components configured to perform any of the operations described herein, or to instruct another system, device, or apparatus to perform any of the operations described herein, which are described in more detail below.

[0018] To help understand the principles of the modified STORI method described, a review of conventional CDMS techniques and traditional STORI methods is given here. Conventional CDMS techniques are based on the fundamental principle of the Discrete Fourier Transform (DFT), where a signal, represented in the time domain (transient) as a set of discrete time and value (signal intensity) pairs, can be represented in the frequency domain as a set of discrete frequency and complex number pairs. Conventional DFT returns a single magnitude for any given frequency, representing how much signal was accumulated at that frequency over the entire acquisition period. The DFT value at any frequency is represented as a complex number and can be obtained by calculating the correlation of the signal with respect to the cosine (real) and sine (imaginary) components of the frequency. Conventional DFT returns a single magnitude for any given frequency, representing how much signal was accumulated at that frequency over the entire transient period. A simple CDMS method assumes that the magnitude of the value returned by the DFT for a given frequency is proportional to the charge of the ion. Therefore, an ion with twice the charge state compared to another ion should induce a signal twice as high on the detection electrode compared to the signal induced by the other ion.

[0019] However, if an ion "disappears" during the transient due to ion fragmentation or other reasons, and ceases to provide further signal, the single value returned by the DFT will be lower than if the ion had survived the entire transient. As a result, a single DFT value is insufficient to reliably determine the charge state. Conventional STORI methods were developed to estimate the actual lifetime of an ion and the instantaneous amplitude of the induced ion current with time resolution during the signal acquisition period.

[0020] In the conventional STORI method, time-varying signals from a detector are processed to determine the m / z and charge of ions. The determination of m / z is achieved by applying an FFT to the time-varying signal to generate an FT spectrum from which the frequency ω of the ion's harmonic motion ("STORI frequency") can be determined. The STORI frequency can be determined by finding the centroid of the FT spectral peaks, for example, by fitting a parabola or other curve to the points of the FT spectral peaks, or by performing a line search to optimize the maximum value of the FT spectral peaks. The centroidalized STORI frequency ω can then be correlated to m / z based on known correlations. In some examples, the correlation between the STORI frequency ω and m / z is expressed by the following relationship:

[0021]

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[0022] The determination of the ion charge z is based on the STORI signal over the lifetime of the ion being acquired. The STORI signal at any given time point is represented as a complex number and can be acquired by calculating the correlation of the time-varying signal (transient) with respect to the cosine (real) and sine (imaginary) components of the frequency. However, conventional STORI methods do not calculate a single value for the sum of cosine and sine correlations over the entire length of the transient, as in DFT techniques. Instead, they maintain separate cumulative records for the cosine and sine correlations (real and imaginary components, respectively) as the transient progresses. mag The STORI signal (represented by t) is plotted at each point, and the slope of the time-varying STORI signal is used to determine the ion lifetime and charge state. Each point in the set of STORI data is at time t n Discretized time-varying signal S(t) n) (transient phenomenon) is the product of either a cosine wave or a sine wave at the STORI frequency ω, and is expressed in the following equations (1) and (2), the preceding STORI real or the previous point in time t n-1 STORI obtained in imag The values ​​are summed together, and each value of the STORI signal is obtained based on the following equation (3).

[0023]

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[0024] The charge z of the ion is the measured slope of the STORI signal (STORI mag (t n The STORI signal is determined according to correlation data that correlates the slope of the STORI signal with the ion charge. In addition to determining the charge of the trapped ions, the STORI signal can be used to identify and characterize ion decay events (e.g., when an ion species decays during the acquisition of a time-varying signal) and to identify and evaluate signals generated by two or more simultaneously trapped ions. Therefore, when determining the ion charge z, the slope of the STORI signal after an ion decay event can be ignored, or transient phenomena of ion decay can be ignored. Once the ion's m / z and charge z are determined using these processing methods, the ion's mass m can be easily calculated.

[0025] As described above, the conventional STORI method does not calculate a single value for the sum of sinusoidal and cosine correlations over the entire duration of the transient, as in the conventional DFT method, but rather maintains the cumulative sum of sinusoidal and cosine correlations as the transient progresses. Figures 1A and 1B show STORI plots representing simulated STORI data for a frequency of approximately 55 kHz, which is representative of the viral capsid. Figure 1A shows STORI plot 100A of ions that remain throughout the entire acquisition period. STORI plot 100A shows STORI mag Curve 102A represents the magnitude of the STORI signal, or STORI value, as a function of time, and STORI real Curve 104A, which represents this as a function of time (cosine correlation represents the real component), and STORI imag This includes curve 106A, which expresses the relationship as a function of time (the sinusoidal correlation represents the imaginary component). As demonstrated by curves 102A, 104A, and 106A, the ions persist throughout the entire acquisition period.

[0026] Figure 1B shows the STORI plot 100B of ions that do not remain over the entire acquisition period. mag Curve 102B, which represents this as a function of time, and STORI real Curve 104B, which represents this as a function of time, and STORI imag This includes curve 106B, which expresses the value as a function of time. The changes in the slopes of curves 102B, 104B, and 106B at 600 ms indicate ion decay events. In Figures 1A and 1B, the slopes of curves 102A and 102B during the ion lifetime are proportional to the ion's charge state, regardless of whether the ion persists throughout the entire acquisition period.

[0027] Conventional STORI methods assume that the ion's vibrational frequency is constant throughout the acquisition period and that the centroidal STORI frequency is correct (e.g., matches the true frequency of the signal). This assumption is valid only if the following conditions are met: (1) the electric field is constant throughout the transient process, (2) other forces do not significantly alter the ion orbit (e.g., space charge repulsion), and (3) the ion's m / z does not change during the acquisition period (e.g., due to fragmentation, desolvation, etc.). In a typical CDMS experiment, both conditions (1) and (2) are met, and for smaller analytes, condition (3) is also met. However, for larger ions (e.g., viral capsids), condition (3) may not be met due to changes in the ion's mass (and therefore m / z) during transient collection, such as ion fragmentation and / or desolvation. Additionally, accurately centroiding the FT spectral peak is not always easy, especially in the presence of noise. Changes in ion mass and / or inaccurate center-of-mass frequency peaks can lead to errors in the reported STORI slope (the slope of the STORI signal, e.g., the slope of curve 102A or 102B), which results in an inaccurate estimate of the ion's charge.

[0028] Figure 2A shows an exemplary FT spectrum 200A of a fixed-frequency sine wave signal with peak 202. The precisely centroidal STORI frequency is indicated by the dashed line 204. Figure 2B shows an exemplary STORI plot 200B generated using the precisely centroidal STORI frequency from Figure 2A. STORI plot 200B shows the STORI mag Curve 206, which represents this as a function of time, and STORI real Curve 208, which represents this as a function of time, and STORI imagThis includes curve 210, which represents the signal as a function of time. As shown in Figure 2B, the conventional STORI calculation represented by curve 206 (correlation with respect to sine and cosine waves at the centroidal STORI frequency in Figure 2A) linearly increases the signal over time. The real and imaginary parts (cosine and sinusoidal correlations represented by curves 208 and 210, respectively) can have different slopes depending on the initial phase of the signal, but their slopes remain constant throughout the transient because the centroidal STORI frequency and the true frequency of the signal remain in a lockstep throughout the entire time.

[0029] Figure 3A shows an exemplary FT spectrum 300A of a fixed-frequency sinusoidal signal with peak 302. The improperly centroidal STORI frequency is indicated by the dashed line 304. As shown in Figure 3A, the STORI frequency and the true frequency of the signal (indicated by the peak 306 of peak 302) are not identical (for example, due to poor centroidal or due to shifts in the signal frequency during transients). Figure 3B shows an exemplary STORI plot 300B generated using the improperly centroidal STORI frequency from Figure 3A. STORI plot 300B shows the STORI as a function of time. mag Curve 308 represents the time function of STORI real Curve 310 represents the curve, and STORI is a function of time. imag Curve 312 represents the STORI frequency as a function of time with respect to the precisely centroidal STORI frequency. mag The diagram includes the dashed ideal curve 314, and the cosine correlation represented by curves 310 and 312, respectively, and does not construct the signal at the same rate throughout the transient. Rather, the STORI signal represented by curve 308 begins to move between the two correlations as the two frequencies become out of phase with each other. The frequency mismatch results in a decrease in the slope of curve 308 compared to the ideal curve 314, and therefore inaccurate charge estimation.

[0030] As the magnitude of the centroid error increases, the resulting STORI slope decreases rapidly. In the examples in Figures 3A and 3B, a centroid error of 4 ppm reduces the STORI slope by approximately 5%. The signal-to-noise ratio must be very low (<10) at this frequency to obtain a variation of 1 ppm at the centroid, and therefore any centroid error for species with stable m / z in this frequency range should be negligible. However, for species with unstable m / z, such as large molecules bound to solvent molecules, small centroid errors can result in non-negligible slope errors.

[0031] The modified STORI method uses STORI data (for example, STORI real (t n ) and STORY imag (t n Using )), the time-varying STORI frequency over the ion lifetime is determined, and the STORI signal (STORI) is determined based on the time-varying STORI frequency as described below. mag (t n These problems with the conventional STORI method are addressed by regenerating (including)

[0032] STORI signal (STORI mag The rate at which the value of ) transitions between cosine and sine waves (real and imaginary components) depends on the magnitude of the mismatch between the STORI frequency and the true frequency of the signal. Therefore, the modified STORI method estimates the frequency correction as a function of time with respect to the STORI frequency, based on the rate at which the value of the STORI signal transitions between cosine and sine waves. The mismatch between the STORI frequency and the true frequency of the signal is related to the time derivative of the STORI value, called δSTORI. Since the value of the STORI signal is a complex number (sine and cosine correlation), its derivative (δSTORI) is also a complex number, with the real part representing how quickly the STORI signal is incorporated into the cosine wave and the imaginary part representing how quickly the STORI signal is incorporated into the sine wave.

[0033] Figure 4 shows a δSTORI plot 400 representing the δSTORI data obtained from the STORI data in Figure 2B for the idealized case of fixed frequency and correct centroid. The δSTORI plot 400 shows δSTORI as a function of time. mag Curve 402 represents this, and δSTORI is a function of time. real Curve 404 represents this, and δSTORI is a function of time. imag This includes curve 406, which represents the function of STORI. As shown in Figure 4, the δSTORI values ​​(e.g., curves 402, 404, and 406) are all linear and constant. In contrast, Figure 5 shows a δSTORI plot 500 representing the δSTORI data obtained from the STORI data in Figure 3B for the case of an imprecisely centroidal STORI frequency. The δSTORI plot 500 shows δSTORI as a function of time. mag Curve 502 represents this, and δSTORI is a function of time. real Curve 504 represents this, and δSTORI is a function of time. imag This includes curve 406, which represents the function, and the function itself. As shown in Figure 5, the real and imaginary components of the δSTORI data (curves 504 and 506, respectively) are not linear and constant. This is because the source of the STORI amplitude transitions between cosine and sine waves over time due to the mismatch between the STORI frequency and the true frequency of the signal (see Figure 3B), and therefore changes as the transient phenomenon progresses.

[0034] δSTORI phase angle(θ δSTORI ) is a measure of how quickly a signal is transmitted between a sine wave and a cosine wave, and is determined by the ratio of imaginary to real values ​​in the δSTORI data (see Figure 5) according to equation (4) below.

[0035]

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[0036]

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[0037] If the true frequency of the signal is not fixed as in the example in Figure 6, but shifts during transients (e.g., due to desolvation or other decay events), the δSTORI phase curve is not linear. However, the slope of the δSTORI phase curve at any given time point indicates the offset between the STORI frequency and the true frequency of the signal at that time. The modified STORI method, as will be described in more detail below, provides this time-dependent frequency offset information to regenerate the STORI signal at each time point and correct any discrepancies between the STORI frequency and the true frequency of the signal. The time-dependent frequency offset information may also be used to determine the m / z of the ion at a particular time during the transition (e.g., based on the relationship between m / z and ω as described above).

[0038] Figure 7 shows an exemplary method 700 for implementing the modified STORI method. While Figure 7 shows exemplary operation according to one embodiment, other embodiments may omit, add, rearrange, and / or modify any of the operations shown in Figure 7.

[0039] In operation 702, a time-varying signal is acquired that represents the current induced on the detector by the oscillating motion of ions within the trap region. In some examples, the trap region is the trap region of an orbital electrostatic ion trap mass spectrometer (e.g., an Orbitrap® mass spectrometer).

[0040] In operation 704, the time-varying signal is processed to derive the frequency of the ion's vibrational motion. For example, a fast Fourier transform can be performed on the time-varying signal to generate an FT spectrum with peaks representing the ion's vibrational motion. The centroid frequency (derivative frequency) of the ion's vibrational motion is determined based on the FT spectrum. The centroid frequency can be derived in any suitable way, 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 value of the peaks.

[0041] In operation 706, using the center frequency of the oscillatory motion of the ions as the STORI frequency ω, according to the above equations (1) and (2), STORI with respect to time real values and STORI with respect to time imag values are generated for the STORI data representing. In some examples, the STORI data also represents the STORI with respect to time generated according to equation (3). As described above, the STORI data is obtained based on the time-varying signal of the FT spectrum peak and the center frequency. mag values. As described above, the STORI data is obtained based on the time-varying signal of the FT spectrum peak and the center frequency.

[0042] In operation 708, the STORI with respect to time real values, the STORI with respect to time imag values, and the STORI with respect to time mag values are regenerated for the STORI data representing based on the variation of the frequency of the oscillatory motion over time. Operation 708 will be described in more detail below.

[0043] In operation 710, the charge z of the ions is determined based on the regenerated STORI data (e.g., based on the STORI signal). The charge z can be determined as described above based on the slope of the STORI signal.

[0044] Next, operation 708 will be described. FIG. 8 shows an exemplary method 800 for performing operation 708 of method 700 (e.g., regeneration of STORI data based on the variation of the frequency of the oscillatory motion over time). FIG. 8 shows exemplary operations according to one embodiment, but other embodiments may omit, add, rearrange, and / or modify any of the operations shown in FIG. 8.

[0045] In operation 802, δSTORI data is generated based on the STORI data. The δSTORI data is generated by taking the time derivative of the STORI data generated in operation 706 (FIG. 7). The STORI data has a real component (STORI real (t n )) and an imaginary component (STORI imag (t nSince it includes both )), the δSTORI data has a real component (δSTORI real (t n )) and the imaginary component (δSTORI imag (t n This includes both of the above. In some examples, a smooth curve is fitted to the STORI data using an appropriate regression, such as linear regression or quadratic or higher-order polynomial regression, and the derivative is taken from the smoothed curve at each point in time.

[0046] In operation 804, δSTORI phase data is generated based on δSTORI data. As explained above, the δSTORI phase data is determined based on the ratio of imaginary to real values ​​of the δSTORI data at each time point during the transient, according to equation (4).

[0047] In operation 806, frequency correction for the centroidal STORI frequency is determined based on the δSTORI phase data. Frequency correction is an adjustment to the centroidal STORI frequency (derived in operation 704) at each point in time during the transient. Frequency correction can be determined by any suitable method. In some examples, a smooth δSTORI phase curve is fitted to the δSTORI phase data using a suitable regression, such as a quadratic or higher-order polynomial regression. The slope of the δSTORI phase curve is determined at each point in time, for example, by obtaining the derivative of the δSTORI phase curve at each point in time. The slope of the δSTORI phase curve at any given point in time represents the frequency correction applied to the centroidal STORI frequency at the time the STORI data is regenerated in operation 708.

[0048] In operation 808, STORI data (for example, STORI real (t n ), STORY imag (t n ), and STORI mag (t nThe data is regenerated using the STORI frequency correction determined in operation 806. The STORI data is regenerated according to the modified equations (1') and (2') shown below, as well as equation (3) above. STORY real (t n ) = S(t n ) * cos(ω(t n ) * t n )+STORY real (t n-1 ) (1') STORY imag (t n ) = -S(t n ) * sin(ω( t n ) * t n )+STORY imag (t n-1 ) (2') Here, at each time point t during the transient phenomenon n STORI frequency ω(t n ) is the centroidal STORI frequency previously derived in operation 704, adjusted for each time point during the transient by the STORI frequency correction determined in operation 806. For example, the time-dependent STORI frequency ω(t n ) may also be given by the following equation (5). ω(t n )=ω centroid +ω correction (t n ) (5) Here, ω centroid This is the centroidal STORI frequency derived in operation 704 based on the FT spectrum, and ω correction (t n ) is the time t determined in operation 806 n This is the estimated frequency correction value in equation (1'), (2'), and (5), where (t0, t1, ...t n ) represents a sequence of time points at regular intervals of Δt, and ω * Δt << 1.

[0049] In some examples, methods 700 and 800 are performed in real time as the transient phenomenon progresses. In other examples, methods 700 and 800 are performed after acquisition (e.g., after the transient phenomenon has been acquired). Next, various exemplary examples of implementing the modified STORI method are described.

[0050] In the first example, a modified STORI method is used to correct the centroid error at a fixed (invariant) frequency. In this example, the transient has a signal-to-noise ratio of approximately 60, and the centroidalized STORI frequency has an error of +3 ppm. Figure 9 shows an exemplary STORI plot 900 representing the STORI data generated using an inaccurately centroidalized STORI frequency (e.g., in operation 706). STORI plot 900 shows STORI as a function of time. mag Curve 902 represents the time function of STORI real Curve 904 represents the time function of STORI imag Curve 906 represents the STORI frequency as a function of time with respect to the precisely centroidal STORI frequency. mag The dashed ideal curve 908 represents the above, and includes the above. As shown in Figure 9, the sinusoidal and cosine correlations represented by curves 904 and 906, respectively, do not construct the signal at the same rate throughout the transient, exhibiting frequency mismatch. Therefore, curve 902 has a fitting slope error of -2.1% compared to the ideal curve 908 for the theoretical transient without centroid frequency error.

[0051] The derivative of the STORI data in Figure 9 is taken to generate the δSTORI data. Figure 10A shows an exemplary δSTORI plot 1000A representing the δSTORI data that can be generated (for example, in operation 802). δSTORI plot 1000A shows the δSTORI data against time. mag Curve 1002 representing the data, and δSTORI over time. real Curve 1004 representing the data, and δSTORI over time. imagThis includes curve 1006 representing the data. Although not shown, the δSTORI data may include smooth curve fitting for the data represented by curves 1002, 1004, and 1006. As shown in Figure 10A, the real and imaginary components of the δSTORI data (curves 1004 and 1006, respectively) are not constant and change as transient phenomena progress because the source of the STORI amplitude transitions between cosine and sine waves over time due to the mismatch between the STORI frequency and the true frequency of the signal (see Figure 9).

[0052] Figure 10B shows an exemplary δSTORI phase plot 1000B representing δSTORI phase data that can be generated using the δSTORI data shown in Figure 10A (for example, in operation 804). The δSTORI phase plot 1000B includes a curve 1008 representing the δSTORI phase data with respect to time and a smooth δSTORI phase curve 1010 fitted to the δSTORI phase data. As shown, the δSTORI phase data has a consistent downward slope.

[0053] Based on the δSTORI phase data (e.g., based on the fitted δSTORI phase curve 1008), an estimated frequency correction is generated as a function of time for the incorrectly centroidaled STORI frequencies (e.g., in operation 806). As described above, the estimated frequency correction at any given time is the time derivative of the δSTORI phase data (e.g., the δSTORI phase curve 1010) shown in Figure 10B, and can be plotted as a function of time. Figure 11 shows an exemplary plot 1100 with a frequency correction curve 1102 representing the time-varying estimated frequency correction.

[0054] The STORI data is regenerated using the time-varying frequency correction shown in Figure 11, according to equations (1'), (2'), and (3) above (for example, in operation 708). Figure 12 shows an exemplary STORI plot 1200 representing the regenerated STORI data. The STORI plot 1200 shows the STORI data against time. mag Curve 1202 representing the relationship between time and STORI realCurve 1204 representing the relationship between time and STORI imag This includes curve 1206, which represents the curve. The slope of the regenerated STORI data in Figure 12 (e.g., curve 1202) has an error of only 0.6%, compared to the -2.1% error of the slope of curve 902 of the original STORI data. Therefore, the slope accuracy, and thus the ion charge estimation, is improved by compensating for the frequency centroid error.

[0055] In the second example, a modified STORI method is used to compensate for frequency shifts during transitions. As mentioned above, the frequency of ions can shift during transients, for example, due to desolvation or loss of neutral fragments. In this example, the acquired transient has a signal-to-noise ratio of approximately 60 and a frequency jump of +27 ppm to the transient at 300 ms (to simulate a sudden m / z change). Note that in this simulated example, the signal maintains continuity during the transition because the z position of the ions in the mass spectrometer does not shift instantaneously when the frequency changes. Figure 13A shows an exemplary FT spectrum 1300A of the signal with a peak 1302 around 55 kHz. The FT spectrum peak 1302 has a front 1304 due to the signal being at a lower frequency in the first 300 ms of the transient before the frequency shift. Figure 13B shows an exemplary STORI plot 1300B representing the generated STORI data for the signal represented by the FT spectrum 1300A. STORI plot 1300B shows the STORI as a function of time for a fixed (non-shifted) frequency signal. mag Curve 1306 representing STORY over time real Curve 1308 representing STORY over time imag Curve 1310 representing STORY mag This includes the dashed ideal curve 1312. As shown in the figure, the real and imaginary components (curves 1308 and 1310, respectively) are curved, indicating frequency mismatch. The STORI signal represented by curve 1306 has a clear curvature and a fitting slope error of -20.3% compared to the ideal curve 1312 of the theoretical transient phenomenon without frequency shift.

[0056] The derivative of the STORI data in Figure 13B is taken to generate the δSTORI data. Figure 14A shows an exemplary δSTORI plot 1400A representing the δSTORI data that can be generated (for example, in operation 802), where δSTORI plot 1400A shows the δSTORI data against time. mag Curve 1402 representing the data, δSTORI over time real Curve 1404 representing the data, and δSTORI over time. imag The curve 1406 represents the data. Although not shown, the δSTORI data may include smooth curve fittings for the data represented by curves 1402, 1404, and 1406 for further processing of the δSTORI data. As shown in Figure 14A, the real and imaginary components of the δSTORI data (curves 1404 and 1406, respectively) have a distinct change in slope at approximately 300 ms due to a shift in the signal frequency.

[0057] Figure 14B shows an exemplary δSTORI phase plot 1400B representing δSTORI phase data that can be generated using the δSTORI data shown in Figure 14A (e.g., in operation 804). The δSTORI phase plot 1400B includes a curve 1408 representing the δSTORI phase data and a smooth δSTORI phase curve 1410 fitted to the δSTORI phase data. As shown in Figure 14B, the δSTORI phase data has two apparent linear sections transitioning at approximately 300 ms. The sign and magnitude of their slopes indicate that the centroidal STORI frequency is higher than the signal frequency in the 0–300 ms range (negative slope) and lower than the signal frequency in the 300–1000 ms range (positive slope), and that the magnitude of the frequency error is greater in the 0–300 ms range (steeper slope) than in the 300–1000 ms range (shallower slope).

[0058] An estimated frequency correction as a function of time in relation to the centroidal STORI frequency is generated based on the δSTORI phase data (e.g., based on the fitted δSTORI phase curve 1410) (e.g., in operation 806). As described above, 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. Figure 15A shows an exemplary plot 1500A with a frequency correction curve 1502 representing the time-varying estimated frequency correction.

[0059] The STORI data is regenerated (for example, in operation 708) using the time-varying frequency correction shown in Figure 15A, according to equations (1'), (2'), and (3) above. Figure 15B shows an exemplary STORI plot 1500B representing the regenerated STORI data. STORI plot 1500B shows the STORI data against time. mag Curve 1504 representing the time, and STORI real Curve 1506 representing the relationship between time and STORI imag This includes curve 1508, which represents the curve. The slope of the regenerated STORI data in Figure 15B (e.g., curve 1504) has an error of only -1.0%, in contrast to the -20.3% error of the slope of curve 1306 of the original STORI data. Thus, the slope accuracy, and therefore the ion charge estimation, is dramatically improved by compensating for the shift frequency.

[0060] In the modified STORI method, as the transition progresses, the accumulation of the phase of the shifted signal can be tracked and constantly compared to the phase that the current frequency accumulates over the same amount of time. If a discontinuity exists between these two phase accumulations, the phases of the reference sine and cosine waves can be adjusted as appropriate before calculating the sinusoidal and cosine correlations to prevent phase discontinuities that would negate any improvements that frequency correction could achieve.

[0061] It will be recognized that the modified STORI method can be used to correct frequency mismatches even when multiple frequency shifts are present during transients. Furthermore, the modified STORI method can be used when both centroid frequency errors and one or more frequency shifts are present during the transition.

[0062] Figure 16 shows an exemplary method 1600 for determining the mass of an ion using a modified STORI method. Figure 16 shows exemplary operation according to one embodiment, but other embodiments may omit, add, rearrange, and / or modify any of the operations shown in Figure 16.

[0063] In operation 1602, transients are acquired for ions oscillating within the trap region. Transients are time-domain data representing a time-varying signal generated by the current induced on the detector by the oscillating motion of ions within the trap region.

[0064] In operation 1604, a Fourier transform (FT) spectrum is generated based on the transient. The FT spectrum can be generated by any suitable method, such as by performing a fast Fourier transform of the transient to convert the time-domain data to frequency-domain data. The FT spectrum is frequency-domain data and has peaks indicating the frequencies of the ion vibrations within the trap domain.

[0065] In operation 1606, the STORI frequency is determined based on the FT spectrum. The STORI frequency is the estimated frequency of the ion oscillations within the trap region. The STORI frequency can be determined by any suitable method. In some examples, the STORI frequency is determined by finding the centroid of the peaks in the FT spectrum, such as by fitting a parabola or other curve to the points of the FT spectral peaks, or by performing a line search to optimize the maximum value of the FT spectral peaks.

[0066] In operation 1608, STORI data of the FT spectral peak is generated based on the transient phenomena and STORI frequency determined in operation 1606. The STORI data is generated according to equations (1), (2), and (3) above. The STORI data is cosine correlation (STORI real (t n )), sinusoidal correlation (STORI imag (t n )), and optionally the magnitude of the complex number (STORI mag (t n )) represents.

[0067] In operation 1610, the frequency correction as a function of time is determined based on the STORI data. The frequency correction as a function of time is determined as described herein (for example, as described in method 800).

[0068] In operation 1612, the STORI data is regenerated based on frequency correction to compensate for any deviation between the STORI frequency determined in operation 1606 and the true frequency of the signal during transients. The STORI data is calculated based on the previously determined STORI frequency and the time-varying STORI frequency ω(t) determined in operation 1606 based on frequency correction. n By using ), it is regenerated as described above (for example, as in operation 708) according to formulas (1'), (2'), and (3).

[0069] In operation 1614, the ion's charge z is determined based on the regenerated STORI data. For example, as described above, the ion's charge z is determined by the regenerated STORI signal (STORI) during the ion's lifetime. mag (t n It is determined based on the correlation between the slope of )) and the charge state.

[0070] In operation 1616, the m / z of the ion is determined based on the STORI frequency, which is corrected based on the correlation between frequency and m / z. If the true frequency of the signal is not shifted but the centroidal STORI frequency is incorrect, the frequency correction should be constant (see, e.g., Figure 6), and therefore, 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 of the ion also changes over time. Therefore, the m / z of the ion is determined by the time-varying STORI frequency ω(t n This can be decided at any point in time based on the following:

[0071] In operation 1618, the mass m of the ion is determined based on the ion's m / z and charge z. If the ion's m / z is time-varying, then the ion's mass m is also time-varying, and the mass m can be determined for any point in time during the transition.

[0072] In the modified STORI method, separate cumulative records are maintained for cosine and sinusoidal correlations as transient phenomena progress (for the real and imaginary components, respectively). These separate cumulative records are then used in the STORI method. real and STORY imag It is expressed as follows: Next, the magnitude of the complex number (STORI signal value (STORI mag The )) are plotted at each point, and the time-varying magnitude slope is used to determine the ion's lifetime and charge state. As explained, in this method, the current summation of magnitude can be distorted when the actual signal transitions between cosine and sine waves (for example, due to frequency mismatch, as shown and explained with reference to Figure 3B).

[0073] In the alternative STORI method for determining the charge state z of an ion (also referred to herein as the “frequency-insensitive” STORI method), a continuous sum of the magnitudes of STORI is preserved (in contrast to the separate continuous sums of sinusoidal and cosine correlations in the modified STORI method), while the real and imaginary STORI values ​​at each time point are discarded.mag The magnitude of STORI is obtained based on the sine and cosine correlation of that time, and the obtained magnitude of STORI is added to the current sum of STORI magnitudes. The magnitude of STORI is determined according to the following equations (6), (7), and (8).

[0074]

number

[0075] Figure 17 shows an exemplary STORI plot 1700 generated according to the frequency-insensitive STORI method. STORI plot 1700 represents simulated STORI data for a frequency of approximately 55 kHz. STORI plot 1700 is STORI mag This includes curve 1702, which expresses the frequency as a function of time. The slope of curve 1702 is proportional to the charge state of the ions. Similar to the frequency correction shown by the modified STORI method, the frequency-insensitive STORI method is more tolerant of the mismatch between the STORI frequency and the true frequency of the signal than the conventional STORI method. However, the frequency-insensitive STORI method may accumulate noise at a faster rate than the modified STORI method.

[0076] In the example 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 the trap region of a mass spectrometer. However, the modified STORI method and the frequency-insensitive STORI method may also be used to determine the m / z, charge z, and mass m of each ion in a population of ions simultaneously trapped and oscillating within the trap region of a mass spectrometer. In such an example, the modified STORI method or the frequency-insensitive STORI method is performed on each peak in the FT spectral peak generated from transient phenomena.

[0077] In some situations where multiple ions are mass-spectrated simultaneously, interference between adjacent signals in the FT spectrum results in a STORI plot with a step-like pattern that is difficult to interpret with a linear trend. Therefore, accurately determining the charge of an ion based on the STORI plot can be difficult. These interfering signals can be dealt with in various ways. In some examples, any adjacent signals in the FT spectrum that are closer than a threshold quantity (e.g., 100 Hz, 50 Hz, 25 Hz, etc.) are filtered and not used in the modified STORI method.

[0078] In another example, the signal of each individual FT peak within an FT spectral 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 spectral interval. This method determines the time-resolved amplitude and time-resolved oscillation frequency from the complex FT spectrum generated by multiple different ion species simultaneously trapped within the trap region. The time-resolved amplitude of the signal corresponding to each ion is proportional to the ion's charge state at each time point, and the time-resolved frequency correlates with the ion's m / z at each time point. Next, an exemplary implementation of this method will be described with reference to Figure 18.

[0079] Figure 18 shows an exemplary method 1800 for determining the time-resolved frequencies of FT components in an FT spectrum and determining the charge state of the ions represented by the FT components. Figure 18 shows exemplary operation according to one embodiment, but other embodiments may omit, add, rearrange, and / or modify any of the operations shown in Figure 18.

[0080] In operation 1802, transient phenomena are acquired for one or more ion species that are simultaneously trapped and oscillating within the trap region. The transient phenomena represent time-domain data (signal S) that are generated by the current induced on the detector by the oscillating motion of the ion collective within the trap region. n In some cases, the trap region is the trap region of an orbital electrostatic ion trap mass spectrometer (e.g., an Orbitrap® mass spectrometer).

[0081] In operation 1804, a Fourier transform (FT) spectrum is generated based on transient phenomena. For example, a complex-valued FT spectrum C n This is generated by performing a Fast Fourier Transform (FFT) on the measured transient without apodization or zero-padding.

[0082] In operation 1806, the spectral interval of the FT spectrum is selected for processing, and the selected spectral interval contains one or more FT components (e.g., peaks). The spectral interval of the FT spectrum is divided into multiple bins (FT bins) of fixed width. In some examples, the width of each FT bin is the reciprocal of the acquisition time of the transient. The selected spectral interval contains K FT bins, and equation c k ={C n0 ,...,C n0+K It can be expressed by}. In some examples, the selected spectral interval includes FT bins from K=32 to K=1024, but any other suitable number may be used. Preferably, K is a power of 2 to facilitate the FFT operations performed later in Method 1800. The spectral interval is small relative to the total width of the FT spectrum to facilitate processing. However, the spectral interval may be large or extend to the total width of the FT spectrum. The FT components contained in the spectral interval exceed the noise |C n This is the local maximum value of |.

[0083] In operation 1808, the frequency of each FT component within the spectral interval is estimated. The frequencies can be estimated by any suitable method. In some examples, the frequency of each FT component is estimated as the maximum value of the FT spectral amplitude. The maximum amplitude of each FT component corresponds to the frequency accuracy up to one FT bin |c k Since it is sufficient to find the FT bin with |, precise centroidization is not necessary. The P peak is the estimated centroid f (p) Assuming it is found at (p=1..P), where the frequency is defined by the FT bin. f (p)The integer values ​​of will correspond to the frequencies for which the Discrete Fourier Transform is defined.

[0084] In operation 1810, the FT spectrum is processed to determine the time-resolved frequencies of one or more FT components within the spectral interval, based on the separated contributions of one or more FT components to the spectral interval. The separated contributions of one or more FT components exclude the contributions of all other FT components within the spectral interval (or all other FT components with signal levels greater than a threshold). An exemplary method of performing operation 1810 is described in more detail below with reference to Figure 19.

[0085] In operation 1812, the charge states z of one or more ionic species corresponding to one or more FT components are determined based on the time-resolved frequencies of one or more FT components within the spectral interval. The charge states can be determined by any suitable method. In some examples, the charge states are determined based on the conventional STORI method or the modified STORI concept described above. For example, STORI data are obtained using the time-resolved STORI frequency ω(t n The time-resolved frequencies determined in operation 1810 may be used to generate the charge state z according to equations (1'), (2'), and (3). Other methods for determining the charge state z based on the time-resolved frequencies are described in more detail below.

[0086] In operation 1814, it is determined whether the processing of method 1800 is complete. In some examples, the processing of method 1800 is complete when the entire FT spectrum, or the range of interest of the FT spectrum, has been processed in operations 1806-1812. If it is determined that the processing is complete, method 1800 terminates. If it is determined that the processing is not complete, 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 different spectral widths. In some examples, spectral intervals that do not contain FT components that exceed a threshold level (e.g., exceeding the noise level or other minimum level) are not analyzed.

[0087] Figure 19 shows an exemplary method 1900 for performing operation 1810. While Figure 19 shows an exemplary operation according to one embodiment, other embodiments may omit, add, rearrange, and / or modify any of the operations shown in Figure 19.

[0088] In operation 1902, for each FT component p included in the spectral interval, the estimated frequency f (p) A centroid-centered filter (for example, a bell function filter) is applied as follows:

[0089]

number

[0090]

number

[0091] In operation 1904, a model transient is generated for each filtered FT component. The model transient for each FT component includes the amplitude and phase functions of the FT component. The model transient is generated from the filtered spectral data of each FT component.

[0092]

number

[0093]

number

[0094]

number

[0095] From iFT

[0096]

number

[0097]

number

[0098]

number

[0099] Phase function φ for each FT component p(t) is fitted. Preferably, the phase function is fitted in the form of a piecewise polynomial, and the breakpoint of the piecewise polynomial is the piecewise constant amplitude function a in operation 1904. (p) This is the same as the breakpoint determined for (t). An exemplary method for fitting the phase function is described in more detail below. Optionally, a piecewise constant amplitude function is used for the estimated phase.

[0100]

number

[0101]

number

[0102]

number

[0103] In operation 1906, the spectral interval c k Spectral contribution of each FT component to

[0104]

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[0105] In operation 1908, for each FT component, the spectral contribution of other FT components within the spectral interval is as follows: c k It is subtracted from.

[0106]

number

[0107] In operation 1910, it is determined whether the processing of method 1900 is complete. If the processing of method 1900 is not complete, method 1900 proceeds to operation 1912, then returns to operation 1902 to perform another iteration loop of operations 1902 - 1910. In operation 1912, the width w of the bell function is increased. If the processing of the method is complete, method 1900 proceeds to operation 1914. The iteration loop includes successive executions of operations 1902 - 1912 until it is determined in operation 1910 that the processing is complete (e.g., the 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 one FT component.

[0108] Method 1900 may 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 ranges from 1 to 20 with a filter width w that increases gradually. In other examples, the processing of method 1900 continues until the width w of the bell function reaches a threshold. In other examples, the processing of method 1900 continues until the time resolution meets a condition (e.g., reaches a threshold). The time resolution in each iteration is limited by the value δt ~ 1 / w according to the spectral uncertainty principle. In the first iteration, the filter width is selected to be small enough to avoid interference between adjacent FT components. Preferably, w is less than the minimum distance between f (p) and f (p+1) and. Thus, the filtered iFFT transient

[0109]

Number

[0110] If there is only one FT component with a spectral interval of P = 1, only a single execution from operations 1902 to 1908 may be used with a sufficiently wide filter function or without filtering, and thus operations 1910 and 1912 may be omitted.

[0111] In operation 1914, the time-resolved frequencies of one or more FT components within the spectral interval are determined based on the phase correlation function of the model transient of one or more FT components. As described above, the model amplitude of the FT component is evaluated as the piecewise constant amplitude function a p (t), and the model phase φ p (t) is determined at each interval of the piecewise constant amplitude function. The breakpoints between the constant values of a p (t) are interpreted as the instants when ions enter or leave the ensemble of trapped ions. The signal amplitude between adjacent breakpoints is interpreted as the number of elementary charges present in a particular FT component at a particular part of the signal acquisition time. For example, the signal amplitude may be proportional to or indicative of the ion charge state based on a known or predetermined relationship between the amplitude and the charge state. The intensity of the noise level is interpreted as the absence of ions at a particular time interval, for example when the ions are fragmented.

[0112] When the signal-to-noise ratio is small, the signal amplitude estimated from the absolute value of the iFT transient

[0113]

Number

[0114]

number

[0115] Model phase φ p (t) is the source of information regarding the time-resolved vibration frequency, and the original estimated frequency f (p) This may differ. The time-resolved frequency F of the FT component at time t. (p) This is determined based on the time-resolved frequency correction (based on the time derivative of the phase function) applied to the estimated frequency of each FT component, as follows:

[0116]

number

[0117] As described above, in some examples of operation 1812, the charge state z of the ionic species is determined using the time-resolved frequency obtained in operation 1912, based on the conventional STORI method or the modified STORI method described above. In other examples, the charge state of the ionic species is determined by the corresponding piecewise constant amplitude function a p Determined based on (t). As explained above, the piecewise constant amplitude function a between adjacent breakpoints of the FT component. p The signal amplitude of (t) is proportional to the charge state of an ionic species represented by a specific FT component in a particular portion of the signal acquisition time, or is otherwise related (e.g., based on known or empirically established relationships).

[0118] An exemplary method for fitting a piecewise constant amplitude function to a sequence of real data points, as referenced in operation 1904.

[0119]

number

[0120]

Equation

[0121]

Equation

[0122]

Equation

[0123] Since the first sum is constant, the breakpoints h1 < h2 <... < h I are determined to maximize the second sum.

[0124] For a fixed set of breakpoints, the amplitude on the interval is evaluated as follows.

[0125]

Equation

[0126]

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[0127] Figures 20A and 20B illustrate this method for fitting a piecewise constant amplitude function. Figure 20A shows a sequence of absolute values ​​of the amplitude of the model transient and an example of the fitted piecewise constant amplitude function, represented as line 2002. Figure 20B shows the penalty function versus trial values ​​for h1 and h2, represented as curves 2004 and 2006, respectively. The penalty function reveals a significant minimum at the correct breakpoint.

[0128] Next, we will describe an exemplary method of piecewise polynomial approximation of the phase function, as referenced in operation 1904. Polynomial function

[0129]

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[0130]

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[0131]

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[0132] Next, we will describe an exemplary method for correcting the signal amplitude in noisy signals. A certain time interval t = h i..h i+1 The true signal amplitude a that persists over time * Consider the following: Adaptation procedure a t =|a * +ζ k +iη k The signal point obtained by | is a * The absolute value of is σ 2 =2<ζ 2 >=2<η 2 >This is the result of adding a random noise component. Under the assumption of a white normal distribution noise, a t The determined mean is:

[0133]

number

[0134] The true amplitude is connected to the measured amplitude as follows: a * =g(snr)a,snr=a * / σ Here, g is the correction factor.

[0135]

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[0136] Methods 1800 and 1900 can be used to determine the time-resolved frequencies of any one or more FT components within a selected spectral interval and / or within an FT spectrum, or of all FT components within a selected spectral interval and / or within an FT spectrum. Similarly, m / z, charge state z, and mass m can be determined based on Methods 1800 and 1900 for any one or more ion species that are simultaneously trapped and oscillating in an ion trap mass spectrometer. Thus, the methods described herein can improve throughput compared to conventional CDMS techniques by enabling the simultaneous and accurate analysis of multiple ion species.

[0137] Next, an exemplary application of Methods 1800 and 1900 is described for the simulated analysis of Flockhouse virus (FHV) particles using an Orbitrap® mass spectrometer, with reference to Figures 22–28. Transient phenomena are acquired by the mass spectrometer and processed to generate FT spectra.

[0138] Figure 22 shows the FT spectrum 2200 divided into multiple FT bins along the frequency domain. A selected spectral interval 2202 of the FT spectrum 2200, with a width of 128 FT bins, is expanded to include three FT components 2204-1, 2204-2, and 2204-3 (collectively, FT component 2204). The amplitude of FT component 2204 is significantly above the noise level 2206. The FT spectrum 2200 has an absolute value of |c k It is shown in magnitude mode as |. Nevertheless, the complex number Re(c k )+iIm(c k ) is used in all calculations.

[0139] Figure 23 shows the spectral interval 2202 and the superimposed filter functions 2302-1, 2302-2, and 2302-3 (shown as dashed lines) of the FT components 2204 for each of the five different iterations 2300 of Method 1900 (e.g., iterations 2300-1 to 2300-5). (For clarity, reference numbers are shown only for the fourth iteration, 2300-4.) The widths w of the filter functions for each consecutive iteration 2300 are 3, 6, 10, 15, and 20 FT bins. As shown, the filter function 2302 is a Gauss-Bell function that is sufficiently narrow and does not significantly overlap.

[0140] Figure 24 shows the amplitude values ​​over transient acquisition of FT component 2204-1 calculated in operation 1904 for each iteration 2300, with a spectral interval of 2202.

[0141]

number

[0142] Figure 25 shows the amplitude values ​​over transient acquisition of FT component 2204-2 calculated in operation 1904 for each iteration 2300.

[0143]

number

[0144] Figure 26 shows the amplitude values ​​over transient acquisition of FT component 2204-3 calculated in operation 1904 for each iteration 2300, with a spectral interval of 2202.

[0145]

number

[0146] Figure 27A shows the spectral interval 2202, constant amplitude functions 2404, 2504, and 2604, and the adapted piecewise constant amplitude functions 2404, 2504, and 2604 for FT components 2204-1, 2204-2, and 2204-3 acquired in the last iteration (e.g., iteration 2300-5). The amplitude of FT component 2204-1 decreases from approximately 2.7 units to the noise level around the midpoint of the acquisition time interval, while the amplitude of FT component 2204-2 increases at the same instant. This behavior leads to the conclusion that FT components 2204-1 and 2204-2 originate from the same ion that collides with residual gas molecules and loses a neutral fragment. As a result of mass loss, the oscillation frequency increases. The amplitude of FT component 2204-3 remains constant throughout the entire acquisition period, leading to the conclusion that the ion's mass remains constant throughout the entire acquisition period. The signal amplitude is thought to be proportional to the charge of the ions involved. The fact that the amplitudes of FT components 2202-1 and 2202-2 are nearly equal (approximately 2.7 units) supports the hypothesis that FT component 2204-1 is generated by the same ions as FT component 2204-2 after the loss of neutral fragments.

[0147] Figure 27B shows the phase as a function of time and the fitted phase functions of FT components 2204-1, 2204-2, and 2204-3 obtained in the last iteration (e.g., iteration 2300-5). Oscillatory phase

[0148]

number

[0149] The time derivatives of the fitted phase functions are used to correct the estimated oscillation frequencies, as described above. Figure 28 shows plots 2800-1 and 2800-2 plotting the corrected frequencies as a function of time based on the time derivatives of the respective phase functions. Plot 2800-1 includes curve 2802-1 showing the time-resolved frequency of FT component 2202-1 and curve 2802-2 showing the time-resolved frequency of FT component 2204-2. Plot 2800-2 includes curve 2802-3 showing the time-resolved frequency of FT component 2204-3. For all three FT components 2204, the frequencies increase with time, probably due to continuous mass losses caused by desolvation. As shown by curves 2802-1 and 2802-2, there is a rapid frequency jump between FT components 2202-1 and 2202-2, which is probably due to losses of a larger neutral fragment.

[0150] As can be seen from the example described above with reference to FHV particles, the relative peak amplitude of the original FT spectrum 2200 (see Figure 22) does not represent the relative signal generated by the ion vibration due to spectral interference caused by adjacent FT components. Therefore, the peak amplitude of the original FT spectrum 2200 is not suitable for assigning the ion charge state. However, the actual instantaneous amplitude of the FT components, and thus the determination of the ion charge state, can be accurately determined by using the methods described herein. For example, the ion charge state can be determined based on a modified STORI method using time-resolved frequencies, as shown by curves 2802-1, 2802-2, and 2802-3 in Figure 28. Alternatively, the ion charge state can be determined based on the actual instantaneous amplitude represented by constant-amplitude functions 2404, 2504, and 2604, as shown in Figure 27A.

[0151] The methods described herein are also applicable to high-density spectra that cannot be effectively divided into non-coherent spectral intervals. In these scenarios, multiple spectral intervals may be selected in operation 1806 such that each FT component of interest belongs to at least one interval and its estimated centroid is located near the center of the interval (e.g., within a predetermined proportion of the interval's center frequency or a predetermined distance from the interval's center frequency). In some examples, the spectral intervals overlap along the frequency axis.

[0152] In operation 1906, the spectral contribution of each FT component

[0153]

number

[0154]

number

[0155] Figure 29 shows exemplary implementations of methods 1800 and 1900 using overlapping spectral intervals. Figure 29 shows a continuous FT spectrum 2900 with four detected FT components 2902-1, 2902-2, 2902-3, and 2902-4. Each spectral interval 2904 (e.g., spectral intervals 2904-1, 2904-2, 2904-3, or 2904-4) is associated with each FT component 2902 located in or near the center of spectral interval 2904. The Gaussian filter function applied to FT component 2902 is represented by dashed curves 2906-1 to 2906-4. The width of the Gaussian filter function varies with iteration. In the example in Figure 29, only the filtered model transient of FT component 2902 for the corresponding spectral interval 2904 is calculated, and its spectral contribution d to the corresponding spectral interval 2904 is calculated.k This is estimated. The spectral contribution of FT component 2902 is calculated on the associated spectral interval 2904 and subtracted from the shared portion of spectral data on overlapping spectral intervals 2904 using the corresponding index shift. Figure 29 shows the corrected spectrum 2908 for spectral interval 2904-1, calculated by subtracting the contributions of FT components 2902-2 and 2902-3 (of overlapping spectral intervals 2904-2 and 2904-3) from the contribution of FT component 2902-1.

[0156] Non-overlapping spectral intervals, such as spectral intervals 2904-1 and 2904-4, do not have direct interference. Therefore, the number of subtractions, which is twice the number of overlapping pairs, is P 2 It is proportional to P, not proportional to the number of units. This is beneficial for performance and enables efficient parallel computing.

[0157] As described herein, various additional benefits may be derived from the examples and principles described herein.

[0158] In some cases, an ion fragmentation pathway may be determined. For example, if a pair of FT spectral components is identified, and the first FT spectral component ends at a specific point in the signal acquisition period, and the second FT spectral component begins at substantially the same point, and both components have the same or similar amplitude, then the components may be generated by the same trapped ion that underwent fragmentation. In this case, the m / z difference between the FT components (determined from their centroids) provides information about the lost fragment, neutrality, or charge, and thus the fragmentation pathway can be determined (e.g., solvent loss or collision-induced fragmentation).

[0159] In another example, the rate of frequency drift can provide information about the molecular conformation of a trapped ion. Since the vibrational frequency is directly related to the m / z of the trapped ion, the frequency change over time detected by the method described herein provides an estimate of the ion's mass change over time. The mass loss occurs either by solvent evaporation or collision-induced loss of small fragments (usually neutral). Ions with larger outer surfaces are, hypothetically, more susceptible to the effects of both mass loss mechanisms. Thus, ion conformations with smaller or larger surfaces can be distinguished based on the rate of frequency drift. Furthermore, measuring the frequency drift of the residual gas under two or more different pressures can enable the distinction of mass loss mechanisms such as evaporation or collision-induced fragmentation.

[0160] As another example, frequency drift can also be determined in a simple way by the short-term (or time) Fourier transform, where the transient is divided into several shorter transients (parts), for example, via recursive bisection. For the smaller parts, the centroid and amplitude of the FT peak are determined, for example, by fitting a sinc model to the corresponding FT spectrum. Dividing the transient into smaller parts continues until the sinc model exhibits a certain fit quality, indicated, for example, by small residuals. The frequency / amplitude expansion is then obtained by piecewise interpolation of the values ​​determined in the transient parts. An example of a short-term 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):2129-2137.

[0161] One or more operations described herein, including any operation of methods 700, 800, 1600, 1800, and 1900, may be performed by a CDMS system. Figure 30 shows an exemplary CDMS system 3000 ("System 3000"). System 3000 may be implemented entirely or partially by a mass spectrometer (e.g., by a controller of the mass spectrometer), including any mass spectrometer described herein. Alternatively, System 3000 may be implemented separately from the mass spectrometer (e.g., a standalone computing system, a remote computing system, or a remote server communicatively coupled to the mass spectrometer by a network connection).

[0162] System 3000 may include, but is not limited to, a storage facility 3002 and a processing facility 3004, which are selectively and communicatively coupled to one another. Facilities 3002 and 3004 each include, or can be implemented by, hardware and / or software components (e.g., a processor, memory, a communication interface, instructions stored in memory for execution by the processor). In some examples, facilities 3002 and 3004 may be distributed among multiple devices and / or multiple locations to serve a particular implementation form.

[0163] The storage unit 3002 may maintain (e.g., store) executable data used by the processing unit 3004 to perform any of the operations described herein. For example, the storage unit 3002 may store instruction 3006 which can be executed by the processing unit 3004 to perform any of the operations described herein. Instruction 3006 can be implemented by any suitable application, software, code, and / or other executable data instance. The storage unit 3002 may also maintain any data that is acquired, received, generated, managed, used, and / or transmitted by the processing unit 3004.

[0164] Processing unit 3004 may be configured to perform various processing operations described herein (for example, to perform an instruction 3006 stored in storage unit 3002 for the purpose of performing the operation). It will be recognized that the operations and examples described herein are merely illustrative of the many different types of operations that may be performed by processing unit 3004. In this description, any reference to an operation performed by system 3000 may be understood as being performed by processing unit 3004 of system 3000. Furthermore, in this description, any operation performed by system 3000 may be understood to include system 3000 instructing or commanding another system or device to perform the operation.

[0165] 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 appropriately configured computing devices. For this purpose, one or more of the systems and / or components described above may include, or be implemented by, any computer hardware and / or instructions (e.g., software) embodied on at least one non-temporary computer-readable medium configured to execute one or more of the processes described herein. Specifically, a system component may be implemented on one physical computing device or on two or more physical computing devices. Thus, a system component may include any number of computing devices, but may employ any of several computer operating systems.

[0166] In certain embodiments, one or more of the processes described herein may be embodied in a non-temporary computer-readable medium and executed at least partially as instructions that can be executed by one or more computing devices. Generally, a processor (e.g., a microprocessor) receives and executes instructions from a non-temporary computer-readable medium (e.g., memory), thereby executing one or more processes, including one or more of the processes described herein. Such instructions may be stored and / or transmitted using any of a variety of known computer-readable media.

[0167] Computer-readable media (also called processor-readable media) include any non-temporary media involved in providing data (e.g., instructions) that can be read by a computer (e.g., by the computer's processor). Such media can take many forms, including but not limited to non-volatile media and / or volatile media. Non-volatile media may include, for example, optical or magnetic disks and other persistent memory. Volatile media may include, for example, dynamic random access memory ("DRAM"), which typically constitutes main memory. Common forms of computer-readable media include, for example, disks, hard disks, magnetic tapes, any other magnetic media, compact disc read-only memory ("CD-ROM"), digital video discs ("DVD"), any other optical media, random access memory ("RAM"), programmable read-only memory ("PROM"), erasable programmable read-only memory ("EPROM"), flash-eep-rom, any other memory chips or cartridges, or any other tangible media that a computer can read.

[0168] Figure 31 shows an exemplary computing device 3100 that may be specifically configured to perform one or more of the processes described herein. As shown in Figure 31, the computing device 3100 may include a communication interface 3102, a processor 3104, a storage device 3106, and an input / output ("I / O") module 3108, all connected to each other via a communication infrastructure 3110. While Figure 31 shows an exemplary computing device 3100, the components illustrated in Figure 31 are not intended to be limiting. Additional or alternative components may be used in other embodiments. The components of the computing device 3100 shown in Figure 31 will be described in further detail thereafter.

[0169] The communication interface 3102 may be configured to communicate with one or more computing devices. Examples of the communication interface 3102 include, but are not limited to, wired network interfaces (such as network interface cards), wireless network interfaces (such as wireless network interface cards), modems, audio / video connections, and any other suitable interfaces.

[0170] The processor 3104 generally represents any type or form of processing unit capable of processing data and / or interpreting, executing, and / or directing the execution of one or more instructions, processes, and / or operations as described herein. The processor 3104 may perform operations by executing computer executable instructions 3112 (e.g., applications, software, code, and / or other executable data instances) stored in the storage device 3106.

[0171] The storage device 3106 may include one or more data storage media, devices, or configurations, and may employ any type, form, and combination of data storage media and / or devices. For example, the storage device 3106 may include any combination of non-volatile media and / or volatile media described herein, but is not limited to the following. Electronic data, including the data described herein, may be stored temporarily and / or permanently within the storage device 3106. For example, data representing a computer executable instruction 3112 configured to instruct a processor 3104 to perform one of the operations described herein may be stored within the storage device 3106. In some examples, the data may be located in one or more databases residing within the storage device 3106.

[0172] The I / O module 3108 may include one or more I / O modules configured to receive user input and provide user output. One or more I / O modules may be used to receive input for a single virtual experience. The I / O module 3108 may include any hardware, firmware, software, or combination thereof that supports input and output capabilities. For example, the I / O module 3108 may include hardware and / or software for capturing user input, including, but not limited to, a keyboard or keypad, a touchscreen component (e.g., a touchscreen display), a receiver (e.g., an RF or infrared receiver), a motion sensor, and / or one or more input buttons.

[0173] The I / O module 3108 may include, but is not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers, one or more devices for presenting output to the user. In certain embodiments, the I / O module 3108 is configured to provide graphical data to the display for presentation to the user. The graphical data may represent one or more graphical user interfaces and / or any other graphical content, as may be useful for a particular implementation.

[0174] In some examples, any of the systems, computing devices, and / or other components described herein may be implemented by computing device 3100. For example, storage equipment 3002 may be implemented by storage device 3106, and processing equipment 3004 may be implemented by processor 3104.

[0175] In the above description, various exemplary embodiments have been described with reference to the accompanying drawings. However, various modifications and changes can be made and additional embodiments can be implemented without departing from the scope of the invention as described in the following claims. For example, certain features of one embodiment described herein can be combined with or replaced by features of another embodiment described herein. Therefore, this specification and the drawings should be considered illustrative, not restrictive.

[0176] The advantages and features of this disclosure can be further illustrated by the following embodiments.

[0177] Example 1. A non-temporary computer-readable medium for storing instructions, wherein, when executed, the instructions are transmitted to at least one processor of a computing device for mass spectrometry to acquire a time-varying signal representing a current induced on a detector by the vibrational motion of ions in a trap region, process the time-varying signal to derive the frequency of the vibrational motion, and process the STORI signal against time according to equations (1) and (2). real STORI for values ​​and time imag This involves generating selective temporal overview (STORI) data of resonant ions that represent values, STORY real (t n ) = S(t n ) * cos(ω * t n )+STORY real (t n-1 ) (1) STORY imag (t n ) = -S(t n ) * sin(ω * t n )+STORY imag (t n-1 ) (2) In the formula, S(t n A non-temporal, computer-readable medium instructing a process to be carried out, which includes: ) being the amplitude of a time-varying signal, ω being the derived frequency of the oscillation; regenerating STORI data based on the variation in the frequency of the oscillation over time; and determining the charge state of the ions based on the regenerated STORI data.

[0178] Example 2. The non-temporary computer-readable medium according to Example 1, wherein regenerating STORI data includes determining frequency correction as a function of time with respect to derived frequencies, and regenerating STORI data based on the derived frequencies and frequency correction.

[0179] Example 3. STORI data is regenerated according to formulas (1') and (2'), STORYreal (t n ) = S(t n ) * cos(ω(t n ) * t n )+STORY real (t n-1 ) (1') STORY imag (t n ) = -S(t n ) * sin(ω( t n ) * t n )+STORY imag (t n-1 ) (2') In the formula, ω(t n The non-temporal computer-readable medium described in Example 2 is characterized by the frequency of the oscillating motion as a function of time, determined based on the derived frequency and frequency correction as a function of time.

[0180] Example 4. A non-temporary computer-readable medium according to Example 2 or 3, further comprising determining the m / z of an ion at a specific time during the acquisition of a time-varying signal based on the derived frequency and frequency correction.

[0181] Example 5. The non-temporary computer-readable medium according to Example 4, further comprising determining the mass of an ion at a specific time based on the charge state of the ion and the m / z of the ion at a specific time.

[0182] Example 6. Determining frequency correction as a function of time involves generating δSTORI data based on STORI data, where δSTORI data represents the time derivative of STORI data, and generating δSTORI phase data based on δSTORI data and according to equation (4), where δSTORI phase angle θδSTORI represents the temporal variation of δSTORI phase angle.

[0183]

number

[0184] Example 7. Determining the charge state of ions based on regenerated STORI data is done according to equation (3), based on the regenerated STORI data, STORI with respect to time mag Determining the value,

[0185]

number

[0186] Example 8. The process further includes determining the lifetime of the ion based on the regenerated STORI data, and determining the charge state of the ion during the STORI lifetime. mag (t n A non-temporary computer-readable medium according to Example 7, based on the slope of ).

[0187] Example 9. A system for determining the charge state of an ion, comprising one or more processors and a memory for storing executable instructions, wherein, when executed by one or more processors, the system instructs a computing device to acquire a time-varying signal representing a current induced on a detector by the oscillating motion of an ion in a trap region, to process the time-varying signal to derive the frequency of the oscillating motion, and to determine the STORI of time according to equations (1) and (2). real STORI for values ​​and time imag This involves generating selective temporal overview (STORI) data of resonant ions that represent values, STORY real (t n ) = S(t n ) * cos(ω * t n )+STORY real (t n-1 ) (1) STORY imag (t n ) = -S(t n ) * sin(ω * t n )+STORY imag (t n-1 ) (2) In the formula, S(t n A system is instructed to perform a process that includes: ω being the amplitude of a time-varying signal, ω being the derived frequency of the oscillation; regenerating STORI data based on the variation in the frequency of the oscillation over time; and determining the charge state of the ions based on the regenerated STORI data.

[0188] Example 10. The system according to Example 9, wherein regenerating STORI data includes determining frequency correction as a function of time with respect to derived frequencies, and regenerating STORI data based on derived frequencies and frequency correction.

[0189] Example 11. STORI data is regenerated according to formulas (1') and (2'), STORY real (t n ) = S(t n ) * cos(ω(t n ) * t n )+STORY real (t n-1 ) (1') STORY imag (t n ) = -S(t n ) * sin(ω( t n ) * t n )+STORY imag (t n-1 ) (2') In the formula, ω(t n The system according to Example 10, wherein ) is the frequency of the oscillating motion as a function of time determined based on the derived frequency and time-based frequency correction.

[0190] Example 12. The system according to Example 10 or 11, further comprising determining the m / z of the ions at a specific time during the acquisition of a time-varying signal based on the derived frequency and frequency correction.

[0191] Example 13. The system according to Example 12, further comprising determining the mass of an ion at a specific time based on the charge state of the ion and the m / z of the ion at a specific time.

[0192] Example 14. Determining frequency correction as a function of time involves generating δSTORI data based on STORI data, where δSTORI data represents the time derivative of STORI data, and based on δSTORI data and according to equation (4), the δSTORI phase angle θ δSTORI This involves generating δSTORI phase data that represents the temporal fluctuations,

[0193]

number

[0194] Example 15. Determining the charge state of ions based on regenerated STORI data is done according to equation (3), based on the regenerated STORI data, with respect to STORI over time. mag Determining the value,

[0195]

number

[0196] Example 16. The process further includes determining the lifetime of the ion based on the regenerated STORI data, and determining the charge state of the ion during the STORI lifetime. mag (t n The system described in Example 15, based on the slope of ).

[0197] Example 17. A system for performing charge detection mass spectrometry, comprising an ion trap mass spectrometer that traps ions within a trap region and establishes a trap electric field within the trap region that causes the ions to undergo oscillatory motion, and a computing system configured to perform a process, the process comprising acquiring a time-varying signal representing a current induced on a detector by the oscillatory motion of ions within the trap region, processing the time-varying signal to derive the frequency of the oscillatory motion, and calculating the STORI of time according to equations (1) and (2). real STORI for values ​​and time imag This involves generating selective temporal overview (STORI) data of resonant ions that represent values,

[0198] STORY real (t n ) = S(t n ) * cos(ω * t n )+STORY real (t n-1 ) (1) STORY imag (t n ) = -S(t n ) * sin(ω * t n )+STORY imag (t n-1 ) (2) In the formula, S(t n A system is instructed to perform a process that includes: ω being the amplitude of a time-varying signal, ω being the derived frequency of the oscillation; regenerating STORI data based on the variation in the frequency of the oscillation over time; and determining the charge state of the ions based on the regenerated STORI data.

[0199] Example 18. The ion trap mass spectrometer is the system described in Example 17, comprising an orbital electrostatic ion trap mass spectrometer.

[0200] Example 19. A non-temporary computer-readable medium for storing instructions, wherein, when executed, the instructions are transmitted to at least one processor of a computing device for mass spectrometry to obtain a time-varying signal representing a current induced on a detector by the vibrational motion of ions in a trap region, process the time-varying signal to derive the frequency of the vibrational motion, and, according to equation (8), STORI mag This involves generating selective temporal overview (STORI) data of resonant ions that represent values,

[0201]

number

[0202] Example 20. Determining the charge state of an ion is possible with STORI mag (t n The slope of ) and the ionic charge and STORI mag (t n A non-temporary computer-readable medium according to Example 19, based on the relationship between the slope of ) and the other.

[0203] Example 21. A non-temporary computer-readable medium for storing instructions, the instructions, when executed, instruct at least one processor of a computing device for mass spectrometry to carry out a process comprising: acquiring transient phenomena of one or more ionic species trapped and oscillating within a trap region; generating a Fourier transform (FT) spectrum based on the transient phenomena; selecting a spectral interval within the FT spectrum that contains one or more FT components; estimating the frequency of each FT component within the spectral interval; processing the FT spectrum to determine the time-resolved frequencies for the FT components within the spectral interval based on the separated contributions of the FT components to the spectral interval; and determining the charge state z of the ionic species corresponding to the FT components based on the time-resolved frequencies of the FT components within the spectral interval.

[0204] Example 22. The computer-readable medium according to Example 21, further comprising determining the mass-to-charge ratio (m / z) of the ionic species corresponding to the FT component based on the time-resolved frequency of the FT component.

[0205] Example 23. The computer-readable medium according to Example 21, wherein the time-resolved frequency of 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.

[0206] Example 24. A computer-readable medium according to Example 21, wherein processing an FT spectrum to determine the time-resolved frequencies of FT components comprises: generating filtered spectral data by applying a filter of width w to each FT component within a spectral interval based on the estimated frequency of each FT component; generating a model transient for each FT component within a spectral interval based on the filtered spectral data, the model transient includes an amplitude function and a phase function; determining the spectral contribution of each FT component to the spectral interval based on the amplitude function and phase function of the FT component; subtracting the spectral contribution of other FT components within the spectral interval for each FT component; and determining the time-resolved frequencies of the FT components within a spectral interval based on the phase function of the model transient.

[0207] Example 25. The computer-readable medium described in Example 24, wherein generating a model transient includes performing an inverse Fourier transform on filtered spectral data.

[0208] Example 26. The amplitude function is a piecewise constant amplitude function, as described in Example 24 for the computer-readable medium.

[0209] Example 27. The computer-readable medium according to Example 26, wherein the piecewise constant amplitude function has multiple breakpoints.

[0210] Example 28. Determining the charge state z of an ionic species is based on the amplitude of a piecewise constant amplitude function between adjacent breakpoints, as described in Example 27, for a computer-readable medium.

[0211] Example 29. The computer-readable medium described in Example 24, wherein the phase function includes a piecewise polynomial function.

[0212] Example 30. A computer-readable medium according to Example 24, wherein determining the spectral contribution of each FT component to the spectral interval includes performing FT on a model transient for each FT component based on the amplitude function and phase function for each respective FT component.

[0213] Example 31. A computer-readable medium according to Example 24, further comprising iteratively determining the width of the filter.

[0214] Example 32. A computer-readable medium according to Example 31, wherein the iterative loop includes applying a filter to each FT component within a spectral interval, generating a 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 iteratively determining the width of the filter, and the iterative loop is carried out until the iterative loop conditions are met.

[0215] Example 33. The computer-readable medium described in Example 32, wherein the iterative loop condition is met when the time resolution reaches a threshold.

[0216] Example 34. The computer-readable medium described in Example 32, wherein the iterative loop condition includes performing an iterative loop of a threshold number of iterations.

Claims

1. A non-temporary computer-readable medium for storing instructions, wherein, when executed, the instructions are transmitted to at least one processor of a computing device for mass spectrometry. To acquire a time-varying signal representing the current induced on the detector by the vibrational motion of ions within the trap region, Processing the time-varying signal in order to derive the frequency of the aforementioned vibration motion, According to equations (1) and (2), STORI with respect to time ｒｅａｌ STORI for values ​​and time ｉｍａｇ This involves generating selective temporal overview (STORI) data of resonant ions that represent values, STORI ｒｅａｌ (t ｎ )=S(t ｎ ) ＊ cos(ω ＊ t ｎ )+STORI ｒｅａｌ (t ｎ－１ ) (1) STORI ｉｍａｇ (t ｎ )=-S(t ｎ ) ＊ sin(ω ＊ t ｎ )+STORI ｉｍａｇ (t ｎ－１ ) (2) In the formula, S(t ｎ ) is the amplitude of the time-varying signal, and ω is the derived frequency of the vibrational motion, Regenerating the STORI data based on the fluctuation of the frequency of the vibrational motion over time, A non-temporary computer-readable medium that instructs a process to be carried out, which includes determining the charge state of the ion based on the regenerated STORI data.

2. Regenerating the aforementioned STORI data is The frequency correction is determined as a function of time with respect to the derived frequency, A non-temporary computer-readable medium according to claim 1, comprising regenerating the STORI data based on the derived frequency and the frequency correction.

3. The STORI data is regenerated according to equations (1') and (2'), STORI ｒｅａｌ (t ｎ )=S(t ｎ ) ＊ cos(ω(t ｎ ) ＊ t ｎ )+STORI ｒｅａｌ (t ｎ－１ ) (1’) STORI ｉｍａｇ (t ｎ )=-S(t ｎ ) ＊ sin(ω(t ｎ ) ＊ t ｎ )+STORI ｉｍａｇ (t ｎ－１ ) (2’) In the formula, ω(t ｎ The non-temporal computer-readable medium according to claim 2, wherein ) is the frequency of the oscillating motion as a function of time determined based on the frequency correction as a function of frequency and time derived.

4. The non-temporary computer-readable medium according to claim 2, further comprising determining the m / z of the ion at a specific time during the acquisition of the time-varying signal based on the derived frequency and the frequency correction.

5. The non-temporary computer-readable medium according to claim 4, further comprising determining the mass of the ion at the specific time based on the charge state of the ion and the m / z of the ion at the specific time.

6. Determining the frequency correction as a function of time means The process involves generating δSTORI data based on the STORI data, wherein the δSTORI data represents the time derivative of the STORI data. Based on the δSTORI data and according to equation (4), the δSTORI phase angle θ δＳＴＯＲＩ This involves generating δSTORI phase data that represents the temporal variation of, [Math 1] In the formula, δSTORI ｒｅａｌ (t ｎ ) is STORI ｒｅａｌ (t ｎ The aforementioned time derivative of ) and δSTORI ｉｍａｇ (t ｎ ) is STORI ｉｍａｇ (t ｎ The above is the time derivative of ) and θ as a function of time δＳＴＯＲＩ (t ｎ A non-temporary computer-readable medium according to claim 2, comprising determining the frequency correction as a function of time based on the slope of ).

7. Determining the charge state of the ion based on the regenerated STORI data is: According to formula (3), based on the regenerated STORI data, STORI with respect to time ｍａｇ Determining the value, [Math 2] STORY ｍａｇ (t ｎ The slope of ) and the ionic charge and STORI ｍａｇ (t ｎ A non-temporary computer-readable medium according to claim 1, comprising determining the charge state of the ion based on the relationship between the slope of the ion and the relationship between the ion and the ion.

8. The process further includes determining the lifetime of the ion based on the regenerated STORI data, Determining the charge state of the ion is STORI ｍａｇ (t ｎ A non-temporary computer-readable medium according to claim 7, based on the aforementioned inclination of ).

9. A system for determining the charge state of ions, One or more processors, A memory for storing executable instructions, wherein when an executable instruction is executed by one or more processors, it is transmitted to a computing device. To acquire a time-varying signal representing the current induced on the detector by the vibrational motion of ions within the trap region, Processing the time-varying signal in order to derive the frequency of the aforementioned vibration motion, According to equations (1) and (2), STORI with respect to time ｒｅａｌ STORI for values ​​and time ｉｍａｇ This involves generating selective temporal overview (STORI) data of resonant ions that represent values, STORI ｒｅａｌ (t ｎ )=S(t ｎ ) ＊ cos(ω ＊ t ｎ )+STORI ｒｅａｌ (t ｎ－１ ) (1) STORI ｉｍａｇ (t ｎ )=-S(t ｎ ) ＊ sin(ω ＊ t ｎ )+STORI ｉｍａｇ (t ｎ－１ ) (2) In the formula, S(t ｎ ) is the amplitude of the time-varying signal, and ω is the derived frequency of the vibrational motion, Regenerating the STORI data based on the fluctuation of the frequency of the vibrational motion over time, A system that instructs a process to be carried out, which includes determining the charge state of the ions based on the regenerated STORI data.

10. Regenerating the aforementioned STORI data is The frequency correction is determined as a function of time with respect to the derived frequency, The system according to claim 9, comprising regenerating the STORI data based on the derived frequency and the frequency correction.

11. The STORI data is regenerated according to equations (1') and (2'), STORI ｒｅａｌ (t ｎ )=S(t ｎ ) ＊ cos(ω(t ｎ ) ＊ t ｎ )+STORI ｒｅａｌ (t ｎ－１ ) (1’) STORI ｉｍａｇ (t ｎ )=-S(t ｎ ) ＊ sin(ω(t ｎ ) ＊ t ｎ )+STORI ｉｍａｇ (t ｎ－１ ) (2’) In the formula, ω(t ｎ The system according to claim 10, wherein ) is the frequency of the oscillating motion as a function of time determined based on the frequency correction as a function of frequency and time derived.

12. The system according to claim 10, further comprising determining the m / z of the ion at a specific time during the acquisition of the time-varying signal based on the derived frequency and the frequency correction.

13. The system according to claim 12, further comprising determining the mass of the ion at a particular time based on the charge state of the ion and the m / z of the ion at a particular time.

14. Determining the frequency correction as a function of time means The process involves generating δSTORI data based on the STORI data, wherein the δSTORI data represents the time derivative of the STORI data. Based on the δSTORI data and according to equation (4), the δSTORI phase angle θ δＳＴＯＲＩ This involves generating δSTORI phase data that represents the temporal variation of, [Math 3] where δSTORI ｒｅａｌ (t ｎ ) is the time derivative of STORI ｒｅａｌ (t ｎ ), and δSTORI ｉｍａｇ (t ｎ ) is the time derivative of STORI ｉｍａｇ (t ｎ ), and that θ as a function of time δＳＴＯＲＩ (t ｎ The system according to claim 10, comprising determining the frequency correction as a function of time based on the slope of ).

15. Determining the charge state of the ion based on the regenerated STORI data is: According to formula (3), based on the regenerated STORI data, STORI with respect to time ｍａｇ Determining the value, [Math 4] STORY ｍａｇ (t ｎ The slope of ) and the ionic charge and STORI ｍａｇ (t ｎ The system according to claim 9, comprising determining the charge state of the ion based on the relationship between the slope of the ion and the relationship between the ion and the slope of the ion.

16. The process further includes determining the lifetime of the ion based on the regenerated STORI data, Determining the charge state of the ion is STORI ｍａｇ (t ｎ The system according to claim 15, based on the aforementioned inclination of ).

17. A system for performing charge detection mass spectrometry, wherein the system is An ion trap mass spectrometer that traps ions within a trap region and establishes a trap electric field within the trap region that causes the ions to undergo oscillating motion, A computing system configured to carry out a process, wherein the process is To acquire a time-varying signal representing the current induced on the detector by the vibrational motion of ions within the trap region, Processing the time-varying signal in order to derive the frequency of the aforementioned vibration motion, According to equations (1) and (2), STORI with respect to time ｒｅａｌ STORI for values ​​and time ｉｍａｇ This involves generating selective temporal overview (STORI) data of resonant ions that represent values, STORI ｒｅａｌ (t ｎ )=S(t ｎ ) ＊ cos(ω ＊ t ｎ )+STORI ｒｅａｌ (t ｎ－１ ) (1) STORI ｉｍａｇ (t ｎ )=-S(t ｎ ) ＊ sin(ω ＊ t ｎ )+STORI ｉｍａｇ (t ｎ－１ ) (2) In the formula, S(t ｎ ) is the amplitude of the time-varying signal, and ω is the derived frequency of the vibrational motion, Regenerating the STORI data based on the fluctuation of the frequency of the vibrational motion over time, A system comprising determining the charge state of the ion based on the regenerated STORI data.

18. The system according to claim 17, wherein the ion trap mass spectrometer comprises an orbital electrostatic ion trap mass spectrometer.

19. A non-temporary computer-readable medium for storing instructions, wherein, when executed, the instructions are transmitted to at least one processor of a computing device for mass spectrometry. To acquire a time-varying signal representing the current induced on the detector by the vibrational motion of ions within the trap region, Processing the time-varying signal in order to derive the frequency of the aforementioned vibration motion, According to equation (8), STORI with respect to time ｍａｇ This involves generating selective temporal overview (STORI) data of resonant ions that represent values, [Math 5] In the formula, time t ｎ STORI in ｒｅａｌ (t ｎ ) and STORI ｉｍａｇ (t ｎ The value of ) is determined according to equations (6) and (7), STORI ｒｅａｌ (t ｎ )=S(t ｎ ) ＊ cos(ω ＊ t ｎ ) (6) STORI ｉｍａｇ (t ｎ )=-S(t ｎ ) ＊ sin(ω ＊ t ｎ ) (7) In the formula, S(t ｎ ) is the amplitude of the time-varying signal, and ω is the derived frequency of the vibrational motion, A non-temporary computer-readable medium that instructs a process to be carried out, including determining the charge state of the ion based on the STORI data.

20. Determining the charge state of the ion is done by STORI ｍａｇ (t ｎ The slope of ) and the ionic charge and STORI ｍａｇ (t ｎ A non-temporary computer-readable medium according to claim 19, based on the relationship between the aforementioned inclination of ) and the aforementioned inclination.

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