Processing unit, system, method, and program

Abstract

To provide a processing unit, a system, a method, and a program that perform non-negative value matrix factorization of a measurement profile of X-ray powder diffraction based upon known information.SOLUTION: The present invention relates to a processing unit 400 that performs non-negative value matrix factorization of a measurement profile of X-ray powder diffraction, and comprises: a measurement profile acquisition part 410 which acquires one or more measurement profiles; a known information acquisition part 420 which acquires known information including the shape of a background included in the measurement profile or a predetermined profile corresponding to a predetermined substance, or limitations of a coefficient matrix of the predetermined profile; and a decomposition part 430 which decomposes the measurement profile into non-negative value matrix factors based upon the known information.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a processing device, a system, a method, and a program. [Background technology]

[0002] X-ray powder diffraction is used in a variety of fields. By analyzing the measurement profile of X-ray powder diffraction, for example, it is possible to identify (qualitative analysis) and quantify the constituents of a powder sample. Conventionally, crystalline phases were identified by comparing the measurement profile or a dI list created from the measurement profile with the diffraction pattern of a known substance.

[0003] Patent Document 1 discloses a crystalline phase identification method for identifying a crystalline phase contained in a sample from a powder diffraction pattern of the sample using a database, the method comprising: {a total pattern fitting step, using information on the crystalline phase contained in the sample, performing total pattern fitting on a first diffraction pattern, which is a powder diffraction pattern of the sample, to calculate a theoretical diffraction pattern of the already identified crystalline phase}; {a residual information generation step, generating residual information of the sample based on the difference between the theoretical diffraction pattern and the first diffraction pattern}; and {a residual information search and match step, comparing the residual information with the database to select a new crystalline phase contained in the sample}.

[0004] Patent Document 2 discloses a spectral data analysis device that obtains a plurality of basis spectral data and activation data representing the magnitude of each basis spectrum by performing nonnegative matrix decomposition on a set of observed spectral data obtained for a signal to be analyzed, and that obtains the plurality of basis spectral data and the activation data by searching for a minimum value of an objective function that includes a regularization term that evaluates the degree of deviation between the set of observed spectral data and a set of estimated spectral data calculated from the plurality of basis spectral data and the activation data, and evaluates the linear independence of the plurality of basis spectral data or the activation data. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2014-178203 A [Patent Document 2] JP 2019-87042 A Summary of the Invention [Problem to be solved by the invention]

[0006] When the mixture contains a large amount of mixture, the peaks of the X-ray powder diffraction measurement profile tend to overlap each other. However, in such a case, if the method described in Patent Document 1 in which the measurement profile is not processed and a search match is performed using a dI list as in the past, is applied, the accuracy of the qualitative analysis is deteriorated.

[0007] Furthermore, the technology described in Patent Document 2 improves the accuracy of decomposition by assuming that the profiles are highly independent from each other. However, when there is a large overlap of peaks in each profile, imposing regularization regarding linear independence increases the risk that each profile cannot be accurately decomposed, and the accuracy of the subsequent qualitative analysis deteriorates.

[0008] As a result of intensive research, the inventors have found that X-ray powder diffraction measurement profiles often contain accompanying known information, and that by subjecting the measurement profile to non-negative matrix factorization based on the known information, the accuracy of the decomposition and the accuracy of the subsequent qualitative analysis are higher than when non-negative matrix factorization is performed without using the known information, and have completed the present invention.

[0009] The present invention has been made in consideration of the above circumstances, and has an object to provide a processing device, system, method, and program for performing non-negative matrix factorization on an X-ray powder diffraction measurement profile based on known information. [Means for solving the problem]

[0010] (1) In order to achieve the above object, the processing device of the present invention is a processing device that performs non-negative matrix factorization on an X-ray powder diffraction measurement profile, and is characterized by comprising: a measurement profile acquisition unit that acquires one or more of the measurement profiles; a known information acquisition unit that acquires known information including a background included in the measurement profile or a shape of a predetermined profile corresponding to a predetermined substance, or a restriction on a coefficient matrix of the predetermined profile; and a decomposition unit that performs non-negative matrix factorization on the measurement profile based on the known information.

[0011] (2) In the processing device of the present invention, the decomposition unit selectively performs normal nonnegative matrix factorization or constrained nonnegative matrix factorization based on the known information, depending on whether the known information is present or not.

[0012] (3) In the processing device of the present invention, the known information is information that values ​​of the coefficient matrix of the predetermined profile included in common in the multiple measurement profiles are equal.

[0013] (4) In the processing apparatus of the present invention, the known information is information on the shape of the predetermined profile included in the measurement profile.

[0014] (5) In the processing apparatus of the present invention, the information on the shape of the predetermined profile is information obtained from a database or information based on actually measured data.

[0015] (6) Furthermore, the processing device of the present invention further includes a dendrogram creation unit that calculates statistics between a plurality of the measurement profiles and creates a dendrogram, and the decomposition unit is characterized in that it performs non-negative matrix factorization on clusters including a group of similar profiles selected by the processing device or by a user from the dendrogram.

[0016] (7) The processing device of the present invention is further characterized by including a peak search unit that performs a peak search on the profile after the nonnegative matrix factorization and creates a dI list, and a qualitative unit that performs qualitative analysis using the dI list.

[0017] (8) The processing device of the present invention is further characterized by comprising a quantification unit that performs quantitative analysis using the qualitatively analyzed data.

[0018] (9) The system of the present invention is characterized by comprising an X-ray diffraction apparatus including an X-ray generating unit that generates X-rays, a detector that detects X-rays, and a goniometer that controls the rotation of the sample, and a processing apparatus described in any one of (1) to (8) above.

[0019] (10) Also, a method of the present invention is a method for non-negative matrix factorization of a measurement profile of X-ray powder diffraction, comprising the steps of: acquiring one or more of the measurement profiles; acquiring known information including a shape of a predetermined profile corresponding to a background or a predetermined substance contained in the measurement profile, or a restriction on a coefficient matrix of the predetermined profile; and performing non-negative matrix factorization on the measurement profile based on the known information.

[0020] (11) Also, a program of the present invention is a program for performing nonnegative matrix factorization on a measurement profile of X-ray powder diffraction, characterized in that the program causes a computer to execute the following processes: a process for acquiring one or more of the measurement profiles; a process for acquiring known information including a shape of a predetermined profile corresponding to a background or a predetermined substance contained in the measurement profile, or a restriction on a coefficient matrix of the predetermined profile; and a process for performing nonnegative matrix factorization on the measurement profile based on the known information. [Brief description of the drawings]

[0021] [Figure 1] 1A and 1B are conceptual diagrams showing nonnegative matrix factorization and nonnegative matrix factorization in the case where known information is included, respectively. [Diagram 2] FIG. 1 is a conceptual diagram showing an example of the configuration of an X-ray diffraction measurement system. [Diagram 3] FIG. 2 is a block diagram showing an example of the configuration of a control device and a processing device. [Figure 4] FIG. 13 is a block diagram showing a modified example of the configuration of the control device and the processing device. [Diagram 5] FIG. 13 is a block diagram showing a modified example of the configuration of the control device and the processing device. [Figure 6] FIG. 13 is a block diagram showing a modified example of the configuration of the processing device. [Figure 7] FIG. 13 is a block diagram showing a modified example of the configuration of the processing device. [Figure 8] 1(b) is a schematic diagram showing an example of a UI for calling up a cluster analysis function, and FIG. [Figure 9] (a) is a schematic diagram illustrating some of the functions of the cluster analysis UI. [Figure 10] FIG. 13 is a schematic diagram showing an example of a dialog for setting known information. [Figure 11] FIG. 13 is a schematic diagram showing an example of a UI for setting up dendrogram creation, etc. [Figure 12]13 is a schematic diagram showing an example of a UI for setting search-match and quantification functions, etc. [Figure 13] 10 is a flowchart showing an example of an operation of the processing device. [Figure 14] 10 is a flowchart showing a modified example of the operation of the processing device. [Figure 15] 10 is a flowchart showing a modified example of the operation of the processing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] Next, an embodiment of the present invention will be described with reference to the drawings. In order to facilitate understanding of the description, the same reference numerals are used to refer to the same components in each drawing, and duplicated description will be omitted.

[0023] [principle] In X-ray powder diffraction measurement profiles, the profiles of multiple substances and backgrounds overlap. When there are many mixtures, the peaks overlap more. In such cases, the accuracy of the peak search is poor, and the conventional search and match method using a dI list is often not suitable.

[0024] Non-negative matrix factorization (NMF) is a method of decomposing a non-negative matrix into a product of non-negative matrices. To facilitate search and match, we consider decomposing the measured profile of X-ray powder diffraction into a weighted sum of multiple profiles (including background profiles). Since each profile and its weight are non-negative, non-negative matrix factorization is suitable for expressing the measured profile of X-ray powder diffraction as a weighted sum of multiple profiles.

[0025] FIG. 1(a) is a conceptual diagram showing nonnegative matrix factorization. The left side of FIG. 1(a) shows a matrix in which n measurement profiles of X-ray powder diffraction with m measurement points are arranged. The right side of FIG. 1(a) shows the result of nonnegative matrix factorization of this. However, the wavy equal in FIG. 1 does not only mean a strict match, but also includes cases where the degree of discrepancy, which indicates the degree of closeness between the left and right sides, is equal to or less than a predetermined value.

[0026] The measurement profiles of X-ray powder diffraction may have a large overlap of profiles. The case where the overlap of profiles is large refers to, for example, the case where the peaks of each profile overlap a lot, the case where amorphous is included, the case where the background is large, etc. In such a case, it is not appropriate to perform non-negative matrix factorization by imposing regularization regarding linear independence as in Patent Document 1, since it may deteriorate the accuracy of the subsequent search match.

[0027] In addition, the measurement profile of X-ray powder diffraction is often accompanied by known information. The method of the present invention performs non-negative matrix factorization on the measurement profile of X-ray powder diffraction based on known information. Non-negative matrix factorization of the measurement profile based on known information means that the measurement profile is non-negative matrix factorized so that the known information is used as a constraint and the constraint is satisfied. The method of the present invention can perform non-negative matrix factorization with high accuracy even when there is a large overlap of profiles. Note that, although various methods have been proposed for non-negative matrix factorization when a non-negative matrix is ​​given, the present invention can use a general method.

[0028] In FIG. 1(a), it is assumed that known information accompanying the measurement profile of X-ray powder diffraction is known. The known information is information accompanying the measurement profile of X-ray powder diffraction. Details will be described later. In such a case, a decomposition in which the known information is appropriately expressed as a result of nonnegative matrix factorization will be a more accurate decomposition. When known information is included on the right side of FIG. 1(a), it can be extracted and expressed, for example, as the second term on the right side of FIG. 1(b). FIG. 1(b) is a conceptual diagram showing the nonnegative matrix factorization when known information is included.

[0029] In the method of the present invention, depending on the type of known information, the known information is set to one or more of the matrix W with N rows and R columns, the matrix W' with N rows and S columns, or the matrix B' with S rows and M columns on the right side of FIG. 1(b). Under the constraints, W', W, and the matrix B with R rows and M columns are optimized. Optimization means to find a basis matrix and a coefficient matrix that make the deviation less than or equal to a predetermined value. In this way, it is possible to perform decomposition such that the known information is appropriately expressed as a result of nonnegative matrix factorization.

[0030] As described above, the method of the present invention can perform non-negative matrix factorization of a measured profile of X-ray powder diffraction based on known information. The detailed method of the present invention will be described in detail in the embodiments.

[0031] [Embodiment] The method of the present invention will be described in detail below. A method of performing non-negative matrix factorization on a measurement profile of X-ray powder diffraction measured by an X-ray diffractometer will be described below. In addition, a method of performing qualitative analysis and a method of performing quantitative analysis using the non-negative matrix factorization will be described.

[0032] Let X be an N-row, M-column matrix that arranges N X-ray powder diffraction measurement profiles having M measurement points. The nonnegative matrix factorization of X is expressed as the following formula (1). W is a coefficient matrix, and B is a basis matrix. W represents the weight of B. Each row of B is a basis vector. Furthermore, R is a hyperparameter that indicates the number of basis vectors.

[0033]

number

[0034] It is assumed that a part of W or B on the right side of the formula (1) contains known information associated with the measurement profile of X-ray powder diffraction. If the coefficient matrix or basis matrix indicating the known information is extracted from W or B in the formula (1) and designated as W' or B', the non-negative matrix factorization of X can be rewritten as the following formula (2). However, it is assumed that one or more of W, W', or B' contains known information. S is a hyperparameter indicating the number of basis vectors of the known information. In addition, the formulas from which W' and B' are extracted are again designated as W and B.

[0035]

number

[0036] The known information of the measurement profile of X-ray powder diffraction is information including the shape of the predetermined profile corresponding to the background or a predetermined substance included in the measurement profile, or a restriction on the coefficient matrix of the predetermined profile. Such known information is set to one or more of W, W', and B', and W', W, and B are optimized under the restriction. In this way, non-negative matrix factorization can be performed using the known information of the measurement profile of X-ray powder diffraction as a restriction. Note that Equation (2) is a format for clearly indicating the presence of known information, and known information may be set in Equation (1) for optimization.

[0037] The known information is preferably information that the values ​​of the coefficient matrices of a given profile commonly included in a plurality of measurement profiles are equal. This is non-negative matrix factorization with a constraint that the plurality of measurement profiles commonly contain equal amounts of components. For example, this can be applied when the plurality of measurement profiles have a common background derived from the device or contain equal amounts of a standard substance. It can also be applied when, for example, the plurality of measurement profiles are obtained by measuring one sample over time and there is a profile derived from a component that does not react during measurement.

[0038] For example, multiple measurement profiles measured under the same conditions using a single X-ray diffraction instrument contain the same background. In this way, when it is known that multiple measurement profiles have a common profile shape but the shape is unknown, the coefficient matrix W and the basis matrix B are optimized by restricting that the values ​​of certain columns of the matrix W must be equal.

[0039] The known information is preferably information on the shape of a predetermined profile included in the measurement profile. This is a non-negative matrix factorization with the constraint that one or more measurement profiles contain a known profile. For example, this can be applied when the background shape is known or when the contents are known. The information on the shape of the predetermined profile is preferably information obtained from a database or information based on actually measured data.

[0040] For example, when the shape of the background of the measurement profile is known, a profile indicating the shape of the background is set as a basis matrix B', and the coefficient matrices W', W and B are optimized.

[0041] It is preferable that the nonnegative matrix factorization is selectively performed by performing normal nonnegative matrix factorization or constrained nonnegative matrix factorization based on known information depending on the presence or absence of known information. In this way, when known information is present, the measurement profile can be subjected to nonnegative matrix factorization with high accuracy depending on the type and content of the known information. Also, when no known information is present, the measurement profile can be subjected to nonnegative matrix factorization without constraints.

[0042] Non-negative matrix factorization can apply optimization methods such as alternating least squares, multiplicative update, and coordinate descent with regularization. As regularization, sparsity of the weights or the profile to be decomposed can be imposed.

[0043] When there are multiple measurement profiles, it is preferable to calculate statistics between the measurement profiles and create a dendrogram, and it is also preferable to subject a cluster containing a group of similar profiles selected from the dendrogram by a processing device described below or selected by a user to non-negative matrix factorization.

[0044] In order to perform non-negative matrix factorization of multiple measurement profiles with high accuracy, it is preferable that most of the measurement profiles contain characteristic profiles. By creating a dendrogram and selecting a cluster containing a group of similar profiles from the created dendrogram, it is expected that measurement profiles that are unlikely to contain characteristic profiles can be removed. As a result, multiple measurement profiles can be subjected to non-negative matrix factorization with high accuracy.

[0045] It is preferable to perform a qualitative analysis after subjecting the measurement profile to nonnegative matrix factorization. The qualitative analysis is preferably performed by performing a peak search on the profile (basis vector) after nonnegative matrix factorization to create a dI list. The qualitative analysis can be performed by performing a search match using the created dI list. The qualitative analysis can be performed using a known method. Since the profile after nonnegative matrix factorization has less peak overlap than the measurement profile before nonnegative matrix factorization, it is often easier to identify components or to perform a qualitative analysis using a dI list created from the profile after factorization, which can increase the accuracy of identification.

[0046] When the measurement profile is subjected to non-negative matrix factorization and then qualitative analysis is performed, it is preferable to further perform quantitative analysis as necessary. The quantitative analysis is performed using the qualitatively analyzed data. The quantitative analysis can be performed using a known method.

[0047] In this way, the measurement profile of X-ray powder diffraction measured by an X-ray diffractometer can be subjected to non-negative matrix factorization based on known information, and can be used for qualitative and quantitative analysis.

[0048] [Entire system] Fig. 2 is a conceptual diagram showing an example of the configuration of an X-ray diffraction measurement system 100. The system 100 has an X-ray diffraction device 200, a control device 300, and a processing device 400. The X-ray diffraction device 200 forms an optical system that irradiates X-rays on a sample and detects diffracted X-rays generated from the sample, and the optical system has a goniometer. Note that the configuration shown in Fig. 2 is just an example, and various other configurations can be adopted.

[0049] The control device 300 is connected to the X-ray diffraction device 200, and controls the X-ray diffraction device 200 and processes and stores the acquired data. The processing device 400 performs non-negative matrix factorization on the measurement profile of X-ray powder diffraction. The control device 300 and the processing device 400 are devices equipped with a CPU and memory, and may be PC terminals or servers on the cloud. In addition to the entire device, some devices or some functions within the devices may be provided on the cloud. The input device 510 is, for example, a keyboard or a mouse, and performs input to the control device 300 and the processing device 400. The display device 520 is, for example, a display, and displays the measurement profile, the result of non-negative matrix factorization, etc.

[0050] By using such a system 100, it is possible to measure an X-ray powder diffraction profile and perform non-negative matrix factorization on the measured profile, and it is also possible to perform qualitative and quantitative analysis using the non-negative matrix factorized profile.

[0051] In FIG. 2, the control device 300 and the processing device 400 are described as the same PC. However, as described above, the method of the present invention can acquire a measurement profile and perform non-negative matrix factorization independently of the X-ray diffraction device 200 and the control device 300, so as shown in FIG. 3, the processing device 400 may be configured as a device different from the control device 300. FIG. 3 is a block diagram showing an example of the configuration of the control device 300 and the processing device 400. Also, as shown in FIG. 4, the processing device 400 may be configured as a part of the function included in the control device 300. Also, as shown in FIG. 5, the processing device 400 and the control device 300 may be configured as an integrated device. FIGS. 4 and 5 are block diagrams showing modified examples of the configuration of the control device 300 and the processing device 400. In the following, a case where the control device 300 and the processing device 400 are configured as different devices will be described.

[0052] [X-ray diffraction equipment] The X-ray diffraction apparatus 200 includes an X-ray generation unit 210 that generates X-rays from an X-ray focus, i.e., an X-ray source, an incident side optical unit 220, a goniometer 230, a sample stage 240 on which a sample is placed, an exit side optical unit 250, and a detector 260 that detects X-rays. The X-ray generation unit 210, the incident side optical unit 220, the goniometer 230, the sample stage 240, the exit side optical unit 250, and the detector 260 that configure the X-ray diffraction apparatus 200 may be general components, and therefore a description thereof will be omitted.

[0053] [Control device] The control device 300 is configured by a computer having a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and memories connected to a bus. The control device 300 is connected to the X-ray diffraction device 200 and receives information from the X-ray diffraction device 200.

[0054] The control device 300 includes a control unit 310, a device information storage unit 320, a measurement data storage unit 330, and a display unit 340. Each unit can send and receive information via a control bus L. The input device 510 and the display device 520 are connected to the CPU via an appropriate interface.

[0055] The control unit 310 controls the operation of the X-ray diffraction apparatus 200. The apparatus information storage unit 320 stores apparatus information acquired from the X-ray diffraction apparatus 200. The apparatus information includes information about the X-ray diffraction apparatus 200, such as the apparatus name, type of radiation source, wavelength, background, etc. In addition, information necessary for non-negative matrix factorization of the measurement profile of X-ray powder diffraction, such as the type and composition of the constituent elements of the sample, may be included.

[0056] The measurement data storage unit 330 stores the measurement profile acquired from the X-ray diffraction device 200. The measurement profile may also include information necessary for non-negative matrix factorization of the measurement profile of X-ray powder diffraction, such as the type of radiation source, wavelength, background, type and composition of the constituent elements of the sample, etc. The display unit 340 displays the measurement profile on the display device 520, allowing the user to confirm the measurement profile. The user can also give instructions and designations to the control device 300, processing device 400, etc. based on the measurement data.

[0057] [Processing device] The processing device 400 is configured by a computer having a CPU, a ROM, a RAM, and a memory connected to a bus. The processing device 400 may be connected to the X-ray diffraction device 200 via the control device 300.

[0058] The processing device 400 includes a measurement profile acquisition unit 410, a known information acquisition unit 420, and a decomposition unit 430. Each unit can transmit and receive information via a control bus L. When the processing device 400 and the control device 300 have different configurations, the input device 510 and the display device 520 are also connected to the CPU of the processing device 400 via an appropriate interface. In this case, the input device 510 and the display device 520 may be different from those connected to the control device 300.

[0059] The measurement profile acquisition unit 410 acquires one or more measurement profiles. The measurement profile acquisition unit 410 may acquire the measurement profile directly from the X-ray diffraction device 200 or via the control device 300.

[0060] The known information acquisition unit 420 acquires known information including the shape of a predetermined profile corresponding to a background or a predetermined substance included in the measurement profile, or a restriction on the coefficient matrix of the predetermined profile.

[0061] The known information acquired by the known information acquisition unit 420 is preferably information that the values ​​of the coefficient matrices of a predetermined profile commonly included in a plurality of measurement profiles are equal. This allows non-negative matrix factorization using such known information, for example, when the background from the X-ray diffraction device 200 is common or when equal amounts of a standard substance are included.

[0062] The known information acquired by the known information acquisition unit 420 is preferably information on the shape of a predetermined profile included in the measurement profile. This makes it possible to perform non-negative matrix factorization using the known information, for example, when the background shape or the contents are known. In addition, the information on the shape of the predetermined profile is preferably information acquired from a database or information based on actually measured data.

[0063] The decomposition unit 430 performs non-negative matrix factorization on the measurement profile based on the known information. Performing non-negative matrix factorization on the measurement profile based on the known information means performing non-negative matrix factorization on the measurement profile using the known information as a constraint so that the constraint is satisfied.

[0064] It is preferable that the decomposition unit 430 selectively performs normal nonnegative matrix factorization or constrained nonnegative matrix factorization based on the known information, depending on the presence or absence of known information.

[0065] Fig. 6 is a block diagram showing a modified example of the configuration of the processing device 400. As shown in Fig. 6, the processing device 400 preferably includes a dendrogram creation unit 440. The dendrogram creation unit 440 calculates statistics between a plurality of profiles and creates a dendrogram.

[0066] When the dendrogram creating unit 440 creates a dendrogram, the decomposing unit 430 preferably performs nonnegative matrix factorization on clusters including similar profiles selected from the dendrogram by the processing device 400 or by the user, thereby improving the accuracy of the nonnegative matrix factorization.

[0067] Fig. 7 is a block diagram showing a modified example of the configuration of the processing device 400. As shown in Fig. 7, the processing device 400 preferably includes a dendrogram creation section 440, a peak search section 450, a qualitative section 460, and a quantitative section 470. The dendrogram creation section 440 is the same as described above.

[0068] The peak search unit 450 performs a peak search on the profile (basis vector) after the nonnegative matrix factorization, and creates a dI list. The peak search is performed on one selected from the profiles resulting from the nonnegative matrix factorization. The profile may be selected by the user, or may be selected by the peak search unit 450 or another functional unit of the processing device 400. In addition, a dI list is created for each profile for which a peak search has been performed. It is preferable to perform a peak search on all profiles other than those determined to be background.

[0069] The qualitative analysis unit 460 performs qualitative analysis using the dI list. The qualitative analysis can be performed by performing search and match on the created dI list. The quantitative analysis unit 470 performs quantitative analysis using the qualitatively analyzed data. In the configuration of FIG. 7, the dendrogram creation unit 440, the peak search unit 450, the qualitative analysis unit 460, and the quantitative analysis unit 470 are optional functional units, and the configuration may be such that one or more of them are not required.

[0070] [User Interface] When the parameters of the processing device 400 are set by a user, it is preferable to use a user interface (UI) function that allows various settings to be made by, for example, operating a mouse or a keyboard. In addition, it is preferable that the functions of the processing device 400 are configured to cooperate with the functions of other devices. In the following, an example of a UI for setting the parameters of the processing device 400 and a UI when the functions of the processing device 400 cooperate with the functions of other devices will be described. It is assumed that the functions of the processing device 400 are implemented as software.

[0071] Fig. 8(a) is a schematic diagram showing an example of a UI for calling up a cluster analysis function. Fig. 8(b) is a schematic diagram showing an example of a UI for setting the cluster analysis function, etc. The cluster analysis function in Fig. 8(b) includes the function of the processing device 400. Pressing the cluster analysis button on the screen in Fig. 8(a) opens the screen in Fig. 8(b).

[0072] FIG. 9 is a schematic diagram illustrating some of the functions of the UI of cluster analysis (UI of FIG. 8(b)). On the screen of FIG. 11, the user can give instructions to the processing device 400, such as obtaining a measurement profile, setting parameters, performing nonnegative matrix factorization, inputting compound information, outputting the results of nonnegative matrix factorization, and transferring data. The parameter setting panel can set the optimization method of nonnegative matrix factorization, the value of hyperparameters, the number of iterations, regularization, and the presence or absence of known information. The value of the hyperparameters may be automatically estimated and set using the Akaike Information Criterion, the Bayes Information Criterion, or the like, or the estimated value may be displayed by the Estimate button. The data transfer button transfers the data of the result of nonnegative matrix factorization to the search match and quantification functions. FIG. 10 is a schematic diagram illustrating an example of a dialog for setting known information. The content of the known information can be set on the dialog of FIG. 10.

[0073] Fig. 11 is a schematic diagram showing an example of a UI for setting up dendrogram creation, etc. On the screen of Fig. 11, the user can give instructions to the processing device 400, such as setting statistics for creating a dendrogram, setting data processing, and selecting clusters.

[0074] Fig. 12 is a schematic diagram showing an example of a UI for setting search-match and quantification functions, etc. Pressing the data transfer button on the screen of Fig. 9 opens the screen of Fig. 12. On the screen of Fig. 12, the user can give instructions to the processing device 400 or other devices to execute search-match, set compound information, etc.

[0075] Note that the setting items and the like displayed in Figures 8 to 12 are examples, and even when the user sets these, it is possible to set only some of them, or to set all of them. Also, there may be setting items and functions that are not displayed in Figures 8 to 12.

[0076] [Measurement method] A sample S is placed in the X-ray diffraction device 200, and the goniometer is driven under predetermined conditions under the control of the control device 300. In addition, X-rays are irradiated onto the sample, and diffracted X-rays generated from the sample are detected. As a result, diffraction data is acquired. The X-ray diffraction device 200 transmits device information and the acquired diffraction data to the control device 300 as measurement data.

[0077] [Disassembly method] (Explanation of the flow when only non-negative matrix factorization is used) FIG. 13 is a flowchart showing an example of the operation of the processing device 400. FIG. 13 shows an example of the operation when only nonnegative matrix factorization is performed. First, the processing device 400 acquires a measurement profile (step S1). Next, known information of the acquired measurement profile is acquired (step S2). The known information may be acquired from the X-ray diffraction device 200 or the control device 300, or may be acquired by user input. Since the known information is associated with the measurement profile, it may be acquired simultaneously with the acquisition of the measurement profile. Since the known information common to a plurality of measurement profiles used in nonnegative matrix factorization may not be determined when the known information is acquired, the known information to be used may be determined from the known information after it is acquired.

[0078] Next, parameters are set (step S3). The parameters to be set are the number of basis vectors to be decomposed (the value of the hyperparameter), parameters required for optimization such as the optimization method, etc. The parameters may be set by inputting them to the user, or may be determined and set by the processing device 400 from the measurement profile or known information. Next, nonnegative matrix factorization is performed (step S4). Nonnegative matrix factorization is performed by creating a matrix consisting of the measurement profile, setting the known information to the basis matrix or coefficient matrix, and optimizing the basis matrix and coefficient matrix other than the known information. Then, the results are output as necessary (step S5), and the process ends. The results may be configured to be only stored and output when instructed by the user. In this way, the measurement profile of X-ray powder diffraction can be subjected to nonnegative matrix factorization based on the known information.

[0079] In the acquisition of known information in step S2 of the flowchart in FIG. 13, if the acquired measurement profile does not include known information or if there is no input by the user, the subsequent processing may be performed without acquiring the known information. That is, the known information may be confirmed in the acquisition of the known information, and depending on the presence or absence of the known information, normal nonnegative matrix factorization or constrained nonnegative matrix factorization based on the known information may be selectively performed. In addition, when it is confirmed that there is known information, depending on the content of the known information, it may be determined whether to impose a constraint on the basis matrix or on the coefficient matrix, or both. In this way, normal nonnegative matrix factorization or constrained nonnegative matrix factorization based on known information may be selectively performed on the measurement profile of X-ray powder diffraction depending on the presence or absence of the known information.

[0080] (Explanation of the flow for creating a dendrogram) Fig. 14 is a flowchart showing a modified example of the operation of the processing device 400. Fig. 14 shows an example of the operation when creating a dendrogram. In the following explanation of the flowchart, characteristic operations will be explained in detail, and explanations of operations that have already been explained may be omitted. First, the processing device 400 acquires a measurement profile (step T1). Next, known information of the acquired measurement profile is acquired (step T2).

[0081] Next, a dendrogram is created (step T3). The dendrogram is created by calculating statistics between the multiple acquired measurement profiles. Next, clusters are selected (step T4). The clusters may be selected by the user or the processing device. Decomposition accuracy is improved by selecting clusters that are groups of similar profiles from the dendrogram. Since known information common to multiple measurement profiles (clusters) used in non-negative matrix factorization may not be determined when the known information is acquired, the known information to be used may be determined from the known information acquired after cluster selection. Also, the known information may be acquired after cluster selection.

[0082] Next, the parameters are set (step T5). Next, nonnegative matrix factorization is performed (step T6). Then, the results are output as necessary (step T7) and the process ends. In this way, after creating a dendrogram and selecting clusters, nonnegative matrix factorization can be performed on the data measured by the X-ray diffraction device.

[0083] (Explanation of the flow of the variant when qualitative analysis or further quantitative analysis is performed) Fig. 15 is a flowchart showing a modified example of the operation of the processing device 400. Fig. 15 shows a modified example of the operation when performing qualitative analysis or further quantitative analysis after nonnegative matrix factorization. From the step of acquiring a measurement profile (step U1) to the step of performing nonnegative matrix factorization (step U4), the steps are the same as steps S1 to S4.

[0084] Next, a peak search is performed (step U5). The peak search is performed on one selected profile from the profiles (basis vectors) resulting from the nonnegative matrix factorization. The profile may be selected by the user or by the processing device 400. In addition, a dI list is created for each profile for which a peak search has been performed. It is preferable to perform the peak search on all profiles other than the profile determined to be the background.

[0085] Next, a qualitative analysis is performed (step U6). The qualitative analysis can be performed by performing a search and match based on the created dI list. If the search and match does not find a profile with a degree of match equal to or greater than a predetermined value, the process may return to step U3 and perform non-negative matrix factorization again from the parameter settings.

[0086] Next, quantitative analysis is performed (step U7). In quantitative analysis, the content of the substance identified by the qualitative analysis is determined using various methods. For example, the DD (Direct Derivation) method, the RIR method, the Rietveld method, etc. can be used. Then, the results are output as necessary (step U8) and the process ends. In this way, the measurement profile of the X-ray powder diffraction is subjected to non-negative matrix factorization based on known information, and a qualitative analysis is performed, which can be used to perform a quantitative analysis.

[0087] 15, after the qualitative analysis in step U6, the results may be output as necessary (step U8) without performing quantitative analysis, and the process may be terminated. In this way, the measurement profile of X-ray powder diffraction can be subjected to non-negative matrix factorization based on known information, and the result can be used to perform qualitative analysis.

[0088] In the flowchart of FIG. 15, a flow of creating a dendrogram and selecting clusters may be added after acquisition of a measurement profile in step U1 or acquisition of known information in step U2.

[0089] The order of steps in each of the above-mentioned flowcharts is not fixed, and the order may be changed or the steps may be processed in parallel as long as they can be processed correctly. Also, each flowchart may be applied in combination with other flowcharts.

[0090] [Example] Using the system 100 configured as described above, X-ray diffraction data was measured for a mixture of indomethacin. The shape of the background profile was given as known information for the measured profile, and nonnegative matrix factorization was performed. As a result of setting the hyperparameters other than the known information to 3, the indomethacin α-type profile, indomethacin γ-type profile, amorphous profile, and background profile could be appropriately resolved, and qualitative analysis could be performed.

[0091] The same measurement profile was subjected to non-negative matrix factorization using the conventional method. As a result, it was not possible to properly resolve the indomethacin α-form, indomethacin γ-form, amorphous profile, and background profile. This is believed to be because the profiles originating from the background and amorphous components are largely overlapped with the crystalline phase profile. It was confirmed that the method of the present invention can extract the profiles originating from the background and amorphous components even in such cases.

[0092] From the above results, it was confirmed that the processing device, system, method, and program of the present invention can perform non-negative matrix factorization of a measured profile of X-ray powder diffraction based on known information. [Explanation of symbols]

[0093] 100 Systems 200 X-ray Diffraction Instrument 210 X-ray Generator 220 Incident optical unit 230 Goniometer 240 Sample stage 250 Output optical unit 260 Detector 300 Control device 310 Control section 320 Device information storage unit 330 Measurement data storage unit 340 Display section 400 Processing Equipment 410 Measurement profile acquisition unit 420 Known Information Acquisition Department 430 Disassembly section 440 Dendrogram Creation Department 450 Peak Search Section 460 Qualitative Department 470 Quantification section 510 Input Device 520 Display device

Claims

1. A processing device for performing non-negative matrix factorization on a measured profile of X-ray powder diffraction, comprising: a measurement profile acquisition unit for acquiring one or more of the measurement profiles; a known information acquiring unit that acquires known information including a background included in the measurement profile or a shape of a predetermined profile corresponding to a predetermined substance, or a restriction of a coefficient matrix of the predetermined profile; a decomposition unit that performs non-negative matrix factorization on the measurement profile based on the known information; A processing device comprising:

2. 2 . The processing device according to claim 1 , wherein the decomposition unit selectively performs normal nonnegative matrix factorization or constrained nonnegative matrix factorization based on the known information, depending on whether the known information is present.

3. 2. The processing apparatus according to claim 1, wherein the known information is information that values ​​of the coefficient matrix of the predetermined profile commonly included in the plurality of measurement profiles are equal to each other.

4. 2. The processing apparatus according to claim 1, wherein the information on the shape of the predetermined profile is information obtained from a database or information based on actually measured data.

5. A dendrogram creation unit is further provided that calculates statistics between the plurality of measurement profiles and creates a dendrogram, The processing device according to claim 1 , wherein the decomposition unit performs non-negative matrix factorization on clusters including similar profiles selected by the processing device or a user from the dendrogram.

6. a peak search unit that performs a peak search on the profile after the nonnegative matrix factorization and creates a d-I list; 2. The processing apparatus according to claim 1, further comprising: a qualitative analysis unit that performs a qualitative analysis using the dI list.

7. 7. The processing apparatus according to claim 6, further comprising a quantification unit that performs a quantitative analysis using the qualitatively analyzed data.

8. An X-ray diffraction apparatus including an X-ray generating unit that generates X-rays, a detector that detects the X-rays, and a goniometer that controls the rotation of a sample; A system comprising: a processing device according to any one of claims 1 to 7.

9. 1. A method for performing non-negative matrix factorization of a measured X-ray powder diffraction profile, comprising the steps of: obtaining one or more of said measurement profiles; Obtaining known information including a shape of a predetermined profile corresponding to a background or a predetermined substance included in the measurement profile, or a restriction of a coefficient matrix of the predetermined profile; performing a non-negative matrix factorization on the measured profile based on the known information; The method according to claim 1, further comprising:

10. A program for performing non-negative matrix factorization on a measured profile of X-ray powder diffraction, comprising: obtaining one or more of said measurement profiles; A process of acquiring known information including a shape of a predetermined profile corresponding to a background or a predetermined substance included in the measurement profile, or a restriction of a coefficient matrix of the predetermined profile; and performing non-negative matrix factorization on the measurement profile based on the known information.

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