Mass spectrum processing apparatus and method

Abstract

To prevent fine mass information from being greatly damaged when creating a data set for multivariable analysis reduced or compressed based on a plurality of mass spectra.SOLUTION: A KMD analyzer 28 generates n KMD analysis results based on n mass spectra. An integrator 34 generates n integrated value distributions based on the n KMD analysis results. A principal component analyzer 42 applies principal component analysis to the n integrated value distributions (data sets). A component-reflected KMD plot is generated based on a loading map (component distribution) generated by the principal component analysis.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a mass spectrum processing apparatus and method, and more particularly to a technique for comparing a plurality of mass spectra obtained from a plurality of samples with each other. [Background technology]

[0002] A mass spectrometry system generally comprises a mass spectrometer and an information processing device. The mass spectrometer performs mass analysis on a sample. The information processing device applies various processes to the mass spectrum obtained by mass analysis. The information processing device can also be called a mass spectrum processing device.

[0003] A mass spectrometry system is used in the analysis of polymers. Kendrick mass defect (KMD) analysis is known as a method for analyzing mass spectra obtained from polymers (see Patent Document 1).

[0004] Known methods for statistically analyzing a data set consisting of multiple pieces of data include principal component analysis (PCA), vertex component analysis (VCA) (see Non-Patent Document 1), hierarchical cluster analysis, and the like. All of these methods are multivariable analysis. Patent Document 2 discloses the UMAP (Uniform Manifold Approximation and Projection) method, which is a dimensionality reduction method, as a mass spectrum analysis method. In this specification, the two terms "analysis" and "analysis" are used following the common Japanese notation, but they have the same meaning. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2024-50094 A [Patent Document 2] Patent Publication No. 2021-196260 [Non-patent literature]

[0006] [Non-Patent Document 1] Jose MP Nascimento, Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 43, NO. 4, APRIL 2005 Summary of the Invention [Problem to be solved by the invention]

[0007] In comparative or differential analysis of multiple samples, multiple mass spectra obtained from multiple samples are compared with each other, or differences among the multiple mass spectra are identified, by applying multivariate analysis to a data set consisting of multiple mass spectra (mass spectral data).

[0008] When multiple mass spectra are acquired using a mass spectrometer with extremely high mass analysis accuracy, each mass spectrum is composed of, for example, 500,000 ion intensities corresponding to 500,000 m / z values. It is not practical to create a dataset from such multiple mass spectra because the amount of calculation required for multivariate analysis based on such a dataset would be enormous.

[0009] Therefore, it is conceivable to reduce or compress multiple mass spectra and then construct a data set for multivariate analysis using the reduced or compressed multiple mass spectra. Specifically, first, multiple integration intervals are set on the m / z axis. Next, for each mass spectrum, multiple ion intensities belonging to each integration interval are integrated. A reduced data set for multivariate analysis is constructed using multiple integrated value sequences calculated from multiple ion intensity sequences.

[0010] However, the larger the integration interval size, the greater the reduction rate (degree of compression) and the less computational effort is required for multivariate analysis, but the greater the loss of decimal mass information (decimal values ​​of each mass). For example, if the processing range from 500 to 4000 on the m / z axis is divided into 5000 integration intervals, the size of each integration interval will be 0.7 (equivalent to 0.7 u). Mass information smaller than 0.7 will be lost during the integration process. This means that even if a mass spectrometer with extremely high mass resolution is used, the benefits of that resolution cannot be fully realized.

[0011] An object of the present invention is to prevent significant loss of detailed mass information when creating a reduced or compressed dataset for multivariate analysis based on multiple mass spectra. Alternatively, an object of the present invention is to utilize the mass resolution function of KMD analysis when creating a dataset for multivariate analysis. Alternatively, an object of the present invention is to provide a mechanism for reflecting the results of a second analysis with lower mass resolution on the results of a first analysis with higher mass resolution. [Means for solving the problem]

[0012] The mass spectrum processing device according to the present invention is characterized by comprising: a KMD analyzer that applies Kendrick mass defect (KMD) analysis to n (n is an integer of 2 or more) mass spectra obtained by mass analysis of a plurality of samples, thereby generating n KMD analysis results; an integrator that applies integration processing to the n KMD analysis results according to a plurality of integration intervals on a nominal Kendrick mass (NKM) axis and a plurality of integration intervals on a KMD axis, thereby generating n integrated value distributions as n reduced KMD analysis results; and a multivariate analyzer that applies multivariate analysis to a data set constructed based on the n integrated value distributions.

[0013] The mass spectrum processing method according to the present invention is characterized by comprising the steps of: applying Kendrick mass defect (KMD) analysis to n (n is an integer of 2 or more) mass spectra obtained by mass analysis of a plurality of samples, thereby generating n KMD analysis results; applying integration processing to the n KMD analysis results according to a plurality of integration intervals on a nominal Kendrick mass (NKM) axis and a plurality of integration intervals on a KMD axis, thereby generating n integrated value distributions as n reduced KMD analysis results; and applying multivariate analysis to a data set constructed based on the n integrated value distributions. [Effects of the Invention]

[0014] According to the present invention, when creating a data set for multivariate analysis that is reduced or compressed based on multiple mass spectra, it is possible to prevent significant loss of detailed mass information. Alternatively, according to the present invention, when creating a data set for multivariate analysis, it is possible to utilize the mass resolution function provided by KMD analysis. Alternatively, according to the present invention, it is possible to provide a mechanism for reflecting the results of a second analysis having a lower mass resolution on the results of a first analysis having a higher mass resolution. [Brief explanation of the drawings]

[0015] [Figure 1]1 is a block diagram showing an example of the configuration of a mass spectrometry system according to an embodiment. [Figure 2] FIG. 1 shows multiple mass spectra obtained from multiple samples. [Figure 3] FIG. 1 shows a peak list including the KMD analysis results. [Figure 4] FIG. 1 shows a KMD plot. [Figure 5] FIG. 1 shows multiple KMD plots. [Figure 6] FIG. 10 is a diagram showing a matrix for integration processing. [Figure 7] FIG. 10 is a diagram showing the integrated value distribution (reduced KMD plot). [Figure 8] FIG. 1 illustrates a data set. [Figure 9] FIG. 1 shows two score plots. [Figure 10] FIG. 10 is a diagram showing a loading map corresponding to the first principal component. [Figure 11] FIG. 10 is a diagram showing a loading map corresponding to the second principal component. [Figure 12] FIG. 10 is a diagram showing a loading map corresponding to the third principal component. [Figure 13] FIG. 10 is a diagram showing a positive emphasis section and a negative emphasis section. [Figure 14] FIG. 1 is a diagram showing a first example of a component-reflected KMD plot. [Figure 15] FIG. 10 is a diagram showing a second example of a component-reflected KMD plot. [Figure 16] FIG. 10 is a diagram showing a third example of a component-reflected KMD plot. [Figure 17] FIG. 10 is a block diagram illustrating another example of the configuration of the information processing device according to the embodiment. [Figure 18] FIG. 10 is a diagram showing a component map (component spectrum map) generated by vertex component analysis. [Figure 19] FIG. 1 shows a component map (phase map) generated by cluster analysis. [Figure 20] 1 is a flowchart showing a mass spectrum processing method according to an embodiment. [Figure 21] FIG. 1 is a diagram showing an analysis region set on a two-dimensional coordinate system. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, an embodiment will be described with reference to the drawings.

[0017] (1) Overview of the embodiment A mass spectrum processing apparatus according to an embodiment includes a KMD analyzer, an integrator, and a multivariate analyzer. The KMD analyzer applies Kendrick mass defect (KMD) analysis to n (n is an integer equal to or greater than 2) mass spectra obtained by mass analysis of multiple samples, thereby generating n KMD analysis results. The integrator applies integration processing to the n KMD analysis results according to multiple integration intervals on a nominal Kendrick mass (NKM) axis and multiple integration intervals on a KMD axis, thereby generating n integrated value distributions as n reduced KMD analysis results. The multivariate analyzer applies multivariate analysis to a data set constructed based on the n integrated value distributions. A processor, described below, functions as the KMD analyzer, integrator, and multivariate analyzer.

[0018] KMD analysis has a mass resolution function. That is, in KMD analysis, each m / z on the m / z axis, i.e., each mass, is converted into an integer mass (NKM) and a decimal mass (KMD). Even if multiple integration intervals are set on the NKM axis, the decimal mass information is not lost. This is because the NKM axis is an axis that represents integer mass. On the other hand, if multiple integration intervals are set on the KMD axis, some of the decimal mass information will be lost, but the entire decimal mass information will not be lost. The decrease in mass resolution is limited.

[0019] According to the above configuration, even if a reduced or compressed data set is created, decimal mass information is preserved to some extent. Therefore, it is possible to utilize the preserved decimal mass information after multivariate analysis. Examples of multivariate analysis include principal component analysis (PCA), vertical component analysis (VCA), and hierarchical cluster analysis. The above n is generally an integer of 3 or greater.

[0020] The mass spectrum processing apparatus according to the embodiment includes a generator that generates at least one plot of both analysis results based on at least one of the n KMD analysis results and the result of the multivariate analysis. The processor described below functions as the generator.

[0021] The plots of both analysis results reflect both the KMD analysis results and the multivariate analysis results. Observation or evaluation of the plots of both analysis results makes it possible to identify differences between multiple samples and the characteristics of each of the multiple samples. In an embodiment, the KMD analysis results are an ion intensity distribution having a first mass resolution, and the multivariate analysis results are a feature distribution (component distribution) having a second mass resolution lower than the first mass resolution.

[0022] In an embodiment, the n KMD analysis results are each an ion intensity distribution on a two-dimensional coordinate system defined by an NKM axis and a KMD axis. The multivariate analysis is a component analysis. The results of the multivariate analysis include a component distribution on the same two-dimensional coordinate system as the above-mentioned two-dimensional coordinate system. The generator generates at least one bi-analysis result plot based on at least one ion intensity distribution and component distribution. The at least one bi-analysis result plot is a component-reflecting KMD plot having a highlighted portion identified based on the component distribution.

[0023] For example, in a component-reflecting KMD plot, a portion corresponding to a region having a component exceeding a threshold or a portion corresponding to a region having a specific component is designated as an emphasized portion. The emphasized portion is a portion that is expressed so as to be distinguished from other portions, or a portion that is displayed in a limited manner. The emphasized portion may be distinguished from other portions by changes in brightness or hue. The component distribution corresponds to the distribution of features calculated by component analysis.

[0024] In an embodiment, the KMD analysis is performed based on the mass of the repeating unit in the sample to be analyzed. The width of each integration interval on the NKM axis is determined based on the mass of the repeating unit. Since the multiple plot positions (ion intensity plot positions) on the NKM axis are arranged at intervals corresponding to the mass of the repeating unit, according to the above configuration, multiple integration intervals are evenly allocated to the multiple plot positions. The sample to be analyzed is the sample that generated the mass spectrum that is the target of the KMD analysis.

[0025] In an embodiment, the n KMD analysis results are ion intensity distributions on a two-dimensional coordinate system defined by an NKM axis and a KMD axis, respectively. The integrator applies an integration process to the n ion intensity distributions, which are the n KMD analysis results, according to a matrix defined by multiple integration intervals on the NKM axis and multiple integration intervals on the KMD axis, thereby generating n integrated value distributions.

[0026] Each ion intensity distribution corresponds to an ion intensity matrix or a KMD plot. The ion intensity distribution may be configured as a peak list including the KMD analysis results. Each integrated value distribution corresponds to an integrated value matrix or an integrated KMD plot. The number of data (the number of integrated values) constituting the integrated value distribution is smaller than the number of data (the number of ion intensities) constituting the ion intensity distribution. When displaying an integrated value distribution, each integrated cell in the matrix may be displayed as a filled-in area, or a display element may be plotted at coordinates representing each integrated cell.

[0027] The mass spectrum processing apparatus according to the embodiment includes a preprocessor that applies a smoothing process along the KMD axis to each of the n accumulated value distributions prior to multivariate analysis and / or applies an intensity correction along the NKM axis to each of the n accumulated value distributions prior to multivariate analysis.

[0028] In the integrated value distribution, the influence of random errors is easily seen in the direction of the KMD axes, in other words, deviations in plot coordinates are easily caused. Therefore, smoothing processing is applied to the integrated value distribution in the direction of the KMD axes as necessary. Smoothing processing is not applied to the direction of the NKM axes.

[0029] In mass spectrometry, an increase in ion mass can result in a decrease in ionization efficiency, a decrease in ion detection sensitivity, and the like. This is called mass discrimination. To mitigate or eliminate the effects of such phenomena, intensity correction is applied to each integrated value distribution in the direction of the NKM axis, as necessary. In this case, multiple integrated value sequences parallel to the NKM axis may be multiplied by a correction coefficient that increases as the NKM increases. Intensity correction may also be applied to each ion intensity distribution in the direction of the NKM axis, and the same results can be obtained.

[0030] The mass spectrum processing apparatus according to the embodiment includes a normalizer that applies a normalization process to a data set. Multivariate analysis is applied to the data set after the normalization process. A processor, which will be described later, functions as the normalizer. The normalization process can optimize the multivariate analysis.

[0031] In an embodiment, the data set is composed of n portions extracted from n cumulative value distributions. This configuration allows the target of multivariate analysis to be narrowed down. The n portions may be identified according to regions set by a user or automatically in the two-dimensional coordinate system.

[0032] In an embodiment, the n KMD analysis results are ion intensity distributions on a two-dimensional coordinate system defined by the NKM axis and the KMD axis. The multivariate analysis is principal component analysis. The results of the multivariate analysis include a loading distribution on the same two-dimensional coordinate system as the above-mentioned two-dimensional coordinate system. A loading map, also called a loading plot, is a component amount plot or feature amount plot. Loadings are weights assigned to variables and correspond to the contribution of the variables to the principal components. N integrated values ​​are calculated for each coordinate on the two-dimensional coordinate system. The n integrated values ​​associated with each coordinate correspond to one variable.

[0033] The mass spectrum processing method according to the embodiment includes a first analysis step, an integration step, and a second analysis step. In the first analysis step, Kendrick mass defect (KMD) analysis is applied to n (n is an integer equal to or greater than 2) mass spectra obtained by mass analysis of multiple samples, thereby generating n KMD analysis results. In the integration step, integration processing is applied to the n KMD analysis results according to multiple integration intervals on a nominal Kendrick mass (NKM) axis and multiple integration intervals on a KMD axis, thereby generating n integrated value distributions as n reduced KMD analysis results. In the second analysis step, multivariate analysis is applied to a data set constructed based on the n integrated value distributions.

[0034] According to the above configuration, KMD analysis and multivariate analysis are applied stepwise to n mass spectra. More specifically, KMD analysis is applied to n mass spectra, and multivariate analysis is then applied to the n KMD analysis results generated thereby. Prior to the multivariate analysis, an accumulation process is applied to the n KMD analysis results, thereby suppressing loss of decimal mass information and reducing the amount of calculation required in the multivariate analysis.

[0035] The mass spectrum processing method according to the embodiment further includes a generating step and an evaluating step. In the generating step, at least one plot of both analysis results is generated based on at least one of the n KMD analysis results and the result of the multivariate analysis. In the evaluating step, differences between the multiple samples or characteristics of each of the multiple samples are evaluated based on the at least one plot of both analysis results.

[0036] A program for executing the mass spectrum processing method according to the embodiment is installed in an information processing device via a network or a portable storage medium, and the information processing device has a non-transitory storage medium in which the program is stored.

[0037] (2)KMD analysis Before describing the details of the embodiment, Kendrick Mass Defect (KMD) analysis will be described.

[0038] Mass analysis of a sample requires the use of an ion source to ionize the sample, a mass analyzer to separate and detect ions according to the value obtained by dividing the ion mass by the charge (i.e., m / z), and an information processing device to create mass spectra and process data. When the sample is a polymer, an ion source that follows a soft ionization method, such as MALDI (Matrix Assisted Laser Desorption / Ionization) or ESI (Electrospray Ionization), is usually used. When MALDI is used, mainly singly charged ions are generated. Therefore, MALDI is often used in the analysis of polymers with a molecular weight distribution. In mass analysis, the following are generally used: 12 A unit system is used in which the mass of C is expressed as 12U.

[0039] A polymer molecule contains multiple repeating units (also called monomer units) linked together. The chemical composition of the repeating units determines the type of polymer. For example, in polyethylene, the repeating unit is C2H4, in polypropylene, the repeating unit is C3H6, in polystyrene, the repeating unit is C8H8, and in polyethylene glycol, the repeating unit is C2H4O.

[0040] The mass of a polymer molecule is determined by the composition of the repeating units, the number of repeating units (degree of polymerization), and the composition of the end groups. For mass spectrometry, the polymer molecules must be ionized, and adduct ions are attached to the polymer molecule ions depending on the type of cationizing agent used. Known adduct ions include proton adduct ions, sodium adduct ions, potassium adduct ions, and silver adduct ions.

[0041] Kendrick mass defect analysis (KMD analysis) is used for polymer analysis. KMD analysis is also sometimes used for the analysis of samples other than polymers.

[0042] For a given polymer mass M, the Kendrick mass (KM) is defined as follows:

[0043] KM = M × Mri / Mr (1)

[0044] Here, Mri is the integer mass of the repeating unit, and Mr is the exact mass of the repeating unit. The integer mass of the former can be calculated from the exact mass of the latter. The exact mass is the mass including the decimal point.

[0045] The integer part of KM is defined as the Nominal Kendrick Mass (NKM). The Kendrick Mass Defect (KMD) is defined as the difference between NKM and KM as follows:

[0046] KMD = NKM - KM (2)

[0047] On the other hand, the mass M of a polymer can generally be expressed as follows:

[0048] M = Mr × n + Me + Mc (3)

[0049] Here, n is the degree of polymerization, Me is the mass of the terminal group (total mass of the two terminal groups), and Mc is the mass of the adduct ion (total mass of the adduct ions if multiple adduct ions are attached). On the right side of the above formula (3), (Mr × n) is the mass of the main chain portion, and (Me + Mc) is the mass of the portion other than the main chain portion (non-main chain portion).

[0050] By substituting the right-hand side of equation (3) for M in equation (1) above, KM can be expressed as follows:

[0051] KM = Mri × n + ( Me + Mc ) Mri / Mr (4)

[0052] Since the first term on the right side of equation (4) above is an integer, it does not contribute to KMD. Based on this, KMD can be expressed as follows from equations (2) and (4) above:

[0053] KMD = Round { ( Me + Mc ) Mri / Mr} - ( Me + Mc ) Mri / Mr (5)

[0054] The first term on the right side of the above equation (5) is NKM. Round{} means rounding off. According to the above equation (5), KMD takes a value within the range of -0.5 to +0.5. KMD does not depend on the degree of polymerization n. More specifically, KMD does not depend on the mass of the main chain portion, but depends on the mass of the non-main chain portion. In KMD analysis, the above coefficient (Mri / Mr) is used to obtain a feature quantity that is not dependent on the mass of the main chain portion.

[0055] An NKM-KMD pair is calculated for each peak in the mass spectrum of a polymer. That is, multiple NKM-KMD pairs corresponding to multiple peaks are obtained. Multiple NKM-KMD pairs are expressed as multiple display elements on a two-dimensional coordinate system having an NKM axis and a KMD axis. This generates a KMD plot (more precisely, an NKM-KMD plot).

[0056] In a KMD plot, multiple display elements corresponding to multiple peaks generated by a certain polymer are arranged at equal intervals parallel to the horizontal axis. For example, each display element is a circle, and in this case, the diameter of each circle is determined according to the area (ionic intensity) of each peak. Note that KMDs that take values ​​in the range of 0 to 1.0 are also known. Such KMDs can also be used in embodiments.

[0057] (3) Details of the embodiment A mass spectrum processing system according to an embodiment is disclosed in Figure 1. The mass spectrum processing system is used, for example, to identify differences between multiple mass spectra obtained from multiple samples, characteristics of each mass spectrum obtained from multiple samples, etc.

[0058] The mass spectrum processing system is composed of a measurement section (measurement device) 10 and an information processing section (information processing device) 12. The measurement section 10 is composed of a mass spectrometer, and the information processing section 12 is composed of a computer. The information processing section 12 corresponds to the mass spectrum processing device.

[0059] The measurement unit 10 performs mass analysis on a sample. The measurement unit 10 is composed of an ion source 16, a mass analyzer 18, and a detector 20. The ion source 16 is, for example, an ion source that complies with the MALDI method. The mass analyzer 18 is, for example, a time-of-flight mass analyzer. The detector 20 detects individual ions that have passed through the mass analyzer 18. A detection signal output from the detector 20 is converted into detection data in a signal processing circuit (not shown), and the detection data is sent to the information processing unit 12.

[0060] In this embodiment, i samples S1 to Si are subjected to mass analysis. In this embodiment, i is 6. More specifically, the i samples S1 to Si are six types of ethylene oxide-propylene oxide (EO-PO) copolymers. Mass analysis is performed j times (four times in this embodiment) on each of these EO-PO copolymers. This results in 24 mass spectra. The repeating unit in ethylene oxide is C2H4O, and the repeating unit in propylene oxide is C3H6O.

[0061] The information processing unit 12 has a processor 22, an input device 50, and a display device 52. The processor 22 performs a plurality of functions, which are represented by a plurality of blocks in FIG.

[0062] The mass spectrum generator 24 generates a mass spectrum (mass spectrum data) based on the input detection data. As described above, in this embodiment, four mass analyses are performed on each of six types of samples. The mass spectrum generator 24 generates 24 mass spectra based on 24 pieces of detection data.

[0063] The list generator 26 generates a peak list for each mass spectrum based on the mass spectrum. The peak list is a list containing multiple ion intensities corresponding to multiple peaks contained in the mass spectrum, more specifically, a list containing multiple ion intensities corresponding to multiple m / z values. When generating the peak list, for example, the m / z value corresponding to the center of gravity of each peak is identified, and the area of ​​the peak is identified as the ion intensity. An ion intensity of 0 is assigned to an m / z value where no peak exists. Specific examples of peak lists will be shown later. Note that deisotoping may be applied to the mass spectrum or peak list.

[0064] The KMD analyzer 28 applies KMD analysis to each mass spectrum. Prior to the KMD analysis, the mass (Mr) of any repeating unit contained in the sample to be analyzed is specified. In this embodiment, C2H4O or C3H6O is specified as the repeating unit.

[0065] In KMD analysis, for each peak, the m / z corresponding to the peak is converted into a combination of NKM as an integer mass and KMD as a decimal mass. In practice, each m / z in the peak list is converted into a combination of NKM and KMD. This generates a peak list containing KMD analysis results for each mass spectrum. The peak list containing the KMD analysis results corresponds to an ion intensity distribution on a two-dimensional coordinate system defined by the NKM axis and the KMD axis, or a KMD plot having the two-dimensional coordinate system. From this perspective, the KMD analyzer 28 can be referred to as an ion intensity distribution generator and a KMD plot generator. In FIG. 1, reference numeral 30 denotes the ion intensity distribution output from the KMD analyzer 28. In practice, the KMD analyzer 28 sequentially outputs 24 ion intensity distributions 30. Note that a display processor 32, described later, may generate KMD plots and the like.

[0066] The integrator 34 applies integration processing to each KMD analysis result (specifically, each ion intensity distribution 30) according to a matrix defined in the two-dimensional coordinate system. Specifically, in the two-dimensional coordinate system, multiple integration intervals are set on the NKM axis, and multiple integration intervals are set on the KMD axis. This defines a matrix. The matrix consists of multiple integration cells arranged two-dimensionally. Specific examples of the matrix will be shown later.

[0067] The integrator 34 adds up the ion intensities belonging to each integrating cell to obtain an integrated value. The integrated value distribution is formed by the integrated values ​​on a two-dimensional coordinate system defined by the NKM axis and the KMD axis. The integrated value distribution may also be referred to as an integrated value plot (integrated KMD plot). A peak list having multiple coordinates on the two-dimensional coordinate system and multiple integrated values ​​associated with them may be generated as the integrated value distribution.

[0068] The size ΔNKM of each integration interval on the NKM axis and the size ΔKMD of each integration interval on the KMD axis are specified by the user or automatically specified depending on the situation. ΔNKM may be determined based on the mass (Mr) of the repeating unit. For example, ΔNKM may be determined by Round {Mr} × k or Round {Mr} × (1 / k). Such determination makes it possible to evenly distribute the intensities of multiple ions discretely arranged on the NKM axis to multiple integration intervals on the NKM axis.

[0069] Considering that random mass errors occurring in the measuring unit (mass spectrometer) 10 appear on the KMD axis, ΔKMD may be determined based on the random mass error. For example, ΔKMD may be set to twice the random mass error. The random mass error can be easily determined by experiment.

[0070] The accumulator 34 applies preprocessing to each of the generated integrated value distributions as necessary. That is, the accumulator 34 functions as a preprocessor. For example, the preprocessing may include a first preprocessing and a second preprocessing.

[0071] In the first preprocessing, smoothing along the KMD axis is applied to each integrated value distribution. Each integrated value distribution consists of multiple integrated value columns (multiple vertical columns) parallel to the KMD axis. For example, a one-dimensional smoothing filter is applied to each integrated value column. Integrated value distributions are susceptible to the influence of random errors in the KMD axis direction, in other words, deviations in plot coordinates are likely to occur. The first preprocessing can mitigate the influence of random errors. Generally, smoothing along the NKM direction is unnecessary.

[0072] In the second pre-processing, intensity correction along the NKM axis is applied to each integrated value distribution. Each integrated value distribution consists of multiple integrated value rows (multiple horizontal rows) parallel to the NKM axis. Each integrated value row is multiplied by a correction coefficient. The correction coefficient increases as the NKM increases. Alternatively, a correction coefficient greater than 1 is set in the high mass range on the NKM axis. In mass spectrometry, an increase in ion mass can cause a decrease in ionization efficiency, a decrease in ion detection sensitivity, and the like. The second pre-processing is performed to mitigate or eliminate the effects of such phenomena. In FIG. 1, reference numeral 36 denotes an integrated value distribution output from the integrator 34. In practice, 24 integrated value distributions are output from the integrator 34.

[0073] The dataset creator 38 creates a dataset based on the multiple accumulation value distributions 36, in this embodiment, based on 24 accumulation value distributions. The dataset may be referred to as a reduced or compressed dataset. More specifically, the dataset creator 38 generates one-dimensional accumulation value sequences from each of the two-dimensional accumulation value distributions. The dataset is then created by arranging the 24 one-dimensional accumulation value sequences in order of spectrum number.

[0074] The normalizer 40 applies normalization processing to a data set prior to multivariate analysis (principal component analysis in the example shown in FIG. 1). Examples of normalization processing include a first normalization processing and a second normalization processing. A data set is made up of a plurality of horizontal integrated value columns arranged in the vertical direction, or, from another perspective, a plurality of vertical integrated value columns arranged in the horizontal direction. In the first normalization processing, each horizontal integrated value column is normalized. In the second normalization processing, each horizontal integrated value column is normalized. The first normalization processing and the second normalization processing are usually performed selectively. The first normalization processing and the second normalization processing will be described in detail later.

[0075] The principal component analyzer 42 performs principal component analysis based on the data set. By performing principal component analysis, multiple principal components are identified, and a score for each principal component is calculated for each mass spectrum (i.e., each sample). During this process, a loading (loading value) is calculated for each principal component and for each coordinate on the two-dimensional coordinate system. The loading is a weight for a variable and represents the contribution of the variable to the principal component. In this embodiment, one variable corresponds to one coordinate (to be precise, 24 integrated values ​​associated with that coordinate).

[0076] Each integrated value constituting the data set is associated with a pair of NKM (specifically, NKM section identifier) ​​and KMD (specifically, KMD identifier). A pair of NKM and KMD is also associated with each loading corresponding to each integrated value.

[0077] The score plot generator 44 generates one or more score plots based on the results of the principal component analysis. Generally, a score plot has two principal component axes: one principal component axis represents a specific principal component, and the other principal component axis represents another specific principal component.

[0078] The loading map generator 46 generates one or more loading maps corresponding to one or more principal components based on the results of the principal component analysis. Each loading map is composed of multiple loadings represented on a two-dimensional coordinate system defined by the NKM axis and the KMD axis. A loading map may also be called a loading distribution or a loading plot. From another perspective, a loading map is a component map, a component amount map, or a feature amount map.

[0079] Generally, in terms of comparative or differential analysis of multiple samples, regions of a loading map with large positive or large negative loadings are more important regions, whereas regions with small positive or small negative loadings are less important regions.

[0080] In an embodiment, data representing the individual loading maps is sent to the display processor 32 and the post-processor 48. The principal component analyzer 42 or the loading map generator 46 may provide the post-processor 48 with a list or table containing multiple loadings and their corresponding coordinate information for each principal component.

[0081] The post-processing unit 48 applies post-processing based on a specific loading map corresponding to the selected principal component to a specific KMD plot (ion intensity distribution) corresponding to the selected mass spectrum, thereby generating a component-reflecting KMD plot as a plot of both analysis results. Specifically, the post-processing unit 48 identifies an emphasis region based on the specific loading map and generates a KMD plot having an emphasis portion corresponding to the emphasis region. The KMD plot is the component-reflecting KMD plot.

[0082] For example, a highlighted region may be identified as a region having a positive loading above a positive threshold or a negative loading above a negative threshold. In an embodiment, post-processor 48 also includes a color calculation function. The highlighted region may be distinguished from other regions by brightness or hue.

[0083] The KMD plot has a first mass resolution, and the loading map has a second mass resolution that is lower than the first resolution. Since the component-reflected KMD plot is generated by post-processing of the KMD plot, the component-reflected KMD plot essentially has the first mass resolution. Furthermore, the important parts are roughly emphasized in the component-reflected KMD plot. The component-reflected KMD plot provides very useful information for comparative or differential analysis of multiple samples.

[0084] A mass spectrum and a principal component of interest may be selected or sequentially selected by the user. In this case, the post-processor 48 selects a specific KMD plot corresponding to the mass spectrum of interest from the generated KMD plots. The post-processor 48 also selects a specific loading map corresponding to the principal component of interest from the generated loading maps. A component-reflected KMD plot may be automatically generated for each combination defined based on the generated KMD plots and the generated loadings. A component-reflected polymerized KMD plot may be generated based on a polymerized KMD plot consisting of multiple KMD plots.

[0085] Data representing a plurality of mass spectra, data representing a plurality of KMD plots, data representing a plurality of component-reflected KMD plots, data representing a plurality of integrated value plots, data representing a plurality of score plots, and data representing a plurality of loading maps are input to the display processor 32. The display processor 32 selects one or more images to be displayed on the display 52 in accordance with a user instruction.

[0086] For example, based on the displayed KMD plots reflecting the multiple components, the user may identify differences between the multiple mass spectra or the relative characteristics of each of the multiple mass spectra. Through such evaluation, the best sample suitable for a specific purpose may be selected from the multiple samples. A material constituting a specific chemical product may be selected as the best sample.

[0087] The display processor 32 may function as an evaluator 53. The evaluator 53 identifies differences between multiple mass spectra based on multiple post-processed KMD plots or identifies relative characteristics of each of the multiple mass spectra. User instructions are received via an input device 50. The input device 50 is configured with a keyboard and a pointing device. The display 52 is configured with a liquid crystal display.

[0088] The mass spectrometry system and mass spectrum processing method according to the embodiment will be described in more detail below.

[0089] Six mass spectra (A1) to (A6) obtained from the six types of EO-PO copolymers described above are shown in Figure 2. The horizontal axis of each mass spectrum is the m / z axis, and the vertical axis of each mass spectrum is the intensity axis.

[0090] FIG. 3 shows an example of a peak list including KMD analysis results. Reference numeral 54 indicates a portion corresponding to the peak list generated by the list generator. The portion 54 has a plurality of m / z values ​​on the m / z axis and a plurality of corresponding ion intensities. The peak list 54A includes the KMD analysis results. That is, for each m / z value, an NKM as an integer mass and a KMD as a decimal mass are associated with the m / z value. In this embodiment, i×j (specifically, 24) peak lists 54A are generated. The calculations of NKM and KMD may be performed in advance, and the calculation results may be registered in advance.

[0091] FIG. 4 shows an example of a KMD plot (ion intensity distribution). Prior to generating the KMD plot, the mass of the repeating unit is specified. In an embodiment, for example, 58.042, which is the mass of the repeating unit CHO in polypropylene oxide, is specified. In FIG. 4, the KMD plot 56 includes a plurality of display elements corresponding to a plurality of peaks contained in the mass spectrum. Each display element 56a is a circle, and its diameter represents the ion intensity. The horizontal axis of the KMD plot 56 is the KMD axis representing NKM, and the vertical axis is the KMD axis representing KMD.

[0092] Figure 5 shows a polymerized KMD plot 58. The polymerized KMD plot 58 is composed of six KMD plots generated from the six mass spectra shown in Figure 3. The content of the polymerized KMD plot 58 is complex, making it very difficult to perform comparative analysis and differential analysis using the polymerized KMD plot 58.

[0093] An example of a matrix is ​​shown in Figure 6. A two-dimensional coordinate system is defined by the NKM axis, which is the horizontal axis, and the KMD axis, which is the vertical axis. A matrix 60 is defined on this two-dimensional coordinate system. Multiple integration intervals are set for the NKM axis. The width of each integration interval is ΔNKM. Similarly, multiple integration intervals are set for the KMD axis. The width of each integration interval is ΔKMD. Multiple integration cells 62 are defined by the multiple integration intervals on the NKM axis and the multiple integration intervals on the KMD axis.

[0094] Based on the ion intensity distribution (KMD plot), for each integration cell 62, the multiple ion intensities belonging thereto are integrated to determine an integrated value. The integrated value is associated with the coordinates of each cell (specifically, a combination of the interval coordinates on the NKM axis and the interval coordinates on the KMD axis). The integrated value distribution is made up of multiple integrated values ​​corresponding to multiple integration cells. In the matrix 60 shown in FIG. 6, the first coordinate is (x1, y1) and the last coordinate is (xmax, ymax).

[0095] In this embodiment, for example, ΔNKM is 58 and ΔKMD is 0.02. In this case, 60 integration intervals are set on the NKM axis and 50 integration intervals are set on the KMD axis. The number of integration cells 62 is 3000. A large compression ratio is achieved while avoiding a large loss of fractional mass information.

[0096] Converting the ion intensity distribution into an integrated value distribution can significantly reduce the amount of data in the data set subjected to principal component analysis. Moreover, because the integer mass information and the decimal mass information are resampled independently, the decimal mass information, which is important for comparative and differential analysis, is not significantly lost.

[0097] The integrated value distribution is subjected to a first pre-processing and / or a second pre-processing as needed. The first pre-processing is a smoothing process along the KMD axis (see reference numeral 64), as already explained. The second pre-processing is an intensity correction process along the KNM axis (see reference numeral 66), as already explained.

[0098] FIG. 7 shows an example of an integrated value distribution (integrated KMD plot). The illustrated integrated value distribution 68 is made up of a plurality of display elements. Each display element corresponds to an integration cell and has a rectangular shape. The density (actually, the hue) of each display element represents the magnitude of the integrated value.

[0099] An example of a data set is shown in Fig. 8. The illustrated data set 70 is made up of a plurality of subsets 72-1 to 72-ij arranged in the order of sample numbers. Here, ij is 24 in this embodiment. Each of the subsets 72-1 to 72-ij is a one-dimensional integrated value sequence generated by converting the integrated value distribution. The one-dimensional integrated value sequence is made up of a plurality of integrated values ​​71. In each of the subsets 72-1 to 72-ij, the first coordinate corresponds to the above coordinates (x1, y1), and the last coordinate corresponds to the above coordinates (xmax, ymax).

[0100] The first normalization process normalizes each of the subsets 72-1 to 72-ij. Specifically, the multiple integrated values ​​are corrected so that the sum Σ1 of the multiple integrated values ​​constituting each integrated value row (each horizontal row) becomes a predetermined value. The multiple total ion intensities corresponding to the multiple mass spectra vary. The first normalization process can suppress the effects of this variation.

[0101] The data set 70 consists of multiple vertical columns arranged horizontally. In the second normalization process, for each vertical column, the sum Σ2 of the multiple integrated values ​​constituting that vertical column is calculated, and the multiple integrated values ​​are corrected so that the sum Σ2 becomes a predetermined value. When the total ion intensity varies among multiple coordinates, the second normalization process can mitigate the effects of the variation. Applying the normalization process to the data set enables proper execution of principal component analysis.

[0102] 9 shows two score plots 73 and 74. In the score plot 73, the horizontal axis is the first principal component axis, and the vertical axis is the second principal component axis. Each point 73a indicates a combination of the first principal component score and the second principal component score corresponding to each sample.

[0103] In the score plot 74, the horizontal axis is the first principal component axis, and the vertical axis is the third principal component axis. Each point 74a indicates a combination of the first principal component score and the third principal component score corresponding to each sample. By observing or evaluating such a score plot, the relationship between multiple spectra, i.e., the relationship between multiple samples, can be visually recognized or evaluated.

[0104] FIG. 10 shows a loading map 76 corresponding to the first principal component. The loading map 76 is a loading distribution on a two-dimensional coordinate system defined by the NKM and KMD axes. The loading map 76 is composed of multiple elements 76a representing multiple loadings, and each element 76a corresponds to an integration cell. The density of each element 76a represents the magnitude of the loading. In practice, the magnitude of positive loadings and the magnitude of negative loadings are represented by changes in hue. By observing or evaluating the loading map 76, it is possible to identify which parts of the multiple integration value distributions as a whole contribute significantly to the first principal component.

[0105] A loading map 78 corresponding to the second principal component is shown in Figure 11. A loading map 80 corresponding to the third principal component is shown in Figure 12.

[0106] Next, post-processing will be described. In an embodiment, for example, as shown in FIG. 13, a first emphasized section 82A and a second emphasized section 82B are set on the loading axis. The first emphasized section 82A is a section exceeding a positive threshold, and the second emphasized section 82B is a section exceeding a negative threshold. When a loading of interest belongs to the first emphasized section 82A or the second emphasized section 82B, a display element representing the ion intensity corresponding to the loading of interest (more precisely, the ion intensity of the peak in the mass spectrum) is highlighted in the KMD plot. The highlighted portion may be defined by other methods. Alternatively, instead of highlighting, display / non-display may be switched.

[0107] FIG. 14 shows a first example of a component-reflected KMD plot. In the illustrated component-reflected KMD plot 84, for example, a first portion 84A consists of a plurality of display elements corresponding to a plurality of positive loadings belonging to a first highlight interval. For convenience, each such display element is referred to as a display element satisfying a first highlight condition. The display element satisfying the first highlight condition has a first color. Also, for example, a second portion 84B consists of a plurality of display elements corresponding to a plurality of negative loadings belonging to a second highlight interval. For convenience, each such display element is referred to as a display element satisfying a second highlight condition. The display elements have a second color different from the first color.

[0108] Reference numeral 86 denotes a display element corresponding to loading that does not belong to any highlighted section. Such a display element is displayed with low brightness. Such a display element may also be hidden.

[0109] 15 shows a second example of a component-reflected KMD plot. In the illustrated component-reflected KMD plot 88, for example, the background of a first region 90A containing a plurality of display elements that satisfy a first highlighting condition is displayed in a first color. The background of a second region 90B containing a plurality of display elements that satisfy a second highlighting condition is displayed in a second color. Note that reference numeral 92A indicates the outer edge of the first region 90A, and reference numeral 92B indicates the outer edge of the second region 90B. Display elements that do not satisfy the first highlighting condition or the second highlighting condition are not highlighted or are hidden (see reference numeral 86).

[0110] 16 shows a third example of a component-reflected KMD plot. In the illustrated component-reflected KMD plot 94, for example, an outer edge 96A of a first region containing multiple display elements that satisfy a first highlighting condition is displayed in a first color. An outer edge 96B of a second region containing multiple display elements that satisfy a second highlighting condition is displayed in a second color. Display elements that do not satisfy the first highlighting condition or the second highlighting condition are not highlighted or are not displayed (see reference numeral 86).

[0111] As described above, according to the embodiment, it is possible to generate a special KMD plot that highlights important parts from the viewpoint of comparative analysis or difference analysis, and since the special KMD plot has the same mass resolution as the first mass resolution of the original KMD plot, it can be precisely evaluated in a two-dimensional mass coordinate system.

[0112] Another example of the configuration of the information processing device according to the embodiment is shown in Fig. 17. In Fig. 17, elements that are the same as those shown in Fig. 1 are given the same reference numerals, and descriptions thereof will be omitted.

[0113] 17 includes an analyzer 42A. The analyzer 42A performs vertex component analysis or hierarchical cluster analysis. The component analysis results from the analyzer 42A are sent to a map generator 46A and also sent to the display processor 32. The map generator 46A generates a component map (component distribution) that represents the component analysis results.

[0114] When analyzer 42A performs vertex component analysis based on a data set, map generator 46A generates, for example, component map 100 shown in Fig. 18. Component map 100 indicates the distribution of multiple components (component spectra).

[0115] In the illustrated example, the first component (first component spectrum) is dominant in region F1, the second component (second component spectrum) is dominant in region F2, the third component (third component spectrum) is dominant in region F3, and the fourth component (fourth component spectrum) is dominant in region F4. Reference numeral 102 denotes a display element corresponding to an integrating cell.

[0116] A component-reflected KMD map is generated by reflecting component map 100 on the KMD map. For example, a specific component (specific region) may be designated in component map 100, and the portion of the KMD map corresponding to the specific region may be highlighted.

[0117] When hierarchical cluster analysis is performed based on a data set in analyzer 42A shown in Fig. 17, map generator 46A generates, for example, component map 104 shown in Fig. 19. Region G1 is a portion classified as the first component (first phase), region G2 is a region classified as the second component (second phase), region G3 is a region classified as the third component (third phase), and region G4 is a region classified as the fourth component (fourth phase).

[0118] A component-reflected KMD map is generated by reflecting component map 104 on the KMD map. For example, a specific component (specific region) may be designated in component map 104, and a portion of the KMD map corresponding to the specific region may be highlighted.

[0119] 20 shows a mass spectrum processing method according to an embodiment. In S10, multiple samples S to be compared are prepared. In S12, mass analysis is performed sequentially on the multiple samples S. This generates n mass spectra, where n is an integer of 2 or greater, and generally an integer of 3 or greater.

[0120] In S14, KMD analysis is sequentially performed on n mass spectra, thereby generating n KMD plots (ion intensity distributions) 106 as the n KMD analysis results. In KMD analysis, each m / z is decomposed into NKM, which is an integer mass, and KMD, which is a decimal mass. Also, in S14, a polymerized KMD plot 108 is generated by combining all or part of the n KMD plots 106.

[0121] In S16, n integrated value distributions are generated by integrating n KMD plots 106. The n integrated value distributions constitute a reduced data set 109. In S18, component analysis is applied to the data set 109. As a result, a component map or component amount map (including a loading map) 113 is generated.

[0122] In S22, post-processing based on the component map 113 is applied to the selected KMD plot 106, thereby generating a KMD plot with highlighted portions (component-reflected KMD plot) 110. Multiple component-reflected KMD plots 110 may be generated based on multiple KMD plots 106, or a component-reflected stacked KMD plot 112 may be generated based on a stacked KMD plot 108.

[0123] In S24, the user observes one or more component-reflecting KMD plots 110, 112, evaluates differences between the multiple samples, and evaluates the characteristics of each sample. Based on the results of such evaluation, a sample suitable for a specific purpose may be selected from the multiple samples. In the comparative analysis or difference analysis, other information 111 representing the component analysis results may be provided to the user.

[0124] 21 shows an accumulated value distribution 118. A portion belonging to a specified specific region 114 may be extracted from the accumulated value distribution 118. For example, a data set for multivariate analysis may be configured by n portions extracted from n accumulated value distributions 118. The specific region 114 may be specified by a user, or may be set automatically. Note that a portion 116 to be excluded from the target of multivariate analysis may be specified in the accumulated value distribution 118. A target of multivariate analysis may be specified in the ion intensity distribution (i.e., the KMD map), or a portion to be excluded from the target of multivariate analysis may be specified. [Explanation of symbols]

[0125] 10 measurement unit, 12 information processing unit, 22 processor, 28 KMD analyzer, 34 accumulator, 38 dataset creator, 40 normalizer, 42 principal component analyzer, 44 score plot generator, 46 loading map generator, 32 display processor.

Claims

1. a Kendrick mass defect (KMD) analyzer that applies KMD analysis to n (n is an integer of 2 or more) mass spectra obtained by mass spectrometry of a plurality of samples, thereby generating n KMD analysis results; an integrator that applies an integration process to the n KMD analysis results according to a plurality of integration intervals on a nominal Kendrick Mass (NKM) axis and a plurality of integration intervals on a KMD axis, thereby generating n integrated value distributions as n reduced KMD analysis results; a multivariate analyzer that applies multivariate analysis to a data set that is constructed based on the n cumulative value distributions; A mass spectrum processing apparatus comprising:

2. 2. The mass spectrum processing apparatus according to claim 1, a generator that generates at least one bi-analysis result plot based on at least one of the n KMD analysis results and the result of the multivariate analysis; A mass spectrum processing apparatus characterized by:

3. 3. The mass spectrum processing apparatus according to claim 2, Each of the n KMD analysis results is an ion intensity distribution on a two-dimensional coordinate system defined by the NKM axis and the KMD axis, the multivariate analysis is a component analysis, the result of the multivariate analysis includes a component distribution on the same two-dimensional coordinate system as the two-dimensional coordinate system; the generator generates the at least one bianalysis result plot based on at least one ion intensity distribution and the component distribution. A mass spectrum processing apparatus characterized by:

4. 4. The mass spectrum processing apparatus according to claim 3, The at least one analysis result plot is a component-reflecting KMD plot having an emphasized portion identified based on the component distribution. A mass spectrum processing apparatus characterized by:

5. 2. The mass spectrum processing apparatus according to claim 1, The KMD analysis is performed based on the mass of the repeating unit in the sample to be analyzed, The width of each integration interval on the NKM axis is determined based on the repeating unit. A mass spectrum processing apparatus characterized by:

6. 2. The mass spectrum processing apparatus according to claim 1, Each of the n KMD analysis results is an ion intensity distribution on a two-dimensional coordinate system defined by the NKM axis and the KMD axis, the integrator applies the integration process to n ion intensity distributions, which are the n KMD analysis results, in accordance with a matrix defined by the plurality of integration intervals on the NKM axis and the plurality of integration intervals on the KMD axis, thereby generating the n integrated value distributions. A mass spectrum processing apparatus characterized by:

7. 7. The mass spectrometer according to claim 6, a preprocessor that applies a smoothing process to each of the n integrated value distributions in the direction of the KMD axis prior to the multivariate analysis; A mass spectrum processing apparatus characterized by:

8. 7. The mass spectrometer according to claim 6, a preprocessor that applies intensity correction in the direction of the NKM axis to each of the n integrated value distributions prior to the multivariate analysis; A mass spectrum processing apparatus characterized by:

9. 2. The mass spectrum processing apparatus according to claim 1, a normalizer that applies a normalization process to the data set; The multivariate analysis is applied to the data set after the standardization process. A mass spectrum processing apparatus characterized by:

10. 2. The mass spectrum processing apparatus according to claim 1, The data set is composed of n portions extracted from the n cumulative value distributions. A mass spectrum processing apparatus characterized by:

11. 2. The mass spectrum processing apparatus according to claim 1, Each of the n KMD analysis results is an ion intensity distribution on a two-dimensional coordinate system defined by the NKM axis and the KMD axis, the multivariate analysis is principal component analysis, The result of the multivariate analysis includes a loading distribution on the same two-dimensional coordinate system as the two-dimensional coordinate system. A mass spectrum processing apparatus characterized by:

12. applying Kendrick mass defect (KMD) analysis to n (n is an integer of 2 or more) mass spectra obtained by mass spectrometry of a plurality of samples, thereby generating n KMD analysis results; applying an integration process to the n KMD analysis results according to a plurality of integration intervals on a nominal Kendrick Mass (NKM) axis and a plurality of integration intervals on a KMD axis, thereby generating n integrated value distributions as n reduced KMD analysis results; applying multivariate analysis to a data set constructed based on the n cumulative value distributions; A mass spectrum processing method comprising:

13. 13. The mass spectrum processing method according to claim 12, further generating at least one plot of both analysis results based on at least one of the n KMD analysis results and the result of the multivariate analysis; evaluating differences between the samples or characteristics of each of the samples based on the at least one plot of both analytical results; A mass spectrum processing method comprising:

14. A program for executing a mass spectrum processing method in an information processing device, The mass spectrum processing method includes: applying Kendrick mass defect (KMD) analysis to n (n is an integer of 2 or more) mass spectra obtained by mass spectrometry of a plurality of samples, thereby generating n KMD analysis results; applying an integration process to the n KMD analysis results according to a plurality of integration intervals on a nominal Kendrick Mass (NKM) axis and a plurality of integration intervals on a KMD axis, thereby generating n integrated value distributions as n reduced KMD analysis results; applying multivariate analysis to a data set constructed based on the n cumulative value distributions; A program comprising:

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