Sample analysis device and method

Abstract

To analyze variance among a plurality of samples by the use of ion intensity variation over the plurality of samples.SOLUTION: Based upon m×n mass spectra generated by performing mass analysis of each of m samples n times, m×n peak lists are generated (S12). Based upon the m×n peak lists, a table 58 is generated (S14). A plurality of ion intensity vectors in the table 58 are compared with a template 66 to generate a similarity sequence 72 (S18). Based upon the similarity sequence 72, a specific peak group is extracted from the m×n mass spectra, etc. (S22).SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a sample analysis device and method, and more particularly to a technique for identifying components whose quantities vary across multiple samples. [Background technology]

[0002] In differential analysis, mass spectrometry is generally applied to multiple samples to be analyzed, and the differences between the samples are identified by comparing the resulting mass spectra. For example, between a first sample and a second sample, components (compounds) that are present in one sample but not in the other are identified. The multiple samples that are the subject of differential analysis can be, for example, multiple materials with different degrees of degradation, multiple materials manufactured under different manufacturing conditions, or multiple materials that have undergone different evaluations.

[0003] Various mass spectrum analysis methods for differential analysis are known. For example, principal component analysis, hierarchical clustering, etc. are known. However, these methods do not utilize changes in ion intensity across multiple samples in mass spectrum analysis. Patent Documents 1 and 2 disclose mass spectrum analysis techniques. However, these patent documents do not disclose mass spectrum analysis techniques that utilize changes in ion intensity across multiple samples. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2018 / 020652 [Patent Document 2] Japanese Patent Application Publication No. 2019-132751 Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present invention is to provide a new sample analysis device and method that focuses on changes in ion intensity across multiple samples, or to realize differential analysis that utilizes the relationships between multiple samples. [Means for solving the problem]

[0006] A sample analyzer according to the present invention includes a mass analyzer that acquires m×n mass spectra by performing mass analysis n times (where n is an integer of 1 or more) on each of m samples (where m is an integer of 2 or more) that are the subject of differential analysis; a list creator that creates m×n peak lists based on the m×n mass spectra; a table creator that creates a table of a plurality of ion intensity vectors corresponding to a plurality of mass-to-charge ratios based on the m×n peak lists, the table creator defining the ion intensity vectors for each mass-to-charge ratio based on the m×n ion intensities; a comparison unit that compares the plurality of ion intensity vectors with a template for the differential analysis and thereby calculates a plurality of evaluation values; and a processing unit that applies processing for the differential analysis to the m×n mass spectra, the m×n peak lists, or the plurality of ion intensity vectors based on the plurality of evaluation values. each ion intensity vector is configured as a sequence of the m×n ion intensities, and the template is configured as a sequence of m×n coefficients corresponding to the sequence of the m×n ion intensities; It is characterized by:

[0007] A sample analysis method according to the present invention includes the steps of: performing mass analysis n times (where n is an integer of 1 or more) on each of m samples (where m is an integer of 2 or more) that are the subject of differential analysis, thereby obtaining m×n mass spectra; creating m×n peak lists based on the m×n mass spectra; creating a table containing a plurality of ion intensity vectors corresponding to a plurality of mass-to-charge ratios based on the m×n peak lists, wherein the ion intensity vectors are defined for each mass-to-charge ratio based on the m×n ion intensities; comparing the plurality of ion intensity vectors with a template for the differential analysis, thereby calculating a plurality of evaluation values; and applying a process for the differential analysis to the m×n mass spectra, the m×n peak lists, or the plurality of ion intensity vectors based on the plurality of evaluation values. each ion intensity vector is configured as a sequence of the m×n ion intensities, and the template is configured as a sequence of m×n coefficients corresponding to the sequence of the m×n ion intensities; It is characterized by: [Effects of the Invention]

[0008] According to the present invention, a new sample analysis device and method can be provided that focuses on changes in ion intensity across multiple samples, or that can realize differential analysis that utilizes the relationships between multiple samples. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing a sample analyzer according to an embodiment. [Figure 2] FIG. 1 is an explanatory diagram showing a sample analysis method according to an embodiment. [Figure 3] FIG. 10 is a diagram showing the creation of a peak list. [Figure 4] FIG. 10 is a diagram illustrating an example of a table. [Figure 5] FIG. 10 is a diagram illustrating an example of a template. [Figure 6] FIG. [Figure 7] FIG. 10 is a diagram showing a mass spectrum after identification processing. [Figure 8] FIG. 10 shows a discriminatively processed KMD plot. [Figure 9] FIG. [Figure 10] FIG. 10 is a diagram illustrating another example of a template. [Figure 11] FIG. 10 is a diagram illustrating an example of a template set. [Figure 12] FIG. 1 shows a first mass spectrum. [Figure 13] FIG. 1 shows a first KMD plot. [Figure 14] FIG. 10 shows a second mass spectrum. [Figure 15] FIG. 2 shows a second KMD plot. [Figure 16] FIG. 10 shows a third mass spectrum. [Figure 17] FIG. 3 shows a third KMD plot. [Figure 18] FIG. 10 is a diagram showing a sample analysis method according to a second embodiment. [Figure 19] FIG. 10 is a diagram showing a sample analysis method according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment will be described with reference to the drawings.

[0011] (1) Overview of the embodiment A sample analyzer according to an embodiment includes a mass analyzer, a list creator, a table creator, a comparison unit, and a processing unit. The mass analyzer performs mass analysis n times (where n is an integer greater than or equal to 1) on each of m samples (where m is an integer greater than or equal to 2) that are the subject of differential analysis, thereby obtaining m×n mass spectra. The list creator creates m×n peak lists based on the m×n mass spectra. The table creator creates a table containing multiple ion intensity vectors corresponding to multiple mass-to-charge ratios based on the m×n peak lists. More specifically, the table creator defines ion intensity vectors for each mass-to-charge ratio based on the m×n ion intensities. The comparison unit compares the multiple ion intensity vectors with a template for differential analysis, thereby calculating multiple evaluation values. The processing unit applies differential analysis processing to the m×n mass spectra, the m×n peak lists, or the multiple ion intensity vectors based on the multiple evaluation values.

[0012] An ion intensity change of interest (an ion intensity sequence of interest) is registered as a template. The template is compared with multiple ion intensity vectors in the table. Based on multiple evaluation values ​​obtained in this way, multiple ion intensity vectors that have a certain relationship with the template are identified, that is, multiple peaks (specific peak groups) that have a certain relationship with the template are identified.

[0013] By manipulating the content of the template, various differential analyses can be performed. For example, information on components that are present in a first sample but not in a second sample, information on components that decrease over time, information on components that increase over time, etc. The sample analysis method according to the embodiment is a new sample analysis method that focuses on changes in ion intensity across multiple samples; in other words, it is a new sample analysis method that utilizes known or existing relationships between multiple samples.

[0014] The ion intensity vector is composed of m x n ion intensities, or m representative values. Each representative value is the average value of n ion intensities, the median value of n ion intensities, etc. The mass analysis unit corresponds to the measurement unit and mass spectrum creator described below. The list creation unit corresponds to the peak detector described below. The table creation unit corresponds to the table creator described below. The comparison unit corresponds to the comparator described below. The processing unit corresponds to the processing unit described below.

[0015] In an embodiment, n is an integer equal to or greater than 3. Each ion intensity vector is configured as a sequence of m×n ion intensities, and the template is configured as a sequence of m×n coefficients corresponding to the sequence of m×n ion intensities. By setting n to an integer equal to or greater than 3, the influence of noise can be reduced and analytical accuracy can be improved.

[0016] In an embodiment, the m×n coefficients are composed of m coefficient sets corresponding to m samples. Each coefficient set is composed of n coefficients having the same value. If a change in ion intensity occurs during n measurements for each sample due to a change in measurement conditions, etc., the change may be reflected in the template. In other words, the values ​​of the n coefficients constituting each coefficient set may be different.

[0017] In an embodiment, the m samples include a first sample and a second sample. The evaluation value is a similarity. When identifying a compound that is contained in a greater amount in the first sample than in the second sample, the value of each coefficient corresponding to the first sample in the template is greater than the value of each coefficient corresponding to the second sample.

[0018] In an embodiment, the comparison unit applies multiple templates for difference analysis to multiple ion intensity vectors in parallel. This configuration allows multiple peak groups to be extracted in parallel. In an embodiment, the comparison unit applies multiple templates for difference analysis to multiple ion intensity vectors in a stepwise manner. This configuration allows multiple peak groups to be extracted in a stepwise manner. As a result of the stepwise extraction of multiple peak groups, the table is reduced in a stepwise manner.

[0019] In an embodiment, the m samples include a first sample and a second sample. The process for differential analysis includes extracting a specific group of peaks contained in the m×n mass spectra or the m×n peak list. The specific group of peaks is, for example, a group of peaks corresponding to compounds contained in a higher amount in the first sample than in the second sample.

[0020] In an embodiment, the processing for the difference analysis further includes processing for generating an image representing the specific peak group so that the specific peak group can be distinguished from other peak groups. An image including only display elements representing the specific peak group may be generated, or an image including display elements representing the specific peak group that have been subjected to the distinguishing processing may be generated.

[0021] The sample analyzer according to the embodiment includes a creation unit that creates a template based on a user's instructions. For example, a template is created depending on the purpose of differential analysis. Alternatively, the sample analyzer according to the embodiment includes a creation unit that creates a template based on a specific ion intensity vector in a table. This configuration makes it possible to identify other compounds that have quantitative changes similar to those of a given compound.

[0022] In an embodiment, the m samples are m samples having different degrees of degradation, m samples manufactured under different manufacturing conditions, or m samples that have been subjected to different evaluations. The m samples may include samples evaluated as good and samples evaluated as defective.

[0023] A sample analysis method according to an embodiment includes a mass analysis step, a list creation step, a table creation step, and a processing step. In the mass analysis step, m samples (where m is an integer equal to or greater than 2) are subjected to differential analysis, and n mass analyses (where n is an integer equal to or greater than 1) are performed on each of these samples. This results in m×n mass spectra. In the list creation step, m×n peak lists are created based on the m×n mass spectra. In the table creation step, a table containing multiple ion intensity vectors corresponding to multiple mass-to-charge ratios is created based on the m×n peak lists. More specifically, an ion intensity vector is defined for each mass-to-charge ratio based on m×n ion intensities. In the comparison step, the multiple ion intensity vectors are compared with a template for differential analysis, thereby calculating multiple evaluation values. In the processing step, differential analysis processing is applied to the m×n mass spectra, the m×n peak lists, or the multiple ion intensity vectors based on the multiple evaluation values.

[0024] The list creation step, table creation step, and processing step in the sample analysis method can be realized as software functions. A program for executing the sample analysis method is installed in an information processing device via a portable storage medium or a network. The concept of an information processing device includes, for example, a computer, a mass spectrum processing device, a sample analysis device, etc. The information processing device has a non-transitory storage medium that stores the program.

[0025] (2) Details of the embodiment Figure 1 shows a sample analyzer according to an embodiment. The sample analyzer has the function of performing differential analysis on multiple samples. The sample analyzer is broadly composed of a measurement unit 10 and an information processing unit 12. The measurement unit 10 is composed of a mass spectrometer, and the information processing unit 12 is composed of an information processing device.

[0026] First, the measurement unit 10 will be described. In an embodiment, the sample set 14 is composed of m samples. The m samples are the measurement targets. m is an integer of 2 or more, and in an embodiment, m is 3. More samples may be the measurement targets. n measurements (mass spectrometry) are performed for each sample. n is an integer of 1 or more, and in an embodiment, n is 3. More measurements may be performed for each sample. In order to reduce the effects of noise and various errors, n is generally 3 or more. In FIG. 1, three samples are represented by A, B, and C. Reference numeral 15 indicates the sample arrangement direction for sample management. The sample arrangement order and measurement order may be the same.

[0027] The m samples may be m samples corresponding to m degrees of degradation, m samples manufactured under m manufacturing conditions, or m samples corresponding to m evaluation results. Differential analysis may also be applied to other m samples.

[0028] The measurement unit 10 has an ion source 16, a mass analyzer 18, and an ion detector 20. A sample introduced into the ion source 16 is ionized. Ions generated by the ionization are sent to the mass analyzer 18. Various ion sources can be used as the ion source 16, such as an ion source that follows an electron ionization method or an ion source that follows a chemical ionization method.

[0029] The mass analyzer 18 applies mass analysis to each ion according to its mass-to-charge ratio, and may be any of a variety of mass analyzers, such as a time-of-flight mass analyzer or a quadrupole mass analyzer.

[0030] Ions that have passed through the mass analyzer 18 are detected by the ion detector 20. A detection signal is output from the ion detector 20. The detection signal contains mass spectrum information. The detection signal is sent to the information processing unit 12 via a signal processing circuit (not shown).

[0031] Next, the information processing unit 12 will be described. The information processing unit 12 is configured by, for example, a computer. First, the configuration of the information processing unit 12 will be briefly described using FIG. 1, and then the configuration will be described in more detail using FIG. 2.

[0032] The mass spectrum creator 22 creates a mass spectrum (MS) based on the detection signal. Specifically, the mass spectrum creator 22 creates n mass spectra for each sample. As a result of mass analysis of m samples, m×n mass spectra are created. Each mass spectrum may be an accumulated mass spectrum.

[0033] The storage unit 24 is configured with a semiconductor memory or the like. Various types of information generated or processed in the differential analysis are stored in the storage unit 24. The m×n mass spectra 26 generated by the mass spectrum creator 22 are stored in the storage unit 24.

[0034] The peak detector 28 functions as a peak list generator. The peak detector 28 applies peak detection to each mass spectrum. This generates a peak list (PL) for each mass spectrum. Each peak list is composed of multiple ion intensities arranged on the m / z axis. The m×n peak lists 30 generated by the peak detector 28 are stored in the memory unit 24.

[0035] The table creator 32 has an alignment function, a normalization function, etc. The table creator 32 constructs a table 34 in the memory unit 24. The table 34 is made up of k ion intensity columns, where k is an integer equal to or greater than 2. Each ion intensity column is made up of m×n ion intensities. Each ion intensity column constitutes an ion intensity vector. The table 34 is made up of k ion intensity vectors.

[0036] One or more templates are registered in the storage unit 24. In the illustrated configuration example, three templates T1, T2, and T3 are registered, which constitute a template set 38. A template creator 36 creates each template. A template may be created based on information input by a user via an input unit 40. A template may also be created based on any of the ion intensity vectors included in the table 34.

[0037] The comparator 42 compares the ion intensity vectors in the table 34 with the selected template and calculates a plurality of similarities as a plurality of evaluation values. A similarity column 44 is generated from the k similarities corresponding to the k ion intensity vectors. The similarity column 44 is stored in the memory unit 24. If three templates are used, three similarity columns 44 are generated and stored.

[0038] In the illustrated configuration example, the processing unit 46 includes an extractor 48, a mass spectrum processor 50, a plot creator 52, etc. The extractor 48 identifies specific peak groups that satisfy certain conditions from multiple mass spectra or multiple peak lists based on the similarity string 44, and extracts the specific peak groups. The mass spectrum processor 50 creates a mass spectrum in which the specific peak groups are identified and expressed. The plot creator 52 creates a KMD (Kendrick Mass Defect) plot in which the specific peak groups are identified and expressed. Instead of a KMD plot, a RKM (Remainder of Kendrick Mass) plot may be created. KMD plots and RKM plots will be described in detail later.

[0039] The display 54 displays a mass spectrum, a KMD plot, etc. The display 54 is configured, for example, by an LCD. The information processing unit 12 has a processor (for example, a CPU) that executes a program. The mass spectrum creator 22, peak detector 28, table creator 32, comparator 42, template creator 36, and processing unit 46 described above correspond to multiple functions performed by the processor. The mass spectrum creator 22 may be provided within the measurement unit 10.

[0040] The operation of the sample analyzer according to the embodiment is shown in Figure 2. Figure 2 also includes a flowchart showing the sample analysis method according to the embodiment.

[0041] In the mass analysis step S10, n mass analyses are performed on each of m samples. This generates m×n mass spectra (MS). In the peak list creation step S12, peak detection is applied to each mass spectrum to create a peak list (PL). m×n peak lists are created from the m×n mass spectra. Each peak list consists of multiple ion intensities associated with multiple mass-to-charge ratios (multiple m / z). Each ion intensity generally corresponds to the area of ​​a peak.

[0042] In the table creation step S14, alignment and normalization are applied to the m×n peak lists. As a result, table 58 is created. Specifically, alignment is a process of creating table 58 by integrating the m×n peak lists while assuming errors included in the m / z axis of each peak list. In table 58, m×n ion intensities are associated with each m / z. These m×n ion intensities constitute an ion intensity series. In an embodiment, k ion intensity series corresponding to k m / z are constructed. k is an integer of 2 or greater, and is, for example, tens, hundreds, or thousands.

[0043] The normalization may include, for example, horizontal normalization and vertical normalization. In the horizontal normalization, for example, each ion intensity is normalized under the condition that the maximum ion intensity in each ion intensity column is 1. The ion intensity column may also be normalized under the condition that the sum of the ion intensities in each ion intensity column is 1. In the vertical normalization, for example, each ion intensity is normalized under the condition that the maximum ion intensity in each k ion intensities arranged vertically is 1. The k ions may also be normalized under the condition that the sum of the ion intensities in each k ion intensities arranged vertically is 1. All of the k ion intensity columns may also be normalized collectively.

[0044] As a result of performing alignment and normalization, a table 58 consisting of k ion intensity columns (k ion intensity vectors) is generated. The vertical direction 60 in the table 58 corresponds to the m / z axis. The horizontal axis 62 in the table 58 is the sample arrangement direction and also the ion intensity arrangement direction. Each ion intensity vector consists of m x n ion intensities after normalization. Reference numeral 64 indicates the arrangement of n ion intensities obtained by measuring each sample n times. This arrangement corresponds to the positioning order. Reference numeral 65 indicates a specific ion intensity vector among the k ion intensity vectors.

[0045] In the comparison step S18, k ion intensity vectors are compared with the template 66. This results in k similarities being calculated. Each similarity is, for example, the Euclidean distance between the vectors. The similarity is greatest when the Euclidean distance is 0. Other evaluation values ​​for evaluating the relationship between the two vectors may also be calculated. A similarity column 72 is formed from the k similarities corresponding to the k ion intensity vectors.

[0046] The template 66 is composed of m × n coefficients. Each coefficient has a value between 0 and 1, for example. As indicated by reference numeral 68, the value of each coefficient may be specified by the user. For example, a template may be specified for identifying a component contained in sample A but not contained in samples B and C, or a template may be specified for identifying a component contained in sample C but not contained in samples A and B. As indicated by reference numeral 70, any existing ion intensity vector may be used as the template. For example, if one wishes to identify compound β, which has the same ion intensity change as that of compound α, the ion intensity vector corresponding to compound α may be used as the template. A template may also be created by processing or modifying an existing ion intensity vector.

[0047] In the sorting step S20, the k ion intensity vectors are sorted in order of similarity based on the similarity column 72. The ion intensity vector corresponding to the greatest similarity is placed at the top. This forms the sorted table 58A. Reference numeral 72A indicates the sorted similarity column. The sorting step S20 may be omitted.

[0048] In the peak group extraction step S22, as indicated by the reference numeral 74, a plurality of ion intensity vectors having a similarity equal to or greater than a threshold are identified in the sorted table 58A, and a plurality of peaks corresponding to these vectors are identified as a specific peak group. For example, when a template for identifying compound X contained only in sample A is used, a specific peak group (corresponding to compound X) contained in n mass spectra or n peak lists corresponding to sample A is identified, and then the specific peak group (actually, a specific ion intensity group) is extracted. The specific peak group is to be identified and displayed. The extraction of the peak group can also be referred to as classifying the peak group.

[0049] Prior to identifying and extracting the peak group, an average mass spectrum may be created based on n mass spectra acquired from the sample of interest, or an average peak list may be created based on n peak lists corresponding to the sample of interest. A specific peak group may be extracted from a representative mass spectrum selected from the n mass spectra or a representative peak list selected from the n peak lists. After extracting the specific peak group, an average value may be calculated for each of the n peaks.

[0050] In the image formation step S24, an image representing the results of the difference analysis is formed, for example, a mass spectrum including a specific group of peaks. 76 In this case, for example, a specific group of peaks may be distinguishably displayed in a predetermined hue, and other groups of peaks may be displayed in gray. A mass spectrum including only the specific group of peaks may also be created.

[0051] In the image formation step S24, a KMD plot 78 or an RKM plot 80 including a specific group of peaks may be created. In this case, a specific group of display elements representing the specific group of peaks may be distinguishably displayed in a predetermined hue, and display elements representing other groups of peaks may be displayed in gray. A KMD plot 78 or an RKM plot 80 including only a specific group of display elements corresponding to the specific group of peaks may be created. Various images may be used to represent the identified group of peaks. Note that a specific compound may be identified as a result of difference analysis by composition analysis based on the identified group of peaks. The peak group extraction step S22 and the image formation step S24 correspond to processing steps.

[0052] The sample analysis method according to this embodiment will be further described with reference to a specific example.

[0053] Figure 3 shows the peak list 82 The creation of a peak list PL1 is shown. By applying peak detection to the mass spectrum MS1, a peak list PL1 is created. The peak list PL1 is a list of multiple m / z 84 Multiple ion intensities associated with 86 It consists of:

[0054] An example of a table is shown in Figure 4. The illustrated table 58 is composed of k ion intensity vectors 90 associated with k m / z values. Each ion intensity vector 90 is composed of m x n (specifically, 9) normalized ion intensities 94. In Figure 4, A, B, and C represent sample A, sample B, and sample C. A1, A2, and A3 represent the first measurement of sample A, the second measurement of sample A, and the third measurement of sample A. The same applies to B1, B2, and B3, and C1, C2, and C3.

[0055] An example of a template is shown in Figure 5. The illustrated template 66 is a template for identifying components that are contained in sample A but not contained (or contained in small amounts) in samples B and C when three mass analyses are performed on each of three samples A, B, and C.

[0056] Specifically, the template 66 is composed of m×n (specifically, 9) coefficients 100. The coefficient arrangement direction 98 corresponds to the ion intensity arrangement direction described above. From another perspective, the template 66 is composed of three coefficient sets corresponding to three samples, and each coefficient set consists of three coefficients. In each coefficient set, the three coefficients are assigned the same value. Specifically, the three coefficients corresponding to sample A are each assigned a value of 1, and the three coefficients corresponding to sample B and the three coefficients corresponding to sample C are each assigned a value of 0. Reference numeral 66A denotes a graph representing the template 66. The template shown in FIG. 5 is an example, and various templates can be used depending on the purpose of the difference analysis.

[0057] A specific example of sorting is shown in Fig. 6. As already explained, table 58 is composed of k ion intensity vectors corresponding to k m / z values. Similarity column 72 is composed of k similarities calculated for the k ion intensity vectors.

[0058] After the sorting, a table 58A and a similarity column 72A are generated. Reference numeral 74 denotes a plurality of ion intensity vectors for which similarities equal to or greater than a threshold value have been calculated. For example, when the template shown in FIG. 5 is used, a plurality of peaks belonging to the area indicated by reference numeral 102 are extracted as a specific peak group. Prior to or after this extraction, an average value of three ion intensities is calculated for each m / z.

[0059] 7 shows a mass spectrum including a specific group of peaks that are identified. In mass spectrum 106, the horizontal axis is the m / z axis, and the vertical axis is the intensity axis. Peaks 108 that belong to the specific group of peaks are displayed dark, and peaks 110 that do not belong to the specific group of peaks are displayed light. By observing mass spectrum 106, it is possible to identify, for example, components contained only in sample A.

[0060] Before explaining the KMD plot, we will explain KMD analysis. KMD analysis is a method for analyzing the mass spectrum of a polymer sample. The mass (molecular weight) M of a polymer with a certain degree of polymerization n can be expressed, for example, as follows: M = Mr × n + Me + Mc (1)

[0061] The mass M can also be referred to as the observed mass. Here, Me is the mass of the terminal group (if the molecule contains two terminal groups, this is the sum of their masses), and Mc is the mass of the additional moiety when a cationizing agent is added prior to ionization. Assuming z=1, i.e., a single charge, the mass of each molecule can be determined from the m / z of each peak in the mass spectrum.

[0062] Kendrick Mass (KM) is defined as follows: KM = M × Mri / Mr (2)

[0063] Here, M is the integer mass of the monomer (repeating unit), and Mr is the exact mass of the monomer. In the above formula (2), the coefficient (M / M) multiplied by the polymer mass M has the effect of converting the exact mass of the monomer (e.g., 58.42) to an integer mass (e.g., 58).

[0064] Substituting the right-hand side of equation (1) for M in equation (2) above gives the following: KM=(Mr×n+Me+Mc) ×Mri / Mr ···(3-1) =Mri×n+(Me+Mc)×Mri / Mr (3-2) =Mri×n+(B+b) (3-3)

[0065] where Mri×n is an integer value. The capital letter B indicates the integer part of (Me+Mc)×Mri / Mr, and the lowercase letter b indicates the decimal part of (Me+Mc)×Mri / Mr.

[0066] Nominal Kendrick Mass (NKM) is an integer value obtained by rounding off the decimal part of KM (i.e., b). Specifically, it is as follows: NKM=Mri×n+B+1 (b≧0.5) ···(4-1) =Mri×n+B (b<0.5) (4-2)

[0067] The Kendrick Mass Defect (KMD) is defined as follows: The KMD corresponds to the shortfall or defect from an integer value. KMD = NKM - KM (5-1) =1-b (b≧0.5) (5-2) =-b (b<0.5) (5-3)

[0068] In KMD analysis, multiple elements (e.g., circles) corresponding to multiple peaks in the mass spectrum of a polymer sample are arranged on a two-dimensional coordinate system defined by the horizontal axis representing NKM and the vertical axis representing KMD. This creates a KMD plot. For example, the size of each element represents the intensity of each peak. In a KMD plot, multiple elements corresponding to the multiple peaks that make up a polymer series are arranged at equal intervals parallel to the horizontal axis. The intervals correspond to the repeating unit. In a KMD plot, the degree of polymerization does not affect the position along the vertical axis. However, some literature provides a simplified explanation that the integer part of KM is the NKM. Generally, KMD ranges from -0.5 to +0.5.

[0069] Instead of a vertical axis representing KMD, a vertical axis representing Remainder of Kendrick Math (RKM) is sometimes used, producing an RKM plot. RKM is defined as follows: RKM=KM / Mri-Floor(KM / Mri) ···(6)

[0070] Here, Floor(X) is an operator that rounds down the decimal points of X. Substituting the above formula (3-2) for KM in the above formula (6) gives the following: RKM={n+(Me+Mc) / Mr}-Floor{n+(Me+Mc) / Mr} ···(7)

[0071] The above n can be eliminated in the above equation (7). Ultimately, RKM is expressed as follows: RKM=(Me+Mc) / Mr-Floor{(Me+Mc) / Mr} ···(8)

[0072] RKM is a decimal fraction obtained by dividing the sum of the mass of the end group and the mass of the cationizing agent by the mass of the repeating unit. RKM is also a value independent of the degree of polymerization (n). Generally, RKM ranges from 0 to 1.0.

[0073] The horizontal axis of the RKM plot represents KM, and the vertical axis represents RKM. In the RKM plot, as in the KMD plot, multiple elements corresponding to polymer series are arranged at equal intervals parallel to the horizontal axis.

[0074] 8 shows a specific example of a KMD plot created in an embodiment. The horizontal axis is the NKM axis, and the vertical axis is the KMD axis. In the illustrated KMD plot 110, the specific peak group is represented by multiple black circles 112, and multiple peaks other than the specific peak group are represented by multiple white circles 114. Using the KMD plot 110, for example, polymers contained only in sample A can be easily identified.

[0075] The chart shown in FIG. 9 may be displayed. The chart includes a graph 116 showing a template and a graph 118 showing ion intensity vectors. The horizontal axis shows coefficient numbers and ion intensity numbers. The left vertical axis shows ion intensity, and the right vertical axis shows coefficient values. Note that the ion intensity may be shown as a relative intensity, with the maximum value being 100%. Nine elements 116a constituting the graph 116 show nine coefficients, and nine elements 118a constituting the graph 118 show nine ion intensities. Observing this chart makes it possible to visually recognize the degree of similarity. The chart shown in FIG. 9 is displayed for a peak selected from a specific peak group. An average vector may be calculated from multiple ion intensity vectors constituting the peak group and displayed on the chart.

[0076] Another example of a template is shown in Figure 10. The upper part of Figure 10 shows a degradation curve 120. The horizontal axis represents time, and the vertical axis represents the degree of degradation. For example, three samples are sampled at three timings t1, t2, and t3. Difference analysis is applied to these samples.

[0077] For example, if one wishes to identify a component that gradually decreases over time, one may use template 122 shown in the bottom of Figure 10. Template 122 is composed of three coefficient sets corresponding to three samples. Three coefficients constituting the first coefficient set corresponding to the first sample are assigned a value of 1, three coefficients constituting the second coefficient set corresponding to the second sample are assigned a value of 0.5, and three coefficients constituting the third coefficient set corresponding to the third sample are assigned a value of 0. Graph 122A is a graph showing the template.

[0078] 11 shows an example of a template set. The illustrated template set 124 is composed of a first template 124-1, a second template 124-2, and a third template 124-3. Measurement targets are sample A, sample B, and sample C. Reference numeral 126A indicates a section corresponding to sample A, reference numeral 126B indicates a section corresponding to sample B, and reference numeral 126C indicates a section corresponding to sample C. The first template 124-1 is intended to identify components contained exclusively in sample A. The second template 124-2 is intended to identify components contained exclusively in sample B. The third template 124-3 is intended to identify components contained exclusively in sample C. The three templates 124-1, 124-2, and 124-3 are applied in parallel.

[0079] When the first template is used, for example, a mass spectrum 126 shown in Fig. 12 and a KMD plot 128 shown in Fig. 13 are created and displayed. The mass spectrum 126 and the KMD plot 128 show only the first group of peaks extracted using the first template.

[0080] When the second template is used, for example, a mass spectrum 130 shown in Fig. 14 and a KMD plot 132 shown in Fig. 15 are created and displayed. The mass spectrum 130 and the KMD plot 132 show only the second group of peaks extracted by using the second template.

[0081] When the third template is used, for example, a mass spectrum 134 shown in Fig. 16 and a KMD plot 136 shown in Fig. 17 are created and displayed. The mass spectrum 134 and the KMD plot 136 show only the third group of peaks extracted by using the third template.

[0082] FIG. 18 shows a sample analysis method according to the second embodiment. Three mass analyses are performed on each of three samples. That is, m=3, n=3. Table 137 is composed of k ion intensity vectors corresponding to k m / z values. In the second embodiment, each ion intensity vector is composed of three ion intensities. Each ion intensity 140 is the average value of the three ion intensities. That is, the average value of the three ion intensities is calculated for each m / z value and for each sample.

[0083] The template 138 is composed of three coefficients 142 corresponding to three samples. The template 138 is compared with k ion intensity vectors. This determines k similarities. Based on the k similarities, a specific group of peaks is identified.

[0084] Figure 19 shows a sample analysis method according to the third embodiment. Three mass analyses are performed on three samples, respectively. That is, m=3, n=3. Table 144 has the same structure as the table shown in Figure 6. The comparison process is made up of multiple sub-processes. Multiple templates are applied in stages as described below.

[0085] Specifically, in the first sub-step (A), a first template 146 is used to generate a first similarity column 152. A first group of peaks is identified based on the first similarity column 152. The first template 146 is created based on a user instruction (see reference numeral 150) or based on a specific ion intensity vector in the table 144 (see reference numeral 152).

[0086] In the second sub-step (B), a table 144A is created by excluding a plurality of ion intensity vectors corresponding to the first peak group from the table 144, and the created table 144A is used. In this case, for example, a specific remaining ion intensity vector included in the table 144A is used as a second template 154 (see reference numeral 158). In the second sub-step (B), the second template may be created based on a user's instruction.

[0087] The plurality of ion intensity vectors in table 144A are compared with second template 154 to create second similarity column 152A. A second peak group is identified based on second similarity column 152A. If necessary, a third sub-process or the like is further executed. According to the third embodiment, a plurality of peak groups can be extracted stepwise with high accuracy. [Explanation of symbols]

[0088] 10 measurement unit, 12 information processing unit, 14 sample set, 22 mass spectrum creator, 24 memory unit, 32 table creator, 36 template creator, 42 comparator, 46 processing unit, 48 extractor, 50 mass spectrum processor, 52 plot creator.

Claims

1. a mass spectrometry unit that performs mass spectrometry n times (where n is an integer of 1 or more) on each of m samples (where m is an integer of 2 or more) that are the subject of differential analysis, thereby obtaining m×n mass spectra; a list creation unit that creates m×n peak lists based on the m×n mass spectra; a table creating unit that creates a table of a plurality of ion intensity vectors corresponding to a plurality of mass-to-charge ratios based on the m×n peak list, the table creating unit defining the ion intensity vector for each mass-to-charge ratio based on the m×n ion intensities; a comparison unit that compares the plurality of ion intensity vectors with the template for differential analysis, thereby calculating a plurality of evaluation values; a processing unit that applies a process for the difference analysis to the m×n mass spectra, the m×n peak lists, or the plurality of ion intensity vectors based on the plurality of evaluation values; Including, each of the ion intensity vectors is configured as a sequence of the m×n ion intensities; The template is configured as a sequence of m×n coefficients corresponding to the sequence of m×n ion intensities. A sample analysis device characterized by:

2. 2. The sample analyzer according to claim 1, The n is an integer of 3 or more. A sample analysis device characterized by:

3. 2. The sample analyzer according to claim 1, the m×n coefficients are composed of m coefficient sets corresponding to the m samples; Each coefficient set is composed of n coefficients having the same value. A sample analysis device characterized by:

4. 4. The sample analyzer according to claim 3, The m samples include a first sample and a second sample, the evaluation value is a similarity, When identifying a compound contained in a larger amount in the first sample than in the second sample, in the template, the value of each coefficient corresponding to the first sample is larger than the value of each coefficient corresponding to the second sample. A sample analysis device characterized by:

5. 2. The sample analyzer according to claim 1, the comparison unit applies a plurality of templates for the difference analysis to the plurality of ion intensity vectors in parallel; A sample analysis device characterized by:

6. 2. The sample analyzer according to claim 1, the comparison unit applies a plurality of templates for the difference analysis to the plurality of ion intensity vectors in a stepwise manner. A sample analysis device characterized by:

7. 2. The sample analyzer according to claim 1, The m samples include a first sample and a second sample, the process for the difference analysis includes a process of extracting a specific group of peaks included in the m×n mass spectra or the m×n peak list; the specific peak group is a peak group corresponding to a compound contained in the first sample in a greater amount than the second sample; A sample analysis device characterized by:

8. 8. The sample analyzer according to claim 7, The process for the difference analysis further includes a process for generating an image representing the specific peak group so that the specific peak group can be distinguished from other peak groups. A sample analysis device characterized by:

9. 2. The sample analyzer according to claim 1, a creation unit that creates the template based on a user's instruction; A sample analysis device characterized by:

10. 2. The sample analyzer according to claim 1, a creating unit that creates the template based on a specific ion intensity vector in the table; A sample analysis device characterized by:

11. 2. The sample analyzer according to claim 1, The m samples are m samples having different degrees of degradation, m samples manufactured according to different manufacturing conditions, or m samples subjected to different evaluations. A sample analysis device characterized by:

12. performing mass spectrometry n times (where n is an integer of 1 or more) on each of m samples (where m is an integer of 2 or more) that are the subject of differential analysis, thereby obtaining m×n mass spectra; creating m×n peak lists based on the m×n mass spectra; creating a table of a plurality of ion intensity vectors corresponding to a plurality of mass-to-charge ratios based on the m×n peak list, wherein the ion intensity vector is defined for each mass-to-charge ratio based on the m×n ion intensities; comparing the plurality of ion intensity vectors with the template for difference analysis, thereby calculating a plurality of evaluation values; applying a process for the difference analysis to the m×n mass spectra, the m×n peak lists, or the plurality of ion intensity vectors based on the plurality of evaluation values; Including, each of the ion intensity vectors is configured as a sequence of the m×n ion intensities; The template is configured as a sequence of m×n coefficients corresponding to the sequence of m×n ion intensities. A sample analysis method characterized by:

13. A program executed on an information processing device for processing m×n mass spectra generated by performing mass analysis n times (where n is an integer of 1 or more) on m samples (where m is an integer of 2 or more) that are the subject of differential analysis, a function of creating an m×n peak list based on the m×n mass spectra; a function of creating a table of a plurality of ion intensity vectors corresponding to a plurality of mass-to-charge ratios based on the m×n peak list, wherein the ion intensity vector is defined for each mass-to-charge ratio based on the m×n ion intensities; a function of comparing the plurality of ion intensity vectors with the template for difference analysis, thereby calculating a plurality of evaluation values; a function of applying a process for the difference analysis to the m×n mass spectra, the m×n peak lists, or the plurality of ion intensity vectors based on the plurality of evaluation values; Including, each of the ion intensity vectors is configured as a sequence of the m×n ion intensities; The template is configured as a sequence of m×n coefficients corresponding to the sequence of m×n ion intensities. A program characterized by:

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