Custom scaled mass defect diagram with filtering and labeling

Abstract

A method for determining mass defect diagrams with user-defined mass scaling, filtering, and labeling in a mass spectrometer is described. One implementation of the method includes: (i) generating a mass defect diagram from the data, (ii) filtering out all ions in the mass defect diagram that do not have an assigned isotopic ion, (iii) selecting an unidentified ion, (iv) determining an isotopic pattern for the unidentified ion, (v) identifying one or more elements indicated by the isotopic pattern for the unidentified ion, (vi) searching for formulas containing one or more elements indicated by the isotopic pattern for the unidentified ion, (vii) determining a chemical formula for the identified ion, and (viii) displaying a chemical formula for the unidentified ion.

Description

TECHNICAL AREA

[0001] This application relates to the generation of mass defect diagrams with user-defined mass scaling, filtering, and labeling. BACKGROUND

[0002] Mass spectrometry (MS) is an analytical technique used to determine the mass of an ion in order to interpret information about a compound, such as elucidating the chemical structures of molecules, including small metabolites and other chemical compounds. Mass spectrometry typically involves ionizing chemical compounds to generate charged molecules or molecular fragments and then measuring their mass-to-charge ratio. In a typical MS procedure, a sample charged onto a mass spectrometer is vaporized, ionizing the sample's components into charged particles (ions). The ions are typically accelerated by an electric field to calculate the mass-to-charge ratio (m / z) of the particles based on information about the ions' motion as they move through electromagnetic fields.The ions can be sorted and identified by a mass analyzer according to their mass-to-charge ratio (m / z) by measuring the value of an indicator quantity and providing data to calculate the abundance of each ion present. The calculated mass of each ion can change or deviate during the operation of the mass spectrometer due to various factors.

[0003] Each isotope has a defined mass defect depending on its relative nuclear binding energy to carbon-12. Each nuclide has a different mass defect, and each molecule of a specific elemental composition has a characteristic mass unique to that composition. The mass defect is determined by calculating the difference between the exact mass of the isotope in question and its standard integer mass. The specific mass defect can be used to aid in identifying the exact chemical formula. This application presents a method for filtering and labeling specific isotopes and chemical compounds according to their mass defects, based on an accurate mass determination.US Patent 2007 / 0114373 A1 relates to a method for determining an empirical formula of an analyte ion from a measured mass spectrum comprising a main peak and one or more isotopic peaks, wherein the method includes comparing a relative isotopic intensity of the measured isotopic peak with a calculated relative isotopic intensity of an isotopic ion of a proposed empirical formula and comparing a relative mass defect of the measured isotopic peak with a calculated relative mass defect of the isotopic ion of the proposed empirical formula, wherein the proposed empirical formula is identified as a potential candidate for the analyte ion on the basis of these comparisons.According to US Patent 7,653,493 B1, ion mass spectrometry data are collected for a group of samples, with at least one grouping of ions being identified for each sample and used to generate another estimated grouping of ions with respect to the sample, and using these groupings, properties of the sample are determined. US Patent 8,399,827 B1 describes methods that can be used in connection with mass spectrometry, such as mass spectrometry analysis, mass spectrometry calibration, identification of proteins / peptides by mass spectrometry, and / or strategies for acquiring mass spectrometry data, and discloses a phase modeling analysis method for identifying proteins or peptides by mass spectrometry.In US 2007 / 0164207 A1, a mass spectrometer comprises an ion source for emitting a plurality of ions from a sample together with a detector positioned downstream of the ion source and configured to detect the impact of the emitted ions on the detector, the mass spectrometer further comprising a control unit operatively coupled to the detector and the ion source and configured to calculate the m / z for each detected ion, the control unit comprising a mass defect filter configured to determine whether the m / z value for each detected ion falls within a predetermined mass defect range. SUMMARY

[0004] One aspect of the application is a method for generating a filtered mass defect diagram based on accurate mass data acquired by a mass spectrometer. In one implementation, the filtered mass defect diagram may be a halogen-filtered mass defect diagram. The method comprises generating a mass defect diagram from the mass spectrometer data, filtering out all ions in the mass defect diagram that do not have a corresponding confirmatory isotope (e.g., an M+2 or M-2 ion), selecting an unidentified ion, and determining an isotopic pattern for the unidentified ion. The method further comprises identifying one or more elements that indicate or suggest the unidentified ion, searching for formulas containing one or more elements that indicate or suggest the isotopic pattern for the unidentified ion.Indicating, determining a chemical formula for the unidentified ion and displaying the chemical formula for the unidentified ion on a display, receiving a user selection of an ion; and displaying the selected ion as an extracted ion chromatogram ± mass tolerance; and identifying one or more chromatographic peak(s) according to the extracted ion chromatogram ± mass tolerance.

[0005] Implementations of the disclosure may include one or more of the following optional features. The method includes receiving a user selection of an ion, displaying the selected ion as an extracted ion chromatogram ± mass tolerance, and identifying one or more chromatographic peaks corresponding to the extracted ion chromatogram ± mass tolerance. The method may also include identifying homologous series and RDBE-related species associated with the unidentified ion. The homologous series may further include chlorine and / or bromine. The RDBE-related species may include deuterium and / or hydrogen.

[0006] In some examples, the data are raw data from the mass spectrometer. The data may be deconvoluted data from a mass spectrometer. The procedure may also include labeling the chemical formulas for the unidentified ion on the display. Furthermore, the procedure may include assigning a color to the unidentified ion on the display.

[0007] In some implementations, the mass defect diagram is a mass defect diagram in which hydrogen is substituted by chlorine (Cl-H mass defect diagram). The mass defect diagram can also be a mass defect diagram in which hydrogen is substituted by bromine (Br-H mass defect diagram). Filtering all ions in the mass defect diagram that do not have an associated confirmatory isotope (e.g., an M+2 or M-2 ion) can further include filtering the mass defect with a specified tolerance and relative abundance. Filtering all ions in the mass defect diagram that do not have an associated M+2 or M-2 ion can also include filtering all ions that do not correspond to Br. x -isotope patterns, where x is an integer from 1 to 15 inclusive. Filtering all ions in the mass defect diagram that do not have an associated M+2 or M-2 ion can also include filtering all ions that do not correspond to Cl. ycorrespond to isotopic patterns, where y is an integer from 1 to 15 inclusive. In some examples, filtering all ions in the mass defect diagram that do not have an associated M+2 or M-2 ion includes filtering all ions that do not correspond to Br. x Cl y -Isotope patterns correspond. Filtering out all ions in the mass defect diagram that do not have a corresponding confirming isotope may further include filtering by determining a distance tolerance.

[0008] The distance tolerance can be based on a static m / z distance between the first and second signals. Alternatively, the distance tolerance can be based on a statistical m / z confidence interval determined from the number of ions in the first signal and the number of ions in the second signal. The distance tolerance can be based on a statistical m / z confidence interval of approximately 2.8. Finally, the distance tolerance can be limited by user input.

[0009] In some examples, filtering all ions in the mass defect diagram that do not have a corresponding confirmatory isotope involves filtering by relative abundance. The relative abundance can be determined for M+1 signals. Determining the relative abundance for M+1 signals may further involve determining a maximum predicted number of an M+1 element based on the intensity of a putative M+1 signal, the intensity of a putative monoisotopic signal, and the terrestrial natural abundance of the M+1 element. The M+1 element can be carbon, nitrogen, silicon, or any other element with a naturally occurring M+1 isotope.

[0010] Determining the relative frequency of M+2 signals can involve determining a maximum predicted number of an M+2 element based on the intensity of a putative monoisotopic signal, the intensity of a putative M+2 signal, and the terrestrial natural frequency of the M+2 element. In some examples, determining the relative frequency of M+2 signals further involves determining a maximum predicted number of M+2 elements based on the intensity of a putative monoisotopic signal, the intensity of a putative M+2 signal, the intensity of a putative M+4 signal, and the terrestrial natural frequency of the M+2 element.Determining the relative frequency of M+2 signals can also include determining a maximum predicted number of an M+2 element based on the intensity of a putative monoisotopic signal, the intensity of a putative M+4 signal, the intensity of a putative M+6 signal, and the terrestrial natural frequency of the M+2 element. Furthermore, determining the relative frequency of M+2 signals can include determining a maximum predicted number of an M+2 element based on the intensity of a putative monoisotopic signal, the intensity of a putative M+6 signal, the intensity of a putative M+8 signal, and the terrestrial natural frequency of the M+2 element.In some implementations, determining the relative abundance for M+2 signals involves determining whether one or more analytes contain both chlorine and bromine, and if the one or more analytes contain both chlorine and bromine, determining a maximum predicted number of an M+2 element based on the terrestrial natural abundance of . 37 Cl and the terrestrial natural abundance of 81 Br.

[0011] Another aspect of the application provides a method for generating a filtered mass defect diagram based on accurate mass data acquired by a mass spectrometer. The device comprises a display, data processing hardware communicating with the display, and storage hardware communicating with the data processing software. The storage hardware stores instructions which, when executed on the data processing hardware, cause the data processing hardware to perform operations. These operations include generating a mass defect diagram from the data obtained from a mass spectrometer, filtering out all ions in the mass defect diagram that do not have a corresponding confirmatory isotope, selecting an unidentified ion, and determining an isotopic pattern for an isotopic cluster of the unidentified ion.The operations also include identifying one or more elements contained in the isotopic pattern for the unidentified ion, searching for formulas that contain one or more elements identified by the isotopic pattern for the unidentified ion, determining a chemical formula of the isotopic cluster relating to the unidentified ion, and displaying the chemical formulas for the unidentified ion on a display.

[0012] This aspect can include one or more of the following optional functions. The operations include receiving a user selection of an ion, displaying the selected ion as an extracted ion chromatogram ± mass tolerance, and identifying one or more chromatographic peaks corresponding to the extracted ion chromatogram ± mass tolerance. The operations can further include identifying homologous series and RDBE-related species associated with the unidentified ion. The homologous series can include chlorine and / or bromine. The RDBE-related species can include deuterium and / or hydrogen.

[0013] In some examples, the data is raw data from a mass spectrometer. The data may be deconvoluted data from a mass spectrometer. The operations may include labeling the chemical formulas for the unidentified ion on the display. The operations may also include assigning a color to the unidentified ion on the display.

[0014] In some implementations, the mass defect diagram is a mass defect diagram in which hydrogen is substituted by chlorine (Cl-H mass defect diagram). The mass defect diagram can also be a mass defect diagram in which hydrogen is substituted by bromine (Br-H mass defect diagram). Filtering all ions in the mass defect diagram that do not have an associated confirmatory isotope (e.g., an M+2 or M-2 ion) can further include filtering the mass defect with a specified tolerance and relative abundance. Filtering all ions in the mass defect diagram that do not have an associated M+2 or M-2 ion can also include filtering all ions that do not match the Brx isotopic pattern, where x is an integer from 1 to 15 inclusive. Filtering all ions in the mass defect diagram that do not have an associated M+2 or M-2 ion can also include filtering all ions that do not match the Cl ycorrespond to isotopic patterns, where y is an integer from 1 to 15 inclusive. In some examples, filtering all ions in the mass defect diagram that do not have an associated M+2 or M-2 ion includes filtering all ions that do not correspond to Br. x Cl y -Isotope patterns correspond. Filtering out all ions in the mass defect diagram that do not have a corresponding confirming isotope pattern can further include filtering by determining a distance tolerance.

[0015] The distance tolerance can be based on a static m / z distance between the first and second signals. Alternatively, it can be based on a statistical m / z confidence interval determined from the number of ions in the first signal and the number of ions in the second signal. The distance tolerance can be based on a statistical m / z confidence interval of approximately 2.8. Finally, the distance tolerance can be limited by user input.

[0016] In some examples, filtering all ions in the mass defect diagram that do not have a corresponding confirmatory isotope involves filtering by relative abundance. The relative abundance can be determined for M+l signals. Determining the relative abundance for M+l signals may further involve determining a maximum predicted number of an M+1 element based on the intensity of a putative M+1 signal, the intensity of a putative monoisotopic signal, and the terrestrial natural abundance of the M+1 element. The M+1 element can be carbon, nitrogen, silicon, or any other element with a naturally occurring M+1 isotope.

[0017] The relative frequency can also be determined for M+2 signals. The operation to determine the relative frequency for M+2 signals can involve determining a maximum predicted count of an M+2 element based on the intensity of a putative monoisotopic signal, the intensity of a putative M+2 signal, and the terrestrial natural frequency of the M+2 element. Alternatively, the operation to determine the relative frequency for M+2 signals can involve determining a maximum predicted count of an M+2 element based on the intensity of a putative monoisotopic signal, the intensity of a putative M+2 signal, the intensity of a putative M+4 signal, and the terrestrial natural frequency of the M+2 element.The operation to determine the relative frequency of M+2 signals may further include determining a maximum predicted count of an M+2 element based on the intensity of a putative monoisotopic signal, the intensity of a putative M+4 signal, the intensity of a putative M+6 signal, and the terrestrial natural frequency of the M+2 element. In some examples, the operation to determine the relative frequency of M+2 signals includes determining a maximum predicted count of an M+2 element based on the intensity of a putative monoisotopic signal, the intensity of a putative M+6 signal, the intensity of a putative M+8 signal, and the terrestrial natural frequency of the M+2 element.The operation to determine the relative abundance for M+2 signals may further include determining whether one or more analytes contain both chlorine and bromine, and if the analytes contain both chlorine and bromine, determining a maximum predicted number of an M+2 element based on the terrestrial natural abundance of . 37 Cl and the terrestrial natural abundance of 81 Br.

[0018] The details of one or more implementations of the application are set out in the attached drawings and the description below. Further aspects, features and advantages will become apparent from the description and drawings as well as from the claims. DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a schematic representation of an exemplary time-of-flight mass spectrometer (FT-MS). Fig. Figure 2 is a schematic representation of a TOF-MS and sample introduction gas chromatography system. Fig. Figure 3 shows an exemplary arrangement of operations for characterizing a mass defect diagram (MDD) with halogen filtration. Fig. Figure 4 shows an exemplary procedure 400 for determining distance signals and relative isotope abundance. Fig. Figure 5 shows the m / z distance distribution of B r -Isotopologists on several spectra of the molecular ion of C4Br4S (nominal m / z 400). Fig. Figure 6 shows an example of an extracted ion chromatogram and a total ion chromatogram based on the data from the TOF-MS. Fig. Figure 7 shows an example graph of the mass defect for some elements. Fig. Figure 8 shows an example of a mass defect diagram with areas of interest. Fig. Figure 9 shows an example of a Cl-H mass defect diagram with mass defect (IUPAC) on the y-axis and m / z on the x-axis. Fig. Figure 10 shows the resulting labeled mass defect diagram, which is labelled according to the operations and the procedure. Fig. Figure 11 is a schematic representation of an exemplary computing device with which the systems and procedures described in this document can be implemented.

[0019] Similar reference symbols in the different drawings indicate similar elements. DETAILED DESCRIPTION

[0020] Referring to Fig. 1. In a time-of-flight (TOF) mass spectrometer (MS) 100, the mass M of an ion 10 can be determined by accelerating ions 10 along a trajectory (e.g., by an electric field), measuring the time of flight T of the ion 10, and determining the mass M of the ion 10 using a relationship between the time of flight T and the mass M (e.g., a mass calibration equation). For example, the time of flight T of each ion 10 can be determined using the following equation: T=d2UMz, where d is a trajectory length of ion 10, M is a mass of ion 10, z is a charge of ion 10, and U is an electric potential difference (voltage) for accelerating ion 10. The acceleration of ions 10 with a known electric field strength U results in each ion 10 having the same kinetic energy as every other ion 10 with the same charge z. Since the velocity of ion 10 depends on its mass-to-charge ratio (m / z), the time it takes for an ion 10 to traverse the path and reach a detector 130 (i.e., time of flight T) can be measured. Heavier ions 10 move relatively slower and have relatively longer time of flight T than lighter ions 10. The measurements obtained by detector 130 are referred to as data 140 (see Fig. 2) transmitted back to computer system 1100 for processing (see Fig. 2).

[0021] Fig. Figure 1 shows a schematic representation of an exemplary time-of-flight mass spectrometer (TF-MS) system 100, comprising an ion source assembly 110 (e.g., an accumulating ion source with transfer ion optics and an orthogonal accelerator) connected to a TF analyzer 120 (e.g., a planar multi-reflective TOF (M-TOF) analyzer) and a detector 130. The ion source assembly 110 accelerates ions 10 (e.g., ion bunches) through the TF analyzer 120 with a trajectory and corresponding trajectory length d into the detector 130.

[0022] Fig. Figure 2 shows a schematic representation of an FZ-MS 100 and a Gas Chromatograph 200 system. The Gas Chromatograph 200 uses a capillary column, multiple capillary columns, or a column group with predefined dimensions and phase properties. In some examples, the Gas Chromatograph 200 can be a liquid chromatograph. A sample is introduced into the column, and depending on the different chemical properties of the molecules in the mixture and their affinity for remaining stationary within the column, the column promotes the separation of the molecules. The sample can be introduced by direct insertion probes or by pyrolysis. In some examples, the sample is introduced without chromatography. The molecules elute from the column at different times, based on the retention time.The FZ-MS 100 captures, ionizes, accelerates, steers, focuses, and separately detects the ionized molecules as they are released from the Gas Chromatograph 200. The FZ-MS 100 and the Gas Chromatograph System 200 are given as an example and for context; it should be understood that any system or mass spectrometer capable of determining the precise mass of the ions may be suitable, including systems that allow non-chromatographic or direct sample introduction.

[0023] Fig. Figure 3 shows an exemplary arrangement of operations 300 for characterizing a mass defect diagram (MDD) 900 with halogen filtering. Although halogen filtering is described here as an example, it should be noted that it is only one embodiment and other elements or combinations of elements can be used. In block 302, the operations 300 comprise generating a Cl-H or Br-H mass defect diagram from the summed mass spectra of raw or deconvoluted data (sum of individual peak mass spectra). The data 140 can be determined from the data 140 provided by the FZ-MS 100 and plotted on an atomic mass defect (IUPAC) scale or another suitable scale.In Block 304, Operations 300 comprise filtering all ions 10 in MDP 900 that do not have an associated M+2- or M-2-ion (mass difference of 1.997050 Da for chlorine or 1.997953 Da for bromine) with a scaled mass defect within a specified tolerance. An example tolerance might be 0.0007 Da, and a relative abundance of ± 15% that does not correspond to a theoretical Br. x -, Cl y - or Br x Cl y - Isotope pattern 150 (where x can be an integer from 1 to 15 inclusive, and y can be an integer from 1 to 15 inclusive) matches in at least one embodiment of the invention. Alternatively, the tolerances can also be as described below with respect to Fig. 4 and Fig. 5 will be calculated.

[0024] In block 306, operations 300 include selecting the most abundant unidentified ion 10 and determining the isotopic pattern 150 for the isotopic cluster 152, i.e. Br2 or B. rCl2, etc. In Block 308, Operations 300 include searching for the formula containing the elements identified by the 150 isotopic pattern, in addition to other common elements, for example, using standard combinatorial approaches. Other common elements include, but are not limited to, carbon, hydrogen, nitrogen, oxygen, sulfur, and / or phosphorus. In Block 308, Operations 300 include labeling the 152 isotopic cluster with the determined chemical formula. In some versions, Operations 300 include assigning a unique color, symbol, or identifier to the 152 isotopic cluster. In Block 310, Operations 310 include labeling the 152 isotopic cluster with the chemical formula determined in Block 308.In block 312, the operations include searching for homologous series (±Cl or ±Br) and RDBE-related species (±H or ±2H) and labeling them with the same color as the isotope cluster 152, which was identified and stained in block 310. In some embodiments, in block 314, the operations include displaying the selected ion 10 as the extracted ion chromatogram ± mass tolerance (i.e., 207.1547 ± 5 ppm or ± 0.001 Da) and identifying the chromatographic peak(s) corresponding to the extracted ion chromatogram. In some embodiments, operational blocks 306 to 314 are repeated until all ions in the MDP 900 are identified.

[0025] Fig. Figure 4 shows an example method 400 for determining the spacing signals and a relative isotope abundance. In block 402, the spacing tolerance T is defined. m determined. A confidence interval or m / z tolerance (T mThe difference between centroids of two recorded spectral signals of similar m / z is given by equation 2: Tm=(±Km)(0.42466)(m)(RFWHM)−1[(n1)−1+(n2)−1]1 / 2 where K m the width of the m / z confidence interval divided by σ, 0.42466 equals σ divided by the full width at half-maximum height (FWHM), m the m / z centroid of spectral signal 1 is approximately equal to the m / z centroid of spectral signal 2, R FWHM where the expected resolving power is at half the maximum signal height, n1 is the number of ions 10 in spectral signal 1 (the more intense signal) and n2 is the number of ions 10 in spectral signal 2 (the less intense signal).

[0026] To simplify implementation and increase automated desotoping speed, parameters can be removed using simplified approximations. Removing n1 or n2 allows the use of a single T. m -value for each stick, instead of a different T m -value for each compared pair of sticks. By definition, n1 is not smaller than n2, therefore m no longer contributes to T. m where n1 is approximately equal to n2, and removing n1 is more prudent than removing n2. Assume n1 is approximately equal to n2, and substituting n1 for n2 in equation 2 yields equation 3: Tm=(±Km)(0.42466)(m)(RFWHM)−1(2)1 / 2(n2)−1 / 2

[0027] The number of ions, (n2), refers to the spectral range of signal 2, as given in equation 4: n2=(a2)(i2) where a2 is the spectral range of signal 2 and i2 is the expected ions 10 per range at the mass of signal 2.

[0028] During detector tuning, ions 10 per region are estimated for the tuning mass. Some detectors are expected to register weaker signals as heavier, i.e., slower, ions 10, and stronger signals as lighter, i.e., faster, ions 10. If the detector response for such detectors is directly proportional to the velocity of ions 10, and the ions per region are estimated for a single tuning mass, then the ions 10 per region expected at the mass of signal 2, (i2), are related to the ions 10 per region at the tuning mass by Equation 5: i2=(itune)(m / mtune)1 / 2 where i tune the ions 10 per area for the tuning mass by means of detector tuning or measurement are and m tune The m / z value is the tuning mass used by detector tuning or detector measurement.

[0029] Substituting equation 5 into equation 4, substituting the result into equation 3, and partially simplifying yields equation 6: Tm=(±Km)(2)1 / 2(0.42466)(m)(RFWHM)−1[(a2)(itune)(m / mtune)1 / 2]−1 / 2

[0030] A further simplification would show that the m / z tolerance, (T m ), presumably varies with the ¾ power (power) of the observed m / z (m). For masses heavier than the tuning mass, the T predicted in Equation 6 m The value should therefore be narrower than the T m -value assuming that the detector response is independent of the ion mass. Similarly, for masses lighter than the tuning mass, the T predicted in Equation 6 is used. m -value wider than T mThe m / z value assumes that the detector response is independent of the ion mass. It should be noted that not all detectors register weaker signals than heavier, i.e., slower ions, and stronger signals than lighter, i.e., faster ions, so that the m / z tolerance varies linearly with the observed m / z for some detectors, or the m / z tolerance appears as a different function of m / z.

[0031] A practical value for K m is approximately 2.8, which corresponds to a confidence level of about 99.5%. Multiplying 2.8 by the square root of 2 yields a pleasing value of approximately 4. The recommended distance tolerance for detectors where the reaction is directly proportional to the ion velocity is then given by Equation 7: Tm=(±4)(0.42466)(m)(RFWHM)−1[(a2)(itune)(m / mtune)1 / 2]−1 / 2

[0032] To allow for easy substitution of the solution (resolution) for other peak heights in equation 7, the constant R is FWHM converted to σ (0.42466), not combined with the confidence interval factor (±4).

[0033] The empirical verification of the distance tolerances predicted in Equation 7 is in Fig. Figure 5 illustrates this. Repeated injections of tetrabromobenzothiophene were recorded, the spacing of all bromine isotopologues of the molecular ion in all spectra of all injections was calculated, and the estimated number of ions in the smaller isotopologue was plotted against the spacing. The spacing tolerances predicted in Equation 7 are shown in curve 502. For comparison, the spacing tolerances predicted by the linear-width m model (i.e., a model assuming that the detector response is independent of m / z) are shown by curve 504. A total of 1230 individual bromine isotopologue pairs are shown in the graph. Fig. Figure 5 shows the spacing of Br isotopologues for the molecular ion of C4Br4S (m / z nominal value of 400). The ions 10 per region were measured at an m / z nominal value of 219. The expected R FWHM With an m / z of 400, the result is 35,000.

[0034] Additional adjustments to the spacing tolerances can be made to avoid statistically justified tolerances that are too tight for very large n or small m / z, and to avoid tolerances that are too wide for very small n and large m / z. To avoid the latter, the tolerance width can be limited to the width corresponding to a number of ions 10 that can be quantitatively determined with reasonable accuracy. The expected CV for the range of 25 ions 10 is approximately 20%; the corresponding upper limit for the tolerance width is captured in Equation 8: Tm=(±4)(0.42466)(m)(RFWHM)−1{MAX[(25),(a2)(itune)(m / mtune)1 / 2]}−1 / 2

[0035] Limiting the tolerance range to the expected range for 25 ions 10 would exclude 2 out of 1230 isotopic pairs found in Fig. The 5 shown are excluded. In both excluded pairs, the smaller isotope has fewer than 16 ions in total.

[0036] For very large n or small m / z, non-statistical contributions to the isotopic signal spacing can dominate the statistical contributions. A final adjustment can consist of adjusting the T predicted by Equation 8. m -value to be overridden with a user-defined minimum tolerance, as shown in Equation 9: T=MAX[Tuser,Tm]

[0037] A suitable default setting for T user = 1.5 mDa

[0038] In Block 404, Procedure 400 includes the determination of the relative frequency tolerances for M+1 signals. After putative pairs of isotopologists have been found within the preceding distance tolerances, appropriate relative frequency tolerances can be determined taking into account the element count predicted by the pairs of putative isotopologists.

[0039] For GC-accessible analytes that are typically found in petroleum, bio, food or environmental samples, 13 C, 15 N, 29 Si and 33 S the main contributions to the relative total frequency of M+1, with minor contributions of 17 O and 2 H. Since boron-containing and metal-containing analytes are rarely found in the above sample types, such analytes will be disregarded in further consideration.

[0040] Of the likely M+1 contributors, 29As expected, silicon has the highest relative abundance per unit mass. Therefore, the most tolerant assumed elemental composition for any putative isotopic cluster in a spectrum is pure terrestrial silicon. The silicon count is then predicted from the relative abundance of the putative M+1 signal. For a genuine M+1 signal, the predicted silicon count, including a suitable tolerance, cannot exceed the measured monoisotopic mass divided by 28.

[0041] The prediction of the silicon number from the relative frequency of M+1 is given in equation 10 and generalized to an arbitrary “M+1” element in equation 11: Si=[M+1][M]−1[0,0508]−1 where Si is the predicted maximum silicon number in the formula, [M+1] is the intensity of the supposed M+1 signal, [M] is the intensity of the supposed monoisotopic signal, and 0.0508 is the terrestrial natural abundance of 29 She is. CM+1=[M+1][M]−1[A]−1 where C M+1 where [M+1] is the predicted maximum number of an “M+1” element (mainly C, N, Si), [M+1] is the intensity of the supposed M+1 signal, [M] is the intensity of the supposed monoisotopic signal, and A is the terrestrial natural abundance of an element.

[0042] Ion statistics fundamentally limit the reliability of the predicted element count according to Equation 11. A confidence interval, or element count tolerance (T), c The predicted number of elements (C) is calculated using equation 12: Tc=(±Kc)(C)(nP)−1 / 2[2+(AC)+(AC)−1]1 2 where C is the predicted number of an element or number of elements, K c The element count confidence interval / σ is; a suitable value is 2.8, which corresponds to a confidence level of approximately 99.5%, and where n p the total number of ions 10 in the pair of purported isotopologue signals; it should be noted that equivalent renderings of Equation 12 using n M or n M+1 instead of n p using the relationships n M + n M+1 = n p and n M+1 / n M = AC could be derived.

[0043] Thus, a provisionally assigned M+1 signal found by the m / z distance can be rejected as an incorrect M+1 assignment if the predicted maximum silicon number (from equation 10) minus the element number tolerance (T) c, from Equation 12) is greater than the measured monoisotopic mass divided by 28. If silicon-containing compounds are not analytes of interest for a particular analysis, the relative abundance threshold of M+1 can be based on pure terrestrial carbon. In this case, the predicted carbon number would be calculated using Equation 11, where A = 0.0108. This predicted carbon number, minus the carbon number tolerance from Equation 12, should not exceed the measured monoisotopic mass divided by 12.

[0044] In block 406, procedure 400 includes the determination of relative frequency tolerances for M+2 signals. Testing supposed 34 S-signals should be similar to testing for supposed M+l signals; the predicted sulfur number minus the sulfur number tolerance from equation 12 should not exceed the measured monoisotopic mass divided by 32.

[0045] Chlorinated and brominated analytes exhibit strong characteristic isotopic patterns with several detectable isotopologists in a series (M, M+2, M+4, M+6, ...). Within a valid series of 37 For Cl, Br, or mixed halogen isotopologues, there is always at least one neighboring pair of halogen isotopologues with a relative frequency difference of no less than the terrestrial natural frequency of . 37 Cl, taking into account statistically valid relative abundance tolerances. Therefore, a number of putative halogen isotopologists should be rejected if all pairs of neighboring elements yield a predicted chlorine number of less than one minus the tolerance given in Equation 12. Alternatively, more thorough approaches to testing putative halogen patterns are possible, but these can be computationally cumbersome. Some of the details are explained below.

[0046] In Block 408, Procedure 400 includes determining the alternative relative abundance tolerances for chlorinated or brominated isotopic patterns. Chlorinated and brominated analytes may exhibit strong isotopic clusters with several even-numbered (M, M+2, M+4, etc.) isotopologists of significant abundance (>10% with respect to the most abundant isotopologist). Putative members of such strong isotopic clusters can be confirmed or rejected by requesting predicted element numbers to agree for neighboring pairs of putative isotopologists. For a typical organic compound containing chlorine or bromine, but not both, Equation 11 can be extended to higher isotopologist pairs as in Equations 13 to 16 and, if desired, further generalized. CM+2=[M+2][M]−1[A]−1 CM+2=1+(2)[M+4][M+2]−1[A]−1 CM+2=2+(3)[M+6][M+4]−1[A]−1 CM+2=3+(4)[M+8][M+6]−1[A]−1 where C M+2the predicted maximum number of an “M+2” element (mainly Cl, Br), [M] is the intensity of the supposed monoisotopic signal, [M+2] is the intensity of the supposed M+2 signal, [M+4] is the intensity of the supposed M+4 signal, [M+6] is the intensity of the supposed M+6 signal, [M+8] is the intensity of the supposed M+6 signal, and A is the terrestrial natural isotopic abundance (mainly 37 Cl, 81 Br).

[0047] The tolerance from Equation 12 can be validly applied to Equation 13 for a putative M+2 / M pair, but may underestimate the uncertainty in the relative abundance for higher isotopologist pairs. A valid generalization of Equation 12 to higher isotopologist pairs can be computationally cumbersome. A more practical approach is to predict the chlorine or bromine number (CM+2) for each pair of neighboring putative M+2 isotopologists and to accept a putative isotopic cluster if the predicted chlorine or bromine number is consistent across all neighboring isotopologist pairs. Loose tolerances should be applied; it is reasonable to require that the number of predicted elements agree within a factor of 2. The data given above can be used to determine the MDP 900 that does not possess an M+2 or M-2 ion.

[0048] In Block 410, the procedure includes determining the minimum monoisotopic mass for a brominated analyte using virtual monoisotopic bromine. Highly brominated analytes may exhibit monoisotopic signals that are significantly weaker than the most abundant isotope. For highly brominated analytes, the probability of a quantifiable, most abundant isotope belonging to an undetectable monoisotopic signal justifies considering virtual bromine isotopes. A reasonable maximum number of adding virtual bromine isotopes is twice the number of detected bromine isotopes minus two. Thus, if three bromine isotopes are detectable, the detected lowest-mass isotope can be tested as M (no virtual bromine isotopes) or M+2 (two virtual bromine isotopes; one on each side of the detected isotopic cluster).If four bromine isotopologists are detectable, the detected isotopologist of the lowest mass can be tested as M (no virtual bromine isotopologists), M+2 (two virtual bromine isotopologists; one on each side of the detected isotope cluster) or M+4 (four virtual bromine isotopologists; two on each side of the detected isotope cluster).

[0049] The monoisotopic mass must be sufficient to support the number of bromine predicted by the isotopic cluster, plus the number of carbon atoms required to support additional bromine beyond two. The minimum monoisotopic mass for a brominated analyte is obtained from Equations 17, 18, and 19. Massmin=(79)(CBr)+(12)(CCmin) where measure min the minimum monoisotopic mass for a brominated analyte, C Br The Br number is predicted by the number of Br isotopologues (Eq. 18), and C Cminthe minimum number of C required to support the number of Br (Eq. 19). CBr = (sum of detected and virtual bromine isotopologues) - 1 CCmin=(CBr−2)(2)−1

[0050] Every fractional value of C Cmin In equation 19, the result is always rounded up to the larger integer. Virtual bromine isotopes cannot be added if the resulting monoisotopic mass is less than the mass. min from equation 17 would be.

[0051] In Block 412, Procedure 400 includes determining the relative M+2 intensity relative to the M intensity for mixed halogen patterns. Analytes containing both Cl and Br exhibit isotopic patterns that do not yield a consistent predicted elemental number using the form in Equations 12 to 16. For such mixed halogens, the total M+2 intensity relative to the M intensity is obtained from Equation 20: [M+2][M]−1=AClCCl+ABrCBr where A c1 the terrestrial natural abundance of 37 Cl is, C c1 the chlorine number in the formula, A Br the terrestrial natural abundance of 81 Br, and C Br The bromine number in the formula is...

[0052] The total M+4 intensity relative to the M intensity is given by equation 21: [M+4][M]−1=12ACl2(CCl2−ClCl)+AClCClABrCBr+12ABr2(CBr2−CBr)

[0053] If either C Cl or C Br Since the result is zero, equation 21 can be divided by equation 20 and the result transformed into equation 13. From [M], [M+2] and [M+4], the system of equations 20 and 21 should provide a real and valid solution for C. Cl and C Br result.

[0054] Fig. Figure 6 shows an example of an extracted ion chromatogram and an extracted total ion chromatogram 600 based on data 140 from the FZ-MS 100. The extracted ion chromatogram and the extracted total ion chromatogram 600 include an x-axis for time and a y-axis for signal intensity. Individual peaks 610 are shown in the extracted ion chromatogram and the extracted total ion chromatogram 600 in relation to the individual detection of ions 10 by the detector 130. The data 140 can be used to determine the mass defect of these ions. Furthermore, the extracted ion chromatogram 600 allows a user to select a region of interest and to limit the mass filtering and mass defect analysis to obtain more accurate results.

[0055] Fig. Figure 7 shows an example graph for a mass defect. The mass defect can be determined by equation 22. Mass defect = Exact mass − Nominal mass.

[0056] For example, the mass defect is centered around carbon, which has an atomic weight of 12.0000 according to IUPAC. Considering C3H8, it has an exact mass of 44.06205 and a nominal mass of 44.00000, resulting in a mass defect of 0.06205. In comparison, C3Cl6 has an exact mass of 281.81257 and a nominal mass of 282.00000, resulting in a mass defect of -0.18743. The graph shown here illustrates atomic mass defects for some common isotopes. For example, 1 H a mass defect of less than 0.01 and 2 H₂ has a mass defect of approximately 0.015, making it easy to distinguish between isotopes. Even isotopes with similar atomic masses can be differentiated by their mass defects and have significantly different mass defect values. For example, 15 N and 16O with an atomic mass of 15.0001 or 15.99491, respectively, shows a significant difference in mass defect of approximately 0.0001 or 0.005.

[0057] Fig. Figure 8 shows an example of a mass defect diagram 800 with the regions of interest 810. When the mass defect is calculated from the data 140 and plotted based on this, with the y-axis representing the mass defect and carbon 12 as the zero mass defect, and the m / z value being plotted on the x-axis, several regions of interest appear. Alkanes generally appear in the alkane region 810a, siloxanes generally in the siloxane region 810b, and halogenated compounds generally in the halogenated compound region 810c. This is useful so that the user can identify a particular compound of interest, which is limited to the amount of data 140 that needs to be processed to determine the specific ions 10 and / or the compound formula.

[0058] Fig. Figure 9 shows an example of a Cl-H mass defect diagram 900 with the mass defect (IUPAC) on the y-axis and m / z on the x-axis. Alternative calculations can be applied to generate a Kendrick mass defect diagram in which CH₂ is considered to be exactly 14 Da instead of the IUPAC mass for CH₂, which is considered to be 14.01565. The Kendrick mass is defined in Equation 23. Kendrick mass=IUPAC mass*(14.00000 / 14.01565)

[0059] The scaled mass defect can be determined by first finding the scaled mass in equation 24. Scaled mass = IUPAC mass * scaling factor

[0060] The special scaling factor for the graphs shown for Cl-H is 34 / 33.96048. The scaled mass defect can be determined by equation 25. Scaled mass defect = Scaled mass − Scaled nominal mass

[0061] Each point 910 in the Cl-H diagram corresponds to a peak on the extracted ion chromatogram, as shown in Fig. 6 shown. The individual points 910 represent compounds and / or ions 10, which are determined by the computer system using operations 300 and procedure 400 according to Fig. 3 and Fig. 4 are to be identified.

[0062] Fig. Figure 10 shows the resulting labeled mass defect diagram 1100, which corresponds to operations 300 and procedure 400 in Fig. 3 and Fig. 4 can be marked. After the formula search in block 308 has been carried out and the ions of 10 elements have been identified, the mass defect diagram is marked to show the individual compounds. Each point 910 can be marked with a symbol or a color to identify the compound to which it refers. Each point 910, or compound, can also include a label 1010 to facilitate identification. The label 1010 can correspond to multiple points 910. A key or index 1020 can be displayed by the computer 1100 or a display to correctly determine the corresponding compound.

[0063] In at least one example, a user employs an FZ-MS 100 or another suitable mass spectrometer system to analyze a sample. Ions 10 from the sample can strike the detector 130, resulting in data 140 being delivered to a computer device 100 connected to the FZ-MS 100. The time and energy of the ions 10 striking the detector 130 can be graphically represented as an ion chromatogram 600 based on the data 140, where the x-axis represents time and the y-axis represents signal intensity. The ion chromatogram 600 can be presented to the user via a display 1180, allowing the user to select from a range of data of interest. This user selection can be a click, a touch gesture, a slider operation, or any other suitable method for selecting the raw or processed data of interest.A user can select the generation of a mass defect diagram and enter additional attributes such as data source, mode, filter, reference formula, defect polarity, defect fit, and / or automatic update. In at least one example, the data source is a slider, the mode is a scaled mass defect, the frequency filter represents a minimum value and has a value of 0.1, the reference formula is CH2, the defect polarity is positive, the defect fit is 0, and automatic update is enabled. Based on this data source, computer system 1100 can generate a mass defect diagram 800, 900. The mass defect can be determined using equation 22 above. In at least one implementation, the mass defect diagram 800, 900 can be filtered using a specific Da value and a relative frequency. In at least one implementation, statically solid distances and relative frequency tolerances are determined.For example, a Km value of approximately 2.8 can be used, and using equation 7 a T. m (Confidence interval or m / z tolerance) can be determined based on the m / z center of spectral signal 1 and / or 2, the expected resolution at half the maximum signal height, the spectral area of ​​signal 2, the ions per region for the tuned mass by detector tuning or measurement, and / or the m / z of the tuned mass used by detector tuning or measurement. The T m can be widened or narrowed to avoid statistically based tolerances that are too tight for very large n or small m / z values, and to avoid tolerances that are too wide for very small n and large m / z values. In some examples, the T mThe value is limited by user input. Next, the relative frequencies of M+1 can generally be determined using Equation 10, and the confidence interval can be determined using Equation 12. The main contributors to the M+1 numbers are 13 C, 15 N, 29 Si and 33 S, with smaller contributions of 170 and 2H. In some examples, silicon equation 10 can be used. Equations 10 and 11 can be determined from the intensity of the putative M+1 signal, the intensity of the monoisotopic signal, and the terrestrial natural abundance of the element in question. Equation 12 also contains the predicted number of an element, the confidence interval of the element number divided by σ, and the total number of ions in the pair of potential isotopic signals. Next, the M+2 signals can be determined.In some examples, the relative abundance tolerances are separated into chlorinated or brominated isotope patterns using equations 13-16 based on the predicted maximum number of an “M+2” element (mainly Cl, Br), the intensity of the supposed monoisotopic signal, the intensity of the supposed M+2 signal, the intensity of the supposed M+4 signal, the intensity of the supposed M+6 signal, the intensity of the supposed M+8 signal, and the terrestrial natural isotope abundance (mainly . 37 Cl, 81Br) A determined. In other examples with a high proportion of brominated analytes, equations 17-19 can be used to determine the minimum monoisotopic mass of the brominated analyte based on the Br number predicted by the number of Br isotopologues and the minimum C number required to support the Br number. In examples with mixed halogen patterns, such as Cl and Br, equations 20 and 21 can be used to determine the M+2 and M+4 intensities based on the terrestrial natural abundance of 37 Cl, the chlorine number in the formula, the terrestrial natural abundance of 81Br, and the bromine number in the formula. The resulting values ​​can be used to filter the data 140 into isotopic patterns 150 and isotopic clusters 152. The computer device 1100 can select the most common unidentified ion 10 in the selected data and determine the isotopic pattern 150 for the isotopic cluster 152. Formula searches can be performed to determine the elements identified by the isotopic pattern 150. For example, in the case of an isotopic pattern for chlorine, formulas containing chlorine are searched for. In some examples, common elements such as carbon, hydrogen, nitrogen, oxygen, sulfur, and / or phosphorus are also examined to determine if the formula contains these elements, in order to determine if the unidentified ion corresponds to the mass defect of the formula.After determining the formula for the unidentified ion, the computer device 1100 can label the unidentified ion 10 on a labeled mass defect diagram 1000, and the computer device can color and / or label the displayed ion 10 on the labeled mass defect diagram 1000. After identifying ion 10, the computer device 1000 searches homologous series (±Cl or ±Br) and RDBE (or RDBÄ) related species (±H or ±2H) and labels them with the same color or label as the isotope cluster 152. In some examples, the user can select an ion 10, or the computer device can select an ion 10 and display to the user an extracted ion chromatogram ± mass tolerance with peaks 610 for the identified ion.

[0064] Fig.Figure 11 is a schematic representation of an exemplary computer device 1100 that can be used to implement the systems and methods described in this document. The computer device 1100 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The components shown here, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the inventions described and / or claimed in this document.

[0065] The computer device 1100 comprises a processor 1110, a memory 1120, a storage device 1130, a high-speed interface / control unit 1140 connected to the memory 1120 and the high-speed expansion ports 1150, and a low-speed interface / control unit 1160 connected to the low-speed bus 1170 and the storage device 1130. All components 1110, 1120, 1130, 1140, 1150, and 1160 are interconnected via various buses and can be mounted on a common mainboard or otherwise, as required. The processor 1110 can process instructions for execution within the computing device 1100, including instructions stored in memory 1120 or in storage device 1130 to display graphical information for a graphical user interface (GUI) on an external input / output device, e.g.on the display 1180, which is coupled to the high-speed interface 1140. In other implementations, multiple processors and / or multiple buses can be used together with multiple memories and memory types, depending on requirements. Furthermore, multiple computing devices 1100 can be connected, with each device providing parts of the necessary operations (e.g., as a server bank, as a group of blade servers, or as a multiprocessor system).

[0066] The memory 1120 stores information non-transiently in the computer device 1100. The memory 1120 can be a computer-readable medium, one or more volatile memory units, or non-volatile memory units. The non-transient memory 1120 can be embodied by physical devices used for the temporary or permanent storage of programs (e.g., sequences of instructions) or data (e.g., information about the program state) for use by the computer device 1100. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM) / programmable read-only memory (PROM) / erasable programmable read-only memory (EPROM) / electronically erasable read-only memory (EEPROM) (e.g., typical for firmware such as boot programs).Examples of volatile memory include, but are not limited to, Random Access Memory (RAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Phase Change Memory (PCM), as well as disks or tapes.

[0067] The storage device 1130 is capable of providing mass storage for the computer device 1100. In some implementations, the storage device 1130 is a computer-readable medium. In various different configurations, the storage device 1130 can be a floppy disk device, a hard disk device, an optical storage disk device or tape device, flash memory or similar solid-state storage device, or an array of devices, including devices in a storage area network or other configurations. In additional implementations, a computer program product is tangiblely embedded in an information carrier. The computer program product contains instructions that, when executed, perform one or more procedures as described above. The information carrier is a computer- or machine-readable medium, such as the memory 1120, the storage device 1130, or memory in the processor 1110.

[0068] The high-speed control unit 1140 manages bandwidth-intensive operations for the computer device 1100, while the low-speed control unit 1160 manages less bandwidth-intensive operations. Such a task distribution is only an example. In some implementations, the high-speed control unit 1140 is coupled to the memory 1120, the display 1180 (e.g., via a graphics processor or accelerator), and the high-speed expansion ports 1150, which can accommodate various expansion cards (not shown). In some implementations, the low-speed control unit 1160 is coupled to the memory device 1130 and the low-speed expansion port 1170. The low-speed expansion port 1170, which provides various communication ports (e.g.,It may include USB, Bluetooth, Ethernet, Wireless Ethernet) and may be paired with one or more input / output devices, such as a keyboard, pointing device, scanner or network device, such as a switch or router, e.g. via a network adapter.

[0069] The computer device 1100 can be implemented in various forms, as shown in the figure. For example, it can be implemented as a standard server 1100a or multiple servers 1100a together, as a laptop computer 1100b, or as part of a rack server system 1100c.

[0070] Various implementations of the systems and techniques described herein can be realized in digital electronic and / or optical circuits, integrated circuits, specially designed ASICs (application-specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementation in one or more computer programs that are executable and / or interpretable on a programmable system comprising at least one programmable processor, which may serve a specific or general purpose, and which is coupled to a storage device, at least one input device, and at least one output device in order to receive data and instructions from and transmit data and instructions to a storage system.

[0071] These computer programs (also known as programs, software, software applications, or code) comprise machine instructions for a programmable processor and can be implemented in a higher-level procedural and / or object-oriented programming language and / or in assembly / machine language. The terms "machine-readable medium" and "computer-readable medium" as used herein refer to any computer program product, non-transitory computer-readable medium, device, and / or any apparatus (e.g., magnetic disks, optical disks, memory, programmable logic devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal.The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0072] Implementations of the subject matter and the functional procedures described in this specification may be implemented in digital electronic circuits or in computer software, firmware, or hardware, including the structures specified in this specification and their structural equivalents, or in combinations of one or more of them. Furthermore, the subject matter described in this specification may be implemented as one or more computer program products, i.e., as one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or control of the operation of, data processing devices.The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a storage device, a composition of matter that produces a machine-readable signal, or a combination of one or more of these. The terms "data processing device," "computer device," and "computer processor" encompass all devices, apparatus, and machines for data processing, including, for example, a programmable processor, a computer, or multiple processors or computers. In addition to the hardware, the device may also include code that creates an execution environment for the computer program in question, such as code representing processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these. A propagating signal is an artificially generated signal, for example,A machine-generated electrical, optical or electromagnetic signal that is produced to encode information for transmission to a suitable receiving device.

[0073] A computer program (also known as an application, program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be used in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computer environment. A computer program does not necessarily correspond to a file in a file system. A program may be stored in a portion of a file containing other programs or data (e.g., one or more scripts stored in a document in a markup language), in a single file associated with the program in question, or in several coordinated files (e.g., files containing one or more modules, subroutines, or portions of code).A computer program can be used to run on one computer or on multiple computers located at one site or distributed across multiple sites and connected via a communication network.

[0074] The processes and logical sequences described in this specification can be executed by one or more programmable processors, which run one or more computer programs to perform functions by processing input data and generating output data. The processes and logical sequences can also be performed by a dedicated logic circuit, such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit), and the device can also be implemented as such a dedicated logic circuit.

[0075] Processors suitable for executing a computer program include, for example, general-purpose and special-purpose microprocessors, as well as one or more processors of any digital computer. Generally, a processor receives instructions and data from read-only memory or random-access memory, or both. The essential elements of a computer are a processor for executing instructions and one or more storage devices for storing instructions and data. Generally, a computer also includes, or is operationally coupled to, one or more mass storage devices for storing data, such as magnetic, magneto-optical, or optical disks, for receiving data or transmitting data, or for both purposes. However, a computer need not have such devices. Furthermore, a computer can be embedded in another device, such as a...in a mobile phone, a personal digital assistant (PDA), a portable audio playback device, a global positioning system (GPS) receiver, to name just a few. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and storage devices, including, for example, semiconductor memory devices such as EPROMs, EEPROMs, and flash memory devices; magnetic disks such as internal hard disks or removable media; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and memory may be supplemented by or integrated into a special logic circuit.

[0076] To enable interaction with a user, one or more aspects of this login can be implemented on a computer with a display, such as a cathode ray tube (CRT), a liquid crystal display (LCD), or a touchscreen, to show information to the user, and optionally a keyboard and pointing device, such as a mouse or a directional pad, with which the user can input information into the computer. Other types of devices can also be used to interact with a user; for example, feedback can be given to the user in any form of sensory feedback, such as visual, audible, or tactile feedback; and input from the user can be received in any form, including auditory, verbal, or tactile input.Furthermore, a computer can interact with a user by sending and receiving documents to and from a device used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests from the web browser.

[0077] One or more aspects of the registration can be implemented in a computer system that includes a backend component, such as a data server, or a middleware component, such as an application server, or a frontend component, such as a client computer device with a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such backend, middleware, or frontend components. The system components can be interconnected by any form or medium of digital data communication, such as a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), an internetwork (such as the Internet), and peer-to-peer networks (such as ad-hoc peer-to-peer networks).

[0078] The computer system can contain clients and servers. Client and server are usually located remotely and typically interact via a communication network. The client-server relationship is established through computer programs running on the respective machines, which maintain a client-server relationship. In some implementations, a server transmits data (e.g., an HTML page) to a client device (e.g., to display data and receive user input from a user interacting with the client device). Data generated at the client device (e.g., a result of user interaction) can be received by the client device at the server.

[0079] Although this specification contains many distinctive features, these are not to be interpreted as a limitation of the scope of the application or the claims, but rather as a description of the functions that are specific to certain implementations of this application. Certain functions described in this specification within the context of separate implementations may also be implemented in combination within a single implementation. Conversely, various functions described here within the context of a single implementation may also be implemented separately in several implementations or in any suitable subcombination.Furthermore, although functions in the preceding text are described as acting in certain combinations and are initially claimed as such, in some cases one or more functions of a claimed combination may be removed from the combination, and the claimed combination may also be directed to a sub-combination or to a variation of a sub-combination.

[0080] Similarly, even if operations are depicted in a specific order in the drawings, this should not be interpreted as meaning that such operations must be performed in the indicated order or in sequential order, or that all depicted operations must be performed to achieve desired results. Multitasking and parallel processing can be advantageous under certain circumstances. Furthermore, the separation of different system components in the implementations described above should not be interpreted as requiring such separation in all implementations. It should be understood that the described program components and systems can generally be integrated together in a single software product or packaged across multiple software products.

[0081] A number of implementations have been described. However, it is assumed that various modifications can be made without deviating from the spirit and scope of the application. Accordingly, other implementations also fall within the scope of the following claims. For example, the actions listed in the claims can be performed in a different order and still achieve desirable results.

Claims

[1] Method for creating a filtered mass defect diagram based on mass data obtained from a mass spectrometer, the method comprising: Generating a mass defect diagram from the data obtained by the mass spectrometer; selecting an unidentified ion; Determining an isotopic pattern for the unidentified ion; Identifying one or more elements that indicate an isotopic pattern for the unidentified ion; Search for formulas containing one or more elements that indicate the isotopic pattern for the unidentified ion; Determining a chemical formula for the unidentified ion; Displaying the chemical formula for the unidentified ion on a display; receiving a user selection of an ion; and Display of the selected ion as an extracted ion chromatogram ± mass tolerance; and Identifying one or more chromatographic peak(s) according to the extracted ion chromatogram ± mass tolerance. [2] Method according to claim 1, further comprising identifying homologous series and RDBE-related species related to the unidentified ion. [3] Method according to claim 2, wherein the formulas in the homologous series contain elements with characteristic isotopologists, such as chlorine, bromine and the like. [4] Method according to claim 3, wherein the RDBE-related species further comprise species that differ in the number of elements for monovalent elements, such as deuterium, hydrogen and the like. [5] Method according to claim 1, wherein the data are raw data from the mass spectrometer. [6] Method according to claim 1, wherein the data are deconvoluted data from the mass spectrometer. [7] Method according to claim 1, further comprising indicating the chemical formulas for the unidentified ion on the display. [8] Method according to claim 1, further comprising assigning a color or identifier to the unidentified ion on the display. [9] Method according to claim 1, wherein the mass defect diagram is an iterative addition of a chemical formula. [10] Method according to claim 9, wherein the mass defect diagram is a CH2 mass defect diagram. [11] Method according to claim 1, wherein the mass defect diagram is a mass defect diagram in which hydrogen is substituted by chlorine (Cl-H mass defect diagram). [12] The method according to claim 1, wherein the mass defect diagram is a mass defect diagram in which hydrogen is substituted by bromine (Br-H mass defect diagram). [13] Method according to claim 1, wherein filtering all ions in the mass defect diagram that do not have an associated isotopologue ion further comprises filtering the mass defect with a relative frequency tolerance. [14] The method of claim 1, wherein the filtering of all ions in the mass defect diagram that do not have an associated isotopologue ion further comprises the filtering of all ions that do not correspond to Br x -Isotope patterns correspond, where x is an integer from 1 to inclusive 15. [15] Method according to claim 1, wherein filtering all ions in the mass defect diagram that do not have a corresponding isotopologue ion further comprises filtering all ions that do not correspond to Cl y -Isotope patterns correspond, where y is an integer from 1 to inclusive 15. [16] The method of claim 1, wherein the filtering of all ions in the mass defect diagram that do not have an associated isotope ion further comprises the filtering of all ions that do not correspond to Br x Cl y -Isotope patterns correspond, where x is an integer between 1 and 15 inclusive, and y is an integer from 1 to 15 inclusive. [17] Method according to claim 1, wherein filtering all ions in the mass defect diagram that do not have an associated isotopologue ion further comprises filtering by determining a distance tolerance. [18] Method according to claim 17, wherein the distance tolerance is based on a static m / z distance between a first signal and a second signal. [19] Method according to claim 18, wherein an m / z distance tolerance is based on a statistical confidence interval. [20] Method according to claim 18, wherein the distance tolerance is further based on a number of ions in the first signal and a number of ions in the second signal. [21] Method according to claim 17, wherein the distance tolerance is limited by a user input. [22] Method according to claim 17, wherein filtering all ions in the mass defect diagram that do not have an associated isotopologue ion further comprises filtering by determining a relative abundance. [23] Method according to claim 22, wherein the relative frequency for an M+1 signal is determined. [24] Method according to claim 23, wherein determining the relative frequency for an M+1 signal further comprises determining a maximum predicted number of an M+1 element based on an intensity of a purported M+1 signal, an intensity of a purported monoisotopic signal and a terrestrial natural frequency of the M+1 element. [25] Method according to claim 24, wherein the M+1 element is carbon, nitrogen or silicon. [26] Method according to claim 22, wherein the relative frequency for an M+2 signal is determined. [27] Method according to claim 26, wherein determining the relative frequency for M+2 signals further comprises determining a maximum predicted number of an M+2 element based on an intensity of a supposed monoisotopic signal, an intensity of a supposed M+2 signal and a terrestrial natural frequency of the M+2 element. [28] Method according to claim 26, wherein determining the relative frequency for M+2 signals further comprises determining a maximum predicted number of an M+2 element based on an intensity of a putative monoisotopic signal, an intensity of a putative M+2 signal, an intensity of a putative M+4 signal and a terrestrial natural frequency of the M+2 element. [29] Method according to claim 26, wherein determining the relative frequency for M+2 signals further comprises determining a maximum predicted number of an M+2 element based on an intensity of a monoisotopic signal, an intensity of a putative M+4 signal, an intensity of a putative M+6 signal and a terrestrial natural frequency of the M+2 element. [30] Method according to claim 26, wherein determining the relative frequency for M+2 signals further comprises determining a maximum predicted number of an M+2 element based on an intensity of a putative monoisotopic signal, an intensity of a putative M+6 signal, an intensity of a putative M+8 signal and a terrestrial natural frequency of the M+2 element. [31] Method according to claim 26, wherein determining the relative frequency for M+2 signals further comprises: Determine whether one or more analytes contain both chlorine and bromine; and If one or more analytes contain both chlorine and bromine, determine a maximum predicted number of an M+2 element based on a terrestrial natural abundance of 37 Chlorine, a terrestrial natural abundance of 81 Contains bromine. [32] Method according to claim 1, wherein the filtered mass defect diagram is a halogen-filtered mass defect diagram. [33] The method of claim 1, further comprising: Filtering all ions in the mass defect diagram that do not have a corresponding confirmatory isotope. [34] Device comprising: an advertisement; Data processing hardware in communication with the display; and Storage hardware in communication with data processing hardware, wherein the storage hardware stores instructions which, when executed on the data processing hardware, cause the data processing hardware to perform operations that include the following: Generating a mass defect diagram from data obtained from a mass spectrometer; Filtering all ions in the mass defect diagram that do not have a corresponding isotope; selecting an unidentified ion; Selecting an isotopic pattern for the unidentified ion; Identifying one or more elements that indicate an isotopic pattern for the unidentified ion; Searching for formulas containing one or more elements that indicate the isotopic pattern for the unidentified ion; Determining a chemical formula for the unidentified ion; Displaying the chemical forms for the unidentified ion on a display; receiving a user selection of an ion; Display of the selected ion as an extracted ion chromatogram ± mass tolerance; and Identifying one or more chromatographic peaks that correspond to the extracted ion chromatogram ± mass tolerance. [35] Device according to claim 34, wherein the operations further include identifying homologous series and RDBE-related species related to the unidentified ion. [36] Device according to claim 34, wherein the formulas in the homologous series contain elements with characteristic isotopologists such as chlorine, bromine or the like. [37] Device according to claim 34, wherein the RDBE-related species further comprise species that differ in the number of elements for monovalent elements, such as deuterium, hydrogen and the like. [38] Device according to claim 34, wherein the data are raw data from a mass spectrometer. [39] Device according to claim 34, wherein the data are deconvoluted data from a mass spectrometer. [40] Device according to claim 34, wherein the operations further include marking the chemical formulas for the unidentified ion on the display. [41] Device according to claim 34, wherein the operations further include assigning a color to the unidentified ion on the display. [42] Device according to claim 34, wherein the mass defect diagram is an iterative addition of a chemical formula, such as a CH2 mass defect diagram or the like. [43] Device according to claim 34, wherein the mass defect diagram is a mass defect diagram in which hydrogen is substituted by chlorine (CI-H mass defect diagram). [44] Device according to claim 34, wherein the mass defect diagram is a mass defect diagram in which hydrogen is substituted by bromine (Br-H mass defect diagram). [45] Device according to claim 34, wherein the operation of filtering all ions in the mass defect diagram that do not have an associated isotopologue ion further comprises filtering the mass defect with relative specific relative abundance tolerance. [46] Device according to claim 34, wherein the operation of filtering all ions in the mass defect diagram that do not have an associated isotope ion further comprises filtering all ions that do not belong to Br x -Isotope patterns correspond, where x is an integer between 1 and inclusive of 15. [47] Device according to claim 34, wherein the operation of filtering all ions in the mass defect diagram that do not have an associated isotope ion further comprises filtering all ions that do not correspond to Cl y -Isotope patterns correspond, where y is an integer between 1 and inclusive of 15. [48] ​​Device according to claim 34, wherein the operation of filtering all ions in the mass defect diagram that do not have an associated isotope ion further comprises filtering all ions that do not belong to Br x Cl y-Isotope patterns correspond, where x is an integer between 1 and 15 inclusive, and y is an integer between 1 and 15 inclusive. [49] Device according to claim 34, wherein the operation of filtering all ions in the mass defect diagram that do not have an associated isotopologue ion further comprises filtering by determining a distance tolerance. [50] Device according to claim 49, wherein the distance tolerance is based on a statistical m / z confidence interval, a first signal and a second signal. [51] Device according to claim 50, wherein the m / z spatial tolerance is based on a statistical confidence interval. [52] Device according to claim 50, wherein the distance tolerance is further based on a number of ions in the first signal and a number of ions in the second signal. [53] Device according to claim 49, wherein the distance tolerance is limited by a user input. [54] Device according to claim 49, wherein the operation of filtering all ions in the mass defect diagram that do not have an associated isotopologue ion further comprises filtering by determining a relative abundance. [55] Device according to claim 54, wherein the relative frequency for an M+1 signal is determined. [56] Device according to claim 55, wherein the operation of determining the relative frequency for an M+1 signal further comprises determining a maximum predicted number of an M+1 element based on an intensity of a purported M+1 signal, an intensity of a purported monoisotopic signal and a terrestrial natural frequency of the M+s1 element. [57] Device according to claim 56, wherein the M+1 element is carbon, nitrogen or silicon. [58] Device according to claim 54, wherein the relative frequency for an M+2 signal is determined. [59] Device according to claim 58, wherein the operation of determining the relative frequency for M+2 signals further comprises determining a maximum predicted number of an M+2 element based on an intensity of a supposed monoisotopic signal, an intensity of a supposed M+2 signal and a terrestrial natural frequency of the M+2 element. [60] Device according to claim 58, wherein the operation of determining the relative frequency for M+2 signals further comprises determining a maximum predicted number of an M+2 element based on an intensity of a purported monoisotopic signal, an intensity of a purported M+2 signal, an intensity of a purported M+4 signal and a terrestrial natural frequency of the M+2 element. [61] Device according to claim 58, wherein the operation of determining the relative frequency for M+2 signals further comprises determining a maximum predicted number of an M+2 element based on an intensity of a purported monoisotopic signal, an intensity of a purported M+4 signal, an intensity of a purported M+6 signal and a terrestrial natural frequency of the M+2 element. [62] Device according to claim 58, wherein the operation of determining the relative frequency for M+2 signals further comprises determining a maximum predicted number of an M+2 element based on an intensity of a purported monoisotopic signal, an intensity of a purported M+6 signal, an intensity of a purported M+8 signal and a terrestrial natural frequency of the M+2 element. [63] Device according to claim 58, wherein the operation of determining the relative frequency for M+2 signals further comprises: Determine whether one or more analytes contain both chlorine and bromine; and If the analytes contain both chlorine and bromine, determining a maximum predicted number of an M+2 element based on a terrestrial natural abundance of 37 Chlorine and / or a terrestrial natural abundance of 81 Contains bromine.

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