Data processing device, data processing method, data processing program and analysis device

Abstract

The present invention relates to a data processing device (100) for performing data processing on a measurement waveform obtained by performing a predetermined measurement on a sample. The data processing device (100) includes an estimation unit (102), a calculation unit (104), and a display processing unit (105). The estimation unit (102) estimates the predicted distribution of each peak shape for a plurality of adjacent peak waveforms contained in the measurement waveform using a predetermined peak shape model. The calculation unit (104) calculates the predicted distribution of a quantitative index for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit (102). The display processing unit (105) can operate in a manner that displays the predicted distribution of the quantitative index calculated by the calculation unit (104).

Description

Technical Field

[0001] The present invention relates to a data processing device, a data processing method, a data processing program and an analyzing device. Background Art

[0002] In an analysis device that combines a component separation device such as a liquid chromatograph or a gas chromatograph and a detector, multiple peaks such as impurities or near-peer substances often overlap. Therefore, it is necessary to separate the peaks before quantitative analysis. In the analysis using a chromatograph, for each peak shape, for example, a Gaussian function, a BEMG function shown in non-patent document 1, and the like are assumed, and a peak shape model obtained by mixing multiple of them is used. For example, when a mixed Gaussian model is used, the number of assumed peaks (number of clusters) is K, and it is assumed that the signal waveform can be represented by the function of the following formula (1).

[0003] [Number 1]

[0004]

[0005] Then, the parameter μ k , σ k It is estimated by the maximum likelihood estimation method, etc. Here, the shape of each peak is represented by the following formula (2).

[0006] [Number 2]

[0007]

[0008] However, when peaks overlap, there is a large uncertainty in the estimation of the shape of each peak or the area of ​​the peak. If the above-mentioned maximum likelihood estimation is used to predict the area or height of each peak (or the concentration of components proportional to them), the prediction error may become large.

[0009] Prior art literature

[0010] Non-patent literature

[0011] Non-patent literature 1: Arase, Shuntaro et al., "Intelligent peak deconvolution through in-depth study of the data matrix from liquid chromatography coupled with a photo-diode array detector applied to pharmaceutical analysis." Journal of Chromatography A, 1469 (2016): 35-47. Summary of the invention

[0012] Technical problem to be solved by the invention

[0013] As described above, when separating peaks, multiple Gaussian functions or BEMG functions (Non-Patent Document 1) are prepared and applied to the peak waveform, thereby estimating the peak shapes one by one from the overlapping peak groups. However, when estimating the shapes of each peak, there is a region where the peak of a substance (also called "main peak") and the peak of an impurity (or an adjacent substance) are essentially indistinguishable. Figure 1 This is a graph showing the relationship between the tailing of the main peak and the impurity peak. Figure 1 As shown, the peak waveform formed by the overlap of the impurity peak and the main peak without tailing becomes a waveform that is roughly the same as the peak waveform of the main peak with tailing, and they cannot be distinguished. Therefore, when quantitatively analyzing the area of ​​the peak waveform, an area error will occur. Furthermore, when fitting the peak waveform using a peak shape model, errors will occur in the quantitative analysis of the area of ​​the peak waveform due to uncertain factors such as noise contained in the measured peak waveform. According to such a situation, the user needs to evaluate the degree of error that the quantitative index obtained from the peak waveform can produce to ensure a reasonable safety rate.

[0014] The present disclosure is completed to solve the above-mentioned problems, and an object of the present disclosure is to provide a technology capable of ensuring a reasonable safety rate by taking into account errors in quantitative indicators obtained from peak waveforms.

[0015] Solutions for solving the above technical problems

[0016] According to a data processing device of one scheme of the present disclosure, data processing is performed on a measurement waveform obtained by performing a predetermined measurement on a sample. The data processing device comprises an estimation unit, a calculation unit and a display processing unit. The estimation unit estimates the predicted distribution of each peak shape using a predetermined peak shape model for each of a plurality of adjacent peak waveforms contained in the measurement waveform. The calculation unit calculates the predicted distribution of a quantitative index for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit. The display processing unit can operate in a manner that displays the predicted distribution of the quantitative index calculated by the calculation unit.

[0017] According to a data processing method of another scheme of the present disclosure, data processing is performed on a measurement waveform obtained by performing a predetermined measurement on a sample. The data processing method comprises: a step of using a predetermined peak shape model to estimate the predicted distribution of each peak shape for a plurality of adjacent peak waveforms contained in the measurement waveform; a step of calculating the predicted distribution of a quantitative index for each peak waveform based on the estimated predicted distribution of the peak shape; and a step of displaying the calculated predicted distribution of the quantitative index.

[0018] According to the data processing program of other schemes of the present disclosure, data processing is performed on the measurement waveform obtained by performing a predetermined measurement on the sample. The data processing program causes the computer to execute the following steps: a step of using a predetermined peak shape model to estimate the predicted distribution of each peak shape for a plurality of adjacent peak waveforms contained in the measurement waveform; a step of calculating the predicted distribution of a quantitative index for each peak waveform based on the estimated predicted distribution of the peak shape; and a step of displaying the calculated predicted distribution of the quantitative index.

[0019] Effects of the Invention

[0020] According to the present disclosure, the data processing device can operate in a manner that displays the predicted distribution of quantitative indicators relative to each peak shape. In this way, since the user can confirm the predicted distribution of the quantitative indicator, it is easy to intuitively grasp the statistical data about the quantitative indicator, and by confirming these data, it is possible to consider the error of the quantitative indicator to ensure a reasonable safety rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a graph showing the relationship between the tailing of the main peak and the impurity peaks.

[0022] Figure 2 This is a block diagram showing an example of the functional configuration of a data processing device.

[0023] Figure 3 This is a flowchart showing an example of data processing.

[0024] Figure 4 This is a flowchart showing an example of the estimation process.

[0025] Figure 5 This is a diagram showing a display example of predicted distribution of peak shapes when it is assumed that the number of peaks is two.

[0026] Figure 6 This is a diagram showing a display example of predicted distribution of peak shapes when it is assumed that the number of peaks is 1.

[0027] Figure 7 This is a diagram showing a display example of predicted distribution of each peak shape when it is assumed that the number of peaks is two.

[0028] Figure 8 It is a diagram showing a display example of predicted distribution of peak areas.

[0029] Fig. 9 This is a diagram showing an example of displaying the predicted distribution and quantiles of the area ratio.

[0030] Fig.10 It is a diagram showing a display example of the predicted distribution and quantiles of each peak shape.

[0031] Fig.11 This is a block diagram showing an example of the functional configuration of an analysis device according to a modified example of the present embodiment. DETAILED DESCRIPTION

[0032] Hereinafter, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In addition, the same reference numerals are given to the same or corresponding parts in the drawings, and their repeated description will not be repeated.

[0033] [Functional structure of data processing device]

[0034] Figure 2 FIG. 1 is a block diagram showing an example of the functional configuration of a data processing device. Figure 2 As shown in FIG. 1 , the data processing device 100 of the present embodiment is configured to be connectable to the sample measurement device 10 .

[0035] The sample measuring device 10 in this embodiment is, for example, a chromatograph analysis device (LC, GC) that combines a component separation device such as a liquid chromatograph or a gas chromatograph with a detector. Alternatively, it may be a chromatograph mass spectrometer (LC / MS, GC / MS) that uses a mass spectrometer (MS) as a detector.

[0036] The data processing device 100 performs data processing on the measurement waveform obtained by performing a predetermined measurement on the sample. Each peak shape included in the measurement waveform corresponds to a plurality of substances included in the sample. In the present embodiment, the measurement waveform is assumed to be a waveform of a chromatogram obtained by measurement by a liquid chromatograph analysis device (sample measurement device 10).

[0037] For example, when a sample contains a substance (also called a "main component") and impurities, a peak corresponding to the main component (also called a "main peak") and a peak corresponding to the impurities (also called a "impurity peak") appear in the measurement waveform. When the main peak and the impurity peak are close to each other, it is difficult to distinguish them (see Figure 1 ).

[0038] Here, the data processing device 100 includes a hard disk, a CPU (central processing unit), and a memory. The CPU reads a program stored in the hard disk into the memory and executes it to realize various functions of the data processing device 100. The data processing device 100 is, for example, a personal computer or a workstation.

[0039] Furthermore, the data processing device 100 is connected to peripheral devices including a keyboard 111 and a display 112. In addition, the data processing device 100 may include an input device such as the display 112 or a display device such as the display 112.

[0040] The data processing device 100 includes an acquisition unit 101, an estimation unit 102, an input unit 103, a calculation unit 104, and a display processing unit 105. The functions of these units are realized by the CPU of the data processing device 100 executing various programs.

[0041] The sample measurement device 10 performs a predetermined measurement on the sample. The acquisition unit 101 acquires a measurement waveform. Specifically, the acquisition unit 101 acquires a measurement waveform obtained by the predetermined measurement performed by the sample measurement device 10.

[0042] The estimation unit 102 estimates the predicted distribution of the peak shape of the peak waveform included in the measurement waveform using a predetermined peak shape model. In the present embodiment, the predetermined peak shape model is a "K-mixed BEMG function" described later. The measurement waveform is the measurement waveform acquired by the acquisition unit 101. In addition, the sample measurement device 10 may not include the acquisition unit 101. In this case, the estimation unit 102 directly acquires the measurement waveform obtained by the predetermined measurement performed by the sample measurement device 10 and performs the estimation.

[0043] In the present embodiment, the estimating unit 102 estimates the predicted distribution of each peak shape by Bayesian estimation, but the present invention is not limited thereto, and the estimating unit 102 may perform estimation using an estimation method other than Bayesian estimation.

[0044] In this embodiment, the peak waveform included in the measured waveform includes a plurality of adjacent peak waveforms. That is, the estimation unit 102 estimates the predicted distribution of each peak shape for each of the plurality of adjacent peak waveforms included in the measured waveform acquired by the acquisition unit 101 using a predetermined peak shape model (K-mixed BEMG function). Figure 5 to Figure 7 To be described later.

[0045] The calculation unit 104 calculates the predicted distribution of the quantitative index for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit 102. In addition, the calculation unit 104 calculates the quantile of the threshold value in the calculated predicted distribution of the quantitative index.

[0046] For example, the quantitative indicator is "the area of ​​the peak shape (also referred to as "peak area")". That is, the predicted distribution of the quantitative indicator in this case is the predicted distribution of the peak area. In addition, the quantitative indicator can also be "the ratio of the peak areas between peak shapes" (also referred to as "area ratio" for short). The predicted distribution of the quantitative indicator in this case is the predicted distribution of the area ratio, which means "the distribution of the ratio of substances corresponding to each peak shape". In addition, for example, the threshold is "0.95" and the quantile obtained is "20.4%". The details will be used Figure 8 to Figure 10 To be described later.

[0047] The display processing unit 105 can operate in a manner to display the predicted distribution of the quantitative index calculated by the calculation unit 104. In addition, the display processing unit 105 can operate in a manner to display the quantile calculated by the calculation unit 104. Specifically, the display processing unit 105 causes the display 112 to display at least one of the predicted distribution and quantile of the quantitative index calculated by the calculation unit 104. As a result, the predicted distribution or quantile of the quantitative index is displayed on the display 112. The details will be described in detail using Figure 8 to Figure 10 To be described later.

[0048] Here, the user can perform various settings or switch the display by operating the keyboard 111. For example, the user can set a threshold value by operating the keyboard 111.

[0049] The input unit 103 receives input from the user. Specifically, the input unit 103 receives input of a threshold value (e.g., 0.95) based on the user's operation. The calculation unit 104 calculates the quantile based on the threshold value inputted by the input unit 103, and displays it on the display 112. For example, the display 112 may display the quantile together with the threshold value, or may display the predicted distribution of the area ratio together with them (see the following description). Fig. 9 ), and the predicted distribution of peak shapes can also be displayed together with them (see below Fig.10 ).

[0050] In addition, the user can switch the display of the display 112 by operating the keyboard 111. For example, the user can switch the display of the predicted distribution of the above-mentioned area ratio (see the following description) by operating the keyboard 111. Fig. 9 ) is switched to the display of the predicted distribution of peak areas (see Figure 8 ), or switch to the display of the predicted distribution of the peak shape (see below Fig.10 ).

[0051] The data processing device 100 may be configured to be connectable to a network such as a LAN (Local Area Network) via a communication interface. In this case, the data processing device 100 may be connected to the sample measurement device 10 via the network. Furthermore, the data processing device 100 may be connected to a plurality of sample measurement devices 10 via the network.

[0052] As described above, the processing performed by the acquisition unit 101, the estimation unit 102, the input unit 103, the calculation unit 104, and the display processing unit 105 can be performed by a personal computer or by a server device connected to the personal computer via a network. In the former case, the program for performing the above-mentioned processing is installed on the personal computer, and in the latter case, it is installed on the server device. The program for performing the above-mentioned processing can be downloaded from the server device via the network, or can be stored in a recording medium (such as a CD or DVD) and distributed.

[0053] [Flowchart of data processing]

[0054] Figure 3 is a flowchart showing an example of data processing. Figure 3 As shown, the data processing device 100 performs data processing. The data processing is a process performed on a measurement waveform obtained by performing a predetermined measurement on a sample, and is a series of processes performed by the acquisition unit 101, the estimation unit 102, the input unit 103, the calculation unit 104, and the display processing unit 105 of the data processing device 100.

[0055] Data processing includes acquisition processing, estimation processing, input processing, calculation processing, and display processing. The acquisition processing is a process performed by the acquisition unit 101, the estimation processing is a process performed by the estimation unit 102, the input processing is a process performed by the input unit 103, the calculation processing is a process performed by the calculation unit 104, and the display processing is a process performed by the display processing unit 105. Hereinafter, steps are simply described as S.

[0056] The CPU reads the program stored in the hard disk into the memory and executes it. Data processing is performed by executing the data processing program. Acquisition processing is performed by executing the acquisition processing program. Inference processing is performed by executing the inference processing program. Input processing is performed by executing the input processing program. Calculation processing is performed by executing the calculation processing program. Display processing is performed by executing the display processing program.

[0057] For example, the acquisition unit 101 performs an acquisition process for transferring data (measurement waveform) between the sample measurement device 10 and the data processing device 100. The acquisition process is performed by executing an acquisition process program.

[0058] When data processing is started, the data processing device 100 executes an acquisition process in S11 and advances the process to S12 . In the acquisition process, the acquisition unit 101 acquires a measurement waveform obtained by a predetermined measurement performed by the sample measurement device 10 .

[0059] The data processing device 100 performs the estimation process in S12 and advances the process to S13. Figure 4 Processing shown.

[0060] Figure 4 FIG. 4 is a flowchart showing an example of estimation processing. Figure 4 As shown, when the estimation process starts, the data processing device 100 estimates the predicted distribution of the peak shape by Bayesian estimation for each of the peak waveforms included in the measured waveform when the number of peaks is assumed to be 1 to N in S21, and advances the process to S22.

[0061] The data processing device 100 sets the number of peaks specified by the user in S22 and advances the process to S23. For example, the predicted distribution of each peak shape with the number of peaks = 1 to N calculated in S21 may also be displayed on the display 112 (see the following description). Figure 5 , Figure 6 ). Then, the user specifies which of the peak numbers 1 to N is appropriate. In this case, for example, the number of peaks specified by the user is set based on the operation of the keyboard 111.

[0062] In S23, the data processing device 100 selects the predicted distribution of the peak shape of the number of peaks set in S22 as the estimation result and ends the estimation process. For example, when the number of peaks is set to 2, the data processing device 100 selects the predicted distribution of the peak shape of the number of peaks = 2 as the estimation result and uses the estimation result in the processes of S13 to S16.

[0063] return Figure 3 , the data processing device 100 performs input processing in S13 and advances the processing to S14. In the input processing, the input unit 103 receives the user's input. Specifically, the input unit 103 receives the input of thresholds and display items based on the user's operation. "Display items" are items that the user wants to display on the display 112, and can be quantitative indicators such as peak area or area ratio, or quantiles, etc. For example, when the quantitative indicator "area ratio" is input as a display item, the predicted distribution of the area ratio is displayed on the display 112. In addition, for example, the threshold is "0.95".

[0064] The data processing device 100 performs calculation processing in S14 and S15. In S14, the calculation unit 104 calculates the predicted distribution of the quantitative index for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit 102, and advances the processing to S15. For example, the "predicted distribution of the area ratio" is calculated as the predicted distribution of the quantitative index.

[0065] In S15, the calculation unit 104 obtains the quantile of the threshold value in the predicted distribution of the quantitative index and advances the process to S17. For example, "20.4%" is obtained as the quantile of the threshold value (0.95) in the predicted distribution of the area ratio. In addition, if the display of the quantile is not specified in S14, the quantile does not need to be calculated.

[0066] The data processing device 100 performs display processing in S16 and terminates the data processing. In the display processing, the display processing unit 105 displays at least one of the predicted distribution and the quantile (eg, 20.4%) of the quantitative index (eg, area ratio) calculated by the calculation unit 104 on the display 112 .

[0067] As a result, the predicted distribution or quantile of the quantitative index is displayed on the display 112. For example, as described below Figure 8 As shown, the predicted distribution of peak areas is displayed on the display 112; or as described later Fig. 9 As shown, "P(area [%] < 20.4) = 0.95" is displayed on the display 112 together with the predicted distribution of the area ratio, which means that the area ratio (= peak area of ​​peak 2 / peak area of ​​peak 1) is less than 20.4% with a probability of 0.95 (95%); or as described later Fig.10 As shown, the predicted distribution of peak shapes is displayed on the display 112.

[0068] Here, the user can change the threshold value or input a change in display item or quantitative index by operating the keyboard 111. When such a change is made, the data processing device 100 can perform data processing again from S13. For example, when the user changes the threshold value from "0.95" to "0.97", the threshold value = "0.97" is input in S13. Then, the data processing device 100 performs the calculation processing of S14 and S15 and the display processing of S16 based on the changed threshold value.

[0069] When the user changes the display item from “peak area” to “peak height”, display item = “peak height” is input in S13 . Then, the data processing device 100 performs calculation processing in S14 and S15 and display processing in S16 based on the changed display item.

[0070] The display processing unit 105 is not limited to performing processing to display the probability above the threshold value input by the input unit 103 as a quantile, nor is it limited to performing processing to display the probability below the threshold value input by the input unit 103 as a quantile, and may perform processing to display the probability exceeding the threshold value input by the input unit 103 as a quantile, or may perform processing to display the probability below the threshold value input by the input unit 103 as a quantile. In addition, the quantile is a numerical value calculated based on the input threshold value, for example, a statistic indicating a certain degree of risk or safety.

[0071] Furthermore, the calculation unit 104 may calculate the first and second predicted distributions based on the mutually correlated quantitative indices for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit 102. In this case, the display processing unit 105 may operate in a manner to change the display method of the first and second predicted distributions based on the selection of the user input received by the input unit 103.

[0072] The quantitative indicators of the "first and second predicted distributions based on quantitative indicators that are correlated with each other" may be the same, or may be arbitrary when they are correlated with each other. For example, the calculation unit 104 may calculate the predicted distribution of the peak area of ​​Peak 1 as the first predicted distribution, and calculate the predicted distribution of the peak area of ​​Peak 2 as the second predicted distribution. The calculation unit 104 may calculate the predicted distribution of the peak shape of Peak 1 as the first predicted distribution, and calculate the predicted distribution of the peak area of ​​Peak 1 as the second predicted distribution. The calculation unit 104 may calculate the predicted distribution of the peak shape of Peak 1 as the first predicted distribution, and calculate the predicted distribution of the peak area of ​​Peak 2 as the second predicted distribution. However, for example, when comparing two measurement waveforms obtained by a prescribed measurement performed by the sample measurement device 10, it cannot be said that they are correlated with each other.

[0073] In addition, as the display method of the first and second predicted distributions, only the first predicted distribution may be displayed, only the second predicted distribution may be displayed, or both may be displayed. When both are displayed, one may be displayed larger and the other smaller.

[0074] Specifically, for example, in S13, a display item may be input to display the predicted distribution of the peak shape of Peak 1, and only the predicted distribution of the peak shape of Peak 1 is displayed on the display 112. In addition, in S13, a display item may be input to display the predicted distribution of the peak shape of Peak 1 larger and the predicted distribution of the peak shape of Peak 2 smaller, and the predicted distribution of the peak shape of Peak 1 is displayed larger and the predicted distribution of the peak shape of Peak 2 is displayed smaller on the display 112.

[0075] [Predicted distribution of peak shape]

[0076] As described above, the acquisition unit 101 acquires a measurement waveform (also referred to as a "signal waveform") obtained by a predetermined measurement performed by the sample measurement device 10. The estimation unit 102 estimates the predicted distribution of each peak shape by Bayesian estimation using a predetermined peak shape model for each of a plurality of adjacent peak waveforms included in the measurement waveform acquired by the acquisition unit 101. The estimation or display of the predicted distribution of the peak shape will be specifically described below.

[0077] In this embodiment, it is assumed that a plurality of peaks overlap in the chromatogram and the number of overlapping peaks is unknown. In such a case, usually, in order to separate the peaks from the signal waveform, the model related to the shape of each peak is added to create a model of the signal waveform, and the signal waveform is fitted by adjusting the parameters of the model.

[0078] In this embodiment, an LC chromatogram is used as a measurement waveform (signal waveform). In addition, a BEMG function is used as a predetermined peak shape model. That is, one peak shape can be expressed by the following formula (3).

[0079] [Number 3]

[0080]

[0081] Since the signal waveform is considered to overlap with the peak, the signal waveform model can be expressed by adding multiple BEMG functions. In the present embodiment, the function formed by adding K BEMG functions (the number of peaks = K) is called a "K-mixed BEMG function". The K-mixed BEMG function is represented by the following equation (4). However, considering the noise mixed in the signal, an error term ε is added as shown in equation (4). The error term ε is normally distributed with zero mean, and its variance is estimated from the signal waveform as a parameter.

[0082] [Number 4]

[0083]

[0084] In addition, in the present embodiment, the BEMG function is applied as a model of the peak shape, but it is not limited to this. For example, a Gaussian function can be applied as a model of the peak shape, and a Cauchy function can also be applied, as long as it can be described by some model function. In addition, the error term can be the following formula (5) in which an error is added to x, or the following formula (6) in which the input x affects the error. In addition, the error term is a normal distribution, but it is not limited to this. For example, it can be based on other probability distributions, or it can be based on rules other than probability distributions.

[0085] [Number 5]

[0086]

[0087] [Number 6]

[0088]

[0089] In addition, when estimating the distribution by Bayesian, for example, sampling by No U-Turn Sampler (NUTS) can be used. In addition, without limitation thereto, other sampling methods can also be used. In addition, for example, a Bayesian estimation method other than sampling such as variational Bayes can also be used.

[0090] If used Figure 1 As described above, when the peak of a substance (also referred to as "main peak", "peak 1") and the peak of an impurity (also referred to as "shoulder peak", "peak 2") are close to each other, it is difficult to distinguish these peaks.

[0091] In such Figure 1 In the case shown, the number of peaks may be 1 (only the main peak exists), or 2 (the main peak and the shoulder peak exist), or 3 or more.

[0092] Therefore, in the present embodiment, the predicted distribution of the peak shape is estimated by assuming that the number of peaks is 1 or 2 or more. Specifically, in the present embodiment, it is configured to be able to estimate the predicted distribution of the peak shape for each case where the number of peaks is 1 to N, and the result is displayed on the display 112. Furthermore, it is configured to be able to determine the appropriate number of peaks and set the number of peaks by comparing the predicted distribution of the peak shape in each number of peaks.

[0093] Below, use Figure 5 to Figure 7 A specific example will be described. Figure 5 This is a diagram showing a display example of predicted distribution of peak shapes when it is assumed that the number of peaks is two. Figure 6 This is a diagram showing a display example of predicted distribution of peak shapes when it is assumed that the number of peaks is 1.

[0094] The posterior distribution of the parameter can be obtained as an estimated value by Bayesian estimation, and the predicted value (predicted distribution) of each peak shape is generated based on the estimated value. Figure 5 , Figure 6 The peak shape estimated from the posterior distribution of each parameter is plotted. Here, the solid line represents the observed waveform. The area surrounded by the dotted line represents the 95% prediction interval on both sides of the predicted distribution of the signal waveform obtained from the estimated quantity (the posterior distribution of the model parameters).

[0095] like Figure 6 As shown in FIG. 1 , when the number of peaks is assumed to be 1 (applying the 1-mixed BEMG function model), it can be confirmed that the predicted distribution on the right side of the peak is expanding, resulting in a large error. Figure 5As shown, when the number of peaks is assumed to be two (the 2-mixed BEMG function model is applied), the error on the right side of the peak is smaller than when the number of peaks is assumed to be one.

[0096] Although not shown, it is also possible to display the predicted distribution of peak shapes when the number of peaks is 3, 4, ... N. Then, when the user determines that the number of peaks = 2 is appropriate, the number of peaks = 2 is set by operating the keyboard 111. Thus, the predicted distribution of the peak shape with the number of peaks = 2 is selected as the estimated result. In addition, based on the predicted distribution of the peak shape, the predicted distribution of the quantitative index and the like are calculated.

[0097] Figure 7 This is a diagram showing an example of the predicted distribution of each peak shape when the number of peaks is assumed to be 2. Here, the solid line represents the observed waveform (measured waveform). The area surrounded by the dotted line represents the 95% predicted interval of the shape of Peak 1 (main peak). The area surrounded by the dotted line represents the 95% predicted interval of the shape of Peak 2 (shoulder peak). Figure 7 The parameter A i 、u i 、s i 、a i , b i The posterior distribution of (in the case of Bayesian inference through sampling, it is the samples obtained according to the posterior distribution) is substituted into the model to express the predicted distribution of the peak shape itself excluding the error term.

[0098] like Figure 7 As shown in Figure 2, it can be seen that peak 2 (shoulder peak) exists on the right side close to peak 1 (main peak). Figure 1 For example, the number of peaks = 1 is the case where there is only a main peak, and the number of peaks = 2 is the case where there are impurity peaks (shoulder peaks) in addition to the main peak. When the peaks are close to each other and tailing occurs, the uncertainty becomes higher because the tail and impurity peaks overlap. As mentioned above, this uncertainty can be evaluated by using the predicted distribution of methods such as Bayesian inference.

[0099] As described above, in the present embodiment, the user visually confirms which of the 1 to N peak numbers is appropriate, so that the user selects (sets) the optimal number of peaks. However, not limited to this, the data processing device 100 may also select the optimal number of peaks. In this case, it is also possible to automatically select using a benchmark such as an information quantity standard or a Bayes factor. For example, when using an information quantity standard as a benchmark, when selecting any one of a plurality of models, the model with the smallest evaluation value is selected as the appropriate model.

[0100] Alternatively, the number of peaks may be selected by both the user and the data processing device 100. For example, the predicted distribution of each peak shape when the number of peaks is 1 to N is displayed, and the number of peaks selected by the data processing device 100 is displayed. In this case, it may be configured so that the user can visually determine the appropriate number of peaks, and when the number of peaks is different from the number of peaks selected by the data processing device 100, it is changed to the number of peaks determined by the user.

[0101] [Predicted distribution and quantiles of quantitative indicators]

[0102] As described above, the calculation unit 104 calculates the predicted distribution of the quantitative index for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit 102. The display processing unit 105 performs processing to display the predicted distribution of the quantitative index calculated by the calculation unit 104 on the display 112.

[0103] Here, the quantitative index includes at least the height of the peak shape (also referred to as "peak height") and the area of ​​the peak shape (also referred to as "peak area"). In addition, it may also include the concentration of a substance calculated based on the peak height or peak area (also referred to as "substance concentration"). "Substance concentration" is the concentration of a substance contained in the sample, i.e., a substance corresponding to each peak.

[0104] Figure 8 It is a diagram showing a display example of predicted distribution of peak areas. Figure 8 An example is Figure 7 In the example of , based on the predicted distribution of each peak shape when the number of peaks is 2, the predicted distribution of the peak area is calculated by the calculation unit 104 as a quantitative index, and the predicted distribution is displayed on the display 112.

[0105] Figure 8 The predicted distribution of the peak area of ​​Peak 1 (main peak) is calculated on the left side of Figure 8 The predicted distribution of the peak area of ​​Peak 2 (shoulder peak) is calculated on the right side of , and the result is plotted as a violin plot. Here, the vertical axis represents the peak area, and the horizontal axis shows the probability density of Peak 1 or Peak 2 symmetrically.

[0106] The average peak area of ​​Peak 2 (shoulder peak) is 0.130, the median is 0.113, and the 95% prediction interval on both sides is [0.079, 0.278]. As shown in the figure, since the distribution shape is asymmetrical, the upper end extends to 0.5, so it is easy to understand visually or intuitively that it may become a maximum value. In addition, it can be intuitively seen from the figure that the area of ​​Peak 1 is about 1.0, the area of ​​Peak 2 is about 0.1, and the area of ​​Peak 2 is about 1 / 10 of the area of ​​Peak 1.

[0107] Although not shown, Figure 8Similarly, based on the predicted distribution of the peak shape estimated by the estimation unit 102, the predicted distribution of the peak height or the substance concentration can be calculated and displayed on the display 112. These displays can be switched by the user's operation. In addition, when drawing, in the case of probability distributions of two variables, by summing up and peripheralizing the probabilities corresponding to all values ​​that one variable can take, another probability distribution is obtained and drawn.

[0108] In addition, the predicted distribution of the quantitative indicator includes the distribution of the ratio of substances corresponding to each peak shape. For example, when the quantitative indicator is the "ratio of peak heights between peak shapes (also referred to as "height ratio")", the predicted distribution of the quantitative indicator is the predicted distribution of height ratio. When the quantitative indicator is the "ratio of peak areas between peak shapes (also referred to as "area ratio")", the predicted distribution of the quantitative indicator is the predicted distribution of area ratio. When the quantitative indicator is the "ratio of concentrations between substances (also referred to as "concentration ratio")", the predicted distribution of the quantitative indicator is the predicted distribution of concentration ratio.

[0109] For example, in the above example where there are peaks 1 and 2, the area ratio = "peak area of ​​peak 2 / peak area of ​​peak 1". In the case where peak 2 is an impurity, the area ratio refers to the ratio of the impurity to the substance corresponding to peak 1. In addition, it can also be that the area ratio = "peak area of ​​peak 2 / (peak area of ​​peak 1+peak area of ​​peak 2)". Or, in the case where there are N peaks, it can be that the area ratio = "peak area of ​​a certain peak / sum of peak areas of peaks 1 to N", and the area ratio can also be calculated for two peaks as comparison objects.

[0110] In addition, if Figure 8 As shown in FIG. 1 , when displaying peak areas, multiple peaks can be displayed simultaneously or one by one. Fig. 9 As shown, when displaying the area ratio, a plurality of area ratios may be displayed simultaneously, or they may be switched and displayed one by one.

[0111] Fig. 9 This is a diagram showing an example of displaying the predicted distribution and quantiles of the area ratio. Fig. 9 Represents a histogram and its kernel density inference.

[0112] As described above, the input unit 103 inputs a threshold value based on the user's operation. The calculation unit 104 calculates the predicted distribution of the quantitative index for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit 102, and further calculates the quantile of the threshold value in the calculated predicted distribution of the quantitative index. The display processing unit 105 can operate in a manner to display the probability of being above or below the threshold value inputted by the input unit 103 as a quantile.

[0113] In this example, "area ratio" is input as the quantitative index, and "0.95" is input as the threshold value. The calculation unit 104 calculates the predicted distribution of the area ratio for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit 102, and calculates the quantile of the threshold value (0.95) in the calculated predicted distribution of the area ratio. The display processing unit 105 displays the probability of being above the threshold value (0.95) input by the input unit 103 as a quantile.

[0114] exist Fig. 9 In the example, the calculated quantile = "20.4%", and "P (area [%] < 20.4) = 0.95" is displayed. This means that the area ratio (peak area of ​​peak 2 / peak area of ​​peak 1) is less than 20.4% with a probability (safety rate) of 0.95 (95%). In this example, "0.95" is entered as the threshold, and the quantile calculated accordingly is "20.4%". In addition, although in Figure 8 Although not shown in FIG. 1 , the peak area of ​​Peak 1 and the peak area of ​​Peak 2 have a one-to-one correspondence as internal data. Therefore, Area Ratio = Peak Area of ​​Peak 2 / Peak Area of ​​Peak 1 is also uniquely determined.

[0115] Alternatively, it can also be indicated that the area ratio is greater than 20.4% with a probability of 0.05 (5%). In this case, "0.05" is input as the threshold value, and the quantile obtained relative to this is "20.4%". The display processing unit 105 displays the probability below the threshold value (0.05) input by the input unit 103 as the quantile. For example, "P(area [%] ≥ 20.4) = 0.05" can also be displayed.

[0116] In this case, in other words, it can be said that the probability (hazard rate) that the area ratio of peak 2 (shoulder peak) to peak 1 is 20.4% or more is 5% (= 1-0.95). Alternatively, the area ratio can be input as a threshold value to calculate the hazard rate. For example, when the user inputs "20.4%" as the threshold value, the hazard rate = "5%" is calculated as the quantile.

[0117] In addition, the display processing unit 105 can operate in a manner that displays the percentage point of the predicted distribution of the quantitative indicator corresponding to the threshold value inputted by the input unit 103. In this example, the 95% point is 0.204. Fig. 9 As shown, the 95% point is shown as a percentage point by a vertical dotted line. Unlike a normal prediction interval, the prediction interval in Bayesian estimation can be explained intuitively in this way.

[0118] In addition, in the above example, the number of peaks is 2, but there may be more than 3 peaks. In this case, Figure 8In this case, you can also select any two peaks and display them as follows: Fig. 9 The predicted distribution of area ratios is shown.

[0119] Here, the risk ratio is set to 5% (threshold value is 0.95), but the threshold value can be specified by the user as described above. If the risk ratio is specified, the area ratio of the impurity peak can be known as the value that can be obtained within the specified risk ratio range.

[0120] Furthermore, when a threshold value input is newly received from the input unit 103 , the display processing unit 105 may be configured to operate so as to display percentage points again in accordance with the newly received threshold value.

[0121] For example, the display processing unit 105 may display percentage points based on the threshold value “0.95” after “0.95” is input as the threshold value in the input unit 103 , and then display percentage points based on the threshold value “0.97” when “0.97” is input as the threshold value in the input unit 103 .

[0122] Specifically, the threshold value is set to "0.95" (risk rate 5%). Fig. 9 After the display shown, the threshold value (e.g., "0.97" (hazard rate 3%)) can be changed to update the display. In this case, for example, the user can verify that the area ratio of the shoulder peak is 20.4% or more when the hazard rate is 5%, and is 22% or more when the hazard rate is 3%. In this case, the display percentage point is also changed. In this way, the user can set and update an arbitrary value as a threshold value, and display the result on the display 112 for evaluation and verification.

[0123] In particular, in the scenario of impurity analysis in pharmaceuticals, there is a strong demand for the evaluation and verification described above. Unnecessary chemical substances, i.e. pharmaceutical impurities, remaining in the active ingredients of pharmaceuticals are subject to reporting obligations when they are present at a concentration above a prescribed level. In addition, when the peaks between the active ingredient and the impurity or between the impurities are close, errors are particularly prone to occur, as described above. Based on this situation, there is a strong demand to understand the risk of errors to what extent regarding the impurity content.

[0124] In addition, although not shown in the figure, Fig. 9 Similarly, the predicted distribution of the height ratio or the concentration ratio can be calculated and displayed on the display 112. In addition, in this case, the percentage point or quantile of the predicted distribution of the height ratio or the concentration ratio is displayed based on the threshold value. These displays can be switched by the user's operation.

[0125] In addition, use Fig. 9 The display of the described quantiles is not limited to being displayed together with the predicted distribution of the area ratio. For example, the predicted distribution of each peak shape may be displayed together. Fig.10 11 is a diagram showing a display example of the predicted distribution and quantile of each peak shape. In this case, "peak shape" is input as a display item as a "quantitative index", and the predicted distribution of the peak shape (95% prediction interval for each peak) is displayed on the display 112.

[0126] like Fig.10 As shown, "P(area [%] < 20.4) = 0.95" is shown together with the predicted distribution of each peak shape. This indicates that the peak area of ​​peak 2 / peak area of ​​peak 1 (area ratio) is less than 20.4% with a probability of 0.95 (95%).

[0127] As shown above, since the user can confirm the predicted distribution of the quantitative indicator, it is easy to intuitively grasp the statistical data about the quantitative indicator, and by confirming these data, it is possible to consider the error of the quantitative indicator to ensure a reasonable safety rate. In addition, since the user can confirm the quantile of the threshold in the predicted distribution of the quantitative indicator, it is possible to consider the error of the quantitative indicator to ensure a reasonable safety rate.

[0128] For example, it is possible to use an area ratio as a quantitative index. In this case, by considering the area error of each peak, it is possible to quantify each peak substance to ensure a reasonable safety rate. In the quantitative composition of the components in the chromatograph, usually, the concentration of each component is proportional to the peak height or peak area, so a predicted distribution of the component concentration is obtained. Based on the predicted distribution, it is possible to evaluate the possibility that each substance exceeds the threshold value specified by laws or existing treaties. In addition, when the number of peak functions (number of peaks) applied to the chromatogram is also used as an inference object, it is also possible to evaluate the probability of the presence or absence of impurities.

[0129] [Functional structure of the analysis device]

[0130] Fig.11 This is a block diagram showing an example of the functional configuration of an analysis device according to a modified example of the present embodiment.

[0131] In this embodiment, if Figure 2 As shown, the sample measurement device 10 performs a predetermined measurement on the sample, and the acquisition unit 101 of the data processing device 100 acquires a measurement waveform obtained by the predetermined measurement performed by the sample measurement device 10 .

[0132] On the other hand, the analysis device 1 according to the modification of the present embodiment is configured to include a data processing device 100 and a measuring unit 11. The measuring unit 11 performs a predetermined measurement on a sample. The acquisition unit 101 acquires a measurement waveform obtained by the predetermined measurement performed by the measuring unit 11.

[0133] That is, in the present embodiment, the sample measurement device 10 different from the data processing device 100 performs predetermined measurement on the sample, but in the modified example of the present embodiment, the measurement unit 11 included in the analysis device 1 performs predetermined measurement on the sample.

[0134] In this case, for example, Fig.11 As shown, the analyzing device 1 includes a measuring unit 11 , an acquiring unit 101 , an estimating unit 102 , an input unit 103 , a calculating unit 104 , a display processing unit 105 , an operating unit 121 , and a display unit 122 .

[0135] The analysis device 1 is, for example, the above-mentioned chromatograph analysis device (LC, GC) or chromatograph mass spectrometer (LC / MS, GC / MS). The measuring unit 11 is a device that performs a predetermined measurement on a sample, and the data processing device 100 is a device that performs data processing on the obtained measurement waveform. That is, the analysis device 1 is a device that has either a device for measuring or a device for data processing. The data processing device 100 may also be a substrate or module for data processing.

[0136] The data processing device 100 includes an acquisition unit 101, an estimation unit 102, an input unit 103, a calculation unit 104, and a display processing unit 105. However, the contents of the processing performed by these units are different from those of the user. Figures 1 to 10 The data processing device 100 receives an operation from a user via an operation unit 121 provided in the data processing device 100 , and displays the operation on a display unit 122 provided in the data processing device 100 .

[0137] In the present embodiment, for example, it is assumed that a program for processing the measured waveform data is installed on a personal computer connected to the LC analysis device, or that the personal computer is connected to a server device that executes the program for processing the measured waveform data. However, in a variation of the present embodiment, the LC analysis device itself is configured to execute the program for processing the measured waveform data.

[0138] [plan]

[0139] It will be understood by those skilled in the art that the above-described embodiments and their modifications are specific examples of the following aspects.

[0140] (Item 1) A data processing device of one scheme performs data processing on a measurement waveform obtained by performing a predetermined measurement on a sample. The data processing device includes an estimation unit, a calculation unit, and a display processing unit. The estimation unit estimates the predicted distribution of each peak shape for a plurality of adjacent peak waveforms contained in the measurement waveform using a predetermined peak shape model. The calculation unit calculates the predicted distribution of a quantitative index for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit. The display processing unit can operate in a manner that displays the predicted distribution of the quantitative index calculated by the calculation unit.

[0141] According to such a configuration, since the user can confirm the predicted distribution of the quantitative index, the user can easily and intuitively grasp the statistical data on the quantitative index, and by confirming these data, a reasonable safety rate can be ensured by taking errors into account for the quantitative index.

[0142] (Item 2) In the data processing device according to Item 1, the calculation unit calculates a quantile of a threshold value in the calculated prediction distribution of the quantitative index. The display processing unit can operate to display the quantile calculated by the calculation unit.

[0143] According to such a configuration, since the user can confirm the quantile of the threshold value in the predicted distribution of the quantitative index, it is possible to ensure a reasonable safety rate by taking errors into account for the quantitative index.

[0144] (Item 3) The data processing device described in Item 1 or Item 2 further includes an input unit for receiving user input. The calculation unit calculates a first predicted distribution and a second predicted distribution based on mutually related quantitative indicators for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit. The display processing unit can operate in a manner that changes the display method of the first predicted distribution and the second predicted distribution based on the selection of the user whose input is received by the input unit.

[0145] According to such a configuration, since the display mode of the first predicted distribution and the second predicted distribution can be changed based on the user's selection, for example, only the first predicted distribution or only the second predicted distribution can be displayed, statistical data on quantitative indicators can be easily and intuitively grasped.

[0146] (Item 4) The data processing device according to Item 2 further comprises an input unit for receiving a user input. The display processing unit can operate to display the probability of being above or below a threshold value inputted by the input unit as a quantile.

[0147] According to such a configuration, it is possible to appropriately perform evaluation based on a quantile calculated based on a threshold value input according to the user's intention.

[0148] (Item 5) The data processing device according to Item 2 further comprises an input unit for receiving a user input. The display processing unit can operate to display a percentage point of a predicted distribution of a quantitative indicator corresponding to a threshold value inputted by the input unit.

[0149] According to such a configuration, it is possible to appropriately perform evaluation based on percentage points displayed based on a threshold value input according to the user's intention.

[0150] (Item 6) In the data processing device described in Item 5, when the input unit newly receives an input of a threshold value, the display processing unit can operate in a manner to display percentage points again corresponding to the newly received threshold value.

[0151] According to such a configuration, it is possible to appropriately perform evaluation based on percentage points displayed based on a threshold value changed according to the user's intention.

[0152] (Item 7) In the data processing device described in any one of Items 1 to 6, the quantitative index includes at least the height of the peak shape and the area of ​​the peak shape.

[0153] According to such a configuration, for example, evaluation targeting a specific substance such as a main component or impurities can be appropriately performed.

[0154] (Item 8) In the data processing device described in Item 7, the predicted distribution of the quantitative index includes the distribution of the ratio of substances corresponding to each peak shape.

[0155] According to such a configuration, for example, it is possible to appropriately perform evaluation on the ratio of impurities contained in the main component or the ratio of impurities to each other.

[0156] (Item 9) In the data processing device described in any one of Items 1 to 8, the estimating unit estimates the predicted distribution of each peak shape by Bayesian estimation.

[0157] According to such a configuration, it is possible to appropriately perform evaluation regarding quantitative indices.

[0158] (Item 10) An analysis device includes the data processing device according to any one of Items 1 to 9, and a measurement unit that performs predetermined measurement on a sample.

[0159] According to such a configuration, it is possible to perform predetermined measurements on a sample and evaluate quantitative indices using only one device (analyzer).

[0160] (Item 11) A data processing method of one scheme performs data processing on a measurement waveform obtained by performing a predetermined measurement on a sample. The data processing method comprises: a step of estimating a predicted distribution of each peak shape using a predetermined peak shape model for each of a plurality of adjacent peak waveforms included in the measurement waveform; a step of calculating a predicted distribution of a quantitative index for each peak waveform based on the estimated predicted distribution of the peak shape; and a step of displaying the calculated predicted distribution of the quantitative index.

[0161] According to such a configuration, since the user can confirm the predicted distribution of the quantitative index, the user can easily and intuitively grasp the statistical data on the quantitative index, and by confirming these data, a reasonable safety rate can be ensured by taking errors into account for the quantitative index.

[0162] (Item 12) The data processing method described in Item 11 further comprises: a step of calculating a quantile of a threshold value in the calculated predicted distribution of the quantitative index; and a step of displaying the calculated quantile.

[0163] According to such a configuration, since the user can confirm the quantile of the threshold value in the predicted distribution of the quantitative index, it is possible to ensure a reasonable safety rate by taking errors into account for the quantitative index.

[0164] (Item 13) The data processing method described in Item 11 or Item 12 also comprises: a step of receiving user input; a step of calculating a first predicted distribution and a second predicted distribution based on mutually correlated quantitative indicators for each peak waveform based on the estimated predicted distribution of the peak shape; and a step of changing the display method of the first predicted distribution and the second predicted distribution based on the user's selection of the received input.

[0165] According to such a configuration, since the display mode of the first predicted distribution and the second predicted distribution can be changed based on the user's selection, for example, only the first predicted distribution or only the second predicted distribution can be displayed, statistical data on quantitative indicators can be easily and intuitively grasped.

[0166] (Item 14) A data processing program of one scheme performs data processing on a measurement waveform obtained by performing a predetermined measurement on a sample. The data processing program causes a computer to execute the following steps: a step of estimating a predicted distribution of each peak shape using a predetermined peak shape model for each of a plurality of adjacent peak waveforms included in the measurement waveform; a step of calculating a predicted distribution of a quantitative index for each peak waveform based on the estimated predicted distribution of the peak shape; and a step of displaying the calculated predicted distribution of the quantitative index.

[0167] According to such a configuration, since the user can confirm the predicted distribution of the quantitative index, the user can easily and intuitively grasp the statistical data on the quantitative index, and by confirming these data, a reasonable safety rate can be ensured by taking errors into account for the quantitative index.

[0168] The embodiments disclosed this time should be considered as illustrative in all aspects and not restrictive. The scope of the present invention is indicated not by the description of the above embodiments but by the claims, which means that all changes within the meaning and scope equivalent to the claims are also included.

[0169] Industrial Applicability

[0170] The present disclosure is used to quantify the peak area (or peak height or concentration) of each substance contained in a sample and evaluate its robustness (evaluation of quantitative indicators) when the measurement waveform, such as a chromatogram, contains multiple close peak waveforms and peak separation becomes important due to the overlap of multiple peaks.

[0171] Description of Reference Numerals

[0172] 1Analytical device

[0173] 10. Sample measuring device

[0174] 11 Measurement Department

[0175] 100 Data processing device

[0176] 101 Acquisition Department

[0177] 102 Presumption Department

[0178] 103 Input unit

[0179] 104 Computing Department

[0180] 105 display processing unit

[0181] 111 Keyboard

[0182] 112 Display

[0183] 121 Operation Department

[0184] 122 display unit.

Claims

1. A data processing device for performing data processing on a measurement waveform obtained by performing a predetermined measurement on a sample, characterized in that: have: an estimating unit that estimates a predicted distribution of each peak shape using a predetermined peak shape model for each of a plurality of adjacent peak waveforms included in the measurement waveform; a calculation unit that calculates a predicted distribution of a quantitative index for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit; a display processing unit operable to display the predicted distribution of the quantitative index calculated by the calculation unit; The calculation unit calculates a first predicted distribution and a second predicted distribution based on the mutually correlated quantitative indexes for each peak waveform based on the predicted distribution of the peak shape estimated by the estimation unit, The display processing unit is capable of operating in a manner of changing the display mode of the first predicted distribution and the second predicted distribution. The estimation unit estimates the predicted distribution of peak shapes respectively when assuming that the number of peaks is 1 to N, determines the appropriate number of peaks and sets the peak number based on the comparison of the predicted distribution of peak shapes in each number of peaks, and selects the predicted distribution of peak shapes of the set number of peaks as the estimation result.

2. The data processing device according to claim 1, characterized in that The plurality of peak waveforms include a first peak waveform and a second peak waveform, The quantitative index includes a first quantitative index and a second quantitative index, The change in the display method of the first predicted distribution and the second predicted distribution includes: a change selected from the group consisting of a change from the display of the first predicted distribution calculated by the calculation unit for the first peak waveform to the display of the second predicted distribution calculated for the second peak waveform, and a change from the display of the first predicted distribution calculated by the calculation unit based on the first quantitative indicator to the display of the second predicted distribution calculated based on the second quantitative indicator.

3. The data processing device according to claim 1, characterized in that The calculation unit calculates a quantile of a threshold value in the calculated prediction distribution of the quantitative index; The display processing unit is operable to display the quantile calculated by the calculation unit.

4. The data processing device according to claim 1, characterized in that further comprising an input unit for receiving user input, The display processing unit may operate to change the display form of the first predicted distribution and the second predicted distribution based on a user's selection whose input is received by the input unit.

5. The data processing device according to claim 3, characterized in that further comprising an input unit for receiving user input, The display processing unit may operate so as to display the probability of being equal to or greater than the threshold or equal to or less than the threshold inputted by the input unit as the quantile.

6. The data processing device according to claim 3, characterized in that further comprising an input unit for receiving user input, The display processing unit may operate to display a percentage point of the predicted distribution of the quantitative index corresponding to the threshold value inputted by the input unit.

7. The data processing device according to claim 6, characterized in that When the input unit newly receives an input of the threshold value, the display processing unit may operate to display the percentage points again in accordance with the newly received threshold value.

8. The data processing device according to any one of claims 1 to 7, characterized in that: The quantitative index includes at least the height of the peak shape and the area of ​​the peak shape.

9. The data processing device according to claim 8, characterized in that The predicted distribution of the quantitative index includes the distribution of the ratio of substances corresponding to each peak shape.

10. The data processing device according to claim 1, characterized in that The estimating unit estimates the predicted distribution of each peak shape by Bayesian estimation.

11. An analysis device, characterized in that: have: The data processing device according to claim 1; A measuring unit performs the predetermined measurement on the sample.

12. A data processing method for processing a measurement waveform obtained by performing a predetermined measurement on a sample, characterized in that: have: A step of estimating a predicted distribution of each peak shape using a predetermined peak shape model for each of a plurality of adjacent peak waveforms included in the measurement waveform; The step of calculating a predicted distribution of a quantitative index for each peak waveform based on the estimated predicted distribution of the peak shape; The step of displaying the calculated predicted distribution of the quantitative indicator; A step of calculating a first predicted distribution and a second predicted distribution based on the quantitative indexes correlated with each other for each peak waveform based on the estimated predicted distribution of the peak shape; The step of changing the display mode of the first predicted distribution and the second predicted distribution, Assuming that the number of peaks is 1 to N, the predicted distribution of peak shapes is estimated respectively, and the appropriate number of peaks is determined and set based on the comparison of the predicted distribution of peak shapes in each number of peaks, and the predicted distribution of peak shapes for the set number of peaks is selected as the estimation result.

13. The data processing method according to claim 12, characterized in that: Also available: The step of calculating the quantile of the threshold value in the calculated prediction distribution of the quantitative indicator; Displays the steps for calculating the quantile.

14. The data processing method according to claim 12, characterized in that: Also available: The step of receiving user input; The step of changing the display form of the first predicted distribution and the second predicted distribution based on the user's selection of the received input.

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