Training Support Method

A training support method includes: acquiring a first chromatogram; displaying a first chromatogram image; acquiring a second chromatogram identical or similar to the first chromatogram and second peak information specifying one or more peaks of the second chromatogram from a chromatogram DB; displaying a second chromatogram image and a second peak information image; receiving input, by a user, of first peak information specifying one or more peaks of the first chromatogram; and training an estimation model based on the first chromatogram and the first peak information.

BACKGROUND OF THE INVENTION

Field of the Invention

The present disclosure relates to a training support method.

Description of the Background Art

WO 2017/040487 discloses a chromatographic system. This chromatograph system detects a peak by artificial intelligence (AI) using an estimation model, and performs qualitative analysis or quantitative analysis of a sample based on the peak.

WO 2017/040487 discloses a technique in which a user can input training data in order to train an estimation model. Specifically, the technique in which the user visually checks an unseparated chromatogram in which peaks are not separated, and inputs information about designation of the peak to a chromatograph system as the training data is disclosed.

SUMMARY OF THE INVENTION

Sometimes annotation of a large amount of training data is required to prepare the estimation model, and it is desired that variation of the annotation is prevented to improve quality of the estimation model.

The present disclosure has been made to solve such a problem, and an object of the present disclosure is to improve the quality of the estimation model by preventing variations in annotations.

A training support method of the present disclosure is a method for causing a computer to execute processing for assisting a training operation of an estimation model used to detect a peak of a signal waveform acquired by an analysis device. The training support method includes acquiring a first signal waveform output by an analysis device. The training support method includes displaying the first signal waveform on a display device. The training support method includes acquiring a second signal waveform having a high similarity degree with the first signal waveform and second peak information specifying one or more peaks of the second signal waveform from a storage device that stores a plurality of annotated signals. The training support method includes displaying, on the display device, the second signal waveform and a second peak information image indicating second peak information. The training support method includes receiving input, by a user, of first peak information specifying one or more peaks of a first signal waveform. The training support method includes training an estimation model based on the first signal waveform and the first peak information.

A training support program of the present disclosure is a program for causing a computer to execute processing for supporting a training operation of an estimation model used to detect a peak of a signal waveform acquired by an analysis device. The training support program causes the computer to acquire a first signal waveform output by an analysis device. The training support program causes the computer to display the first signal waveform on a display device. The training support program causes the computer to acquire a second signal waveform having a high similarity degree to the first signal waveform and second peak information specifying one or more peaks of the second signal waveform from a storage device that stores a plurality of annotated signals. The training support program causes the computer to display, on the display device, the second signal waveform and a second peak information image indicating the second peak information. The training support program causes the computer to receive input, by a user, of first peak information specifying one or more peaks of the first signal waveform. The training support program causes the computer to train the estimation model based on the first signal waveform and the first peak information.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the drawings, the same or corresponding part is denoted by the same reference numeral, and the description thereof will not be repeated.

First Embodiment

The present disclosure relates to a technique for supporting training of an estimation model used to detect a peak of a signal waveform output by an analysis device. Examples of the analysis device include a gas chromatograph (GC) device, a liquid chromatography (LC) device, a mass spectrometer, a spectrophotometer, and an X-ray analyzer.

For example, the signal waveform may be a chromatogram waveform or a mass spectrum waveform. When the analysis device is the spectrophotometer, the signal waveform is an absorption spectrum waveform. When the analysis device is the X-ray analyzer, the signal waveform is an X-ray spectrum waveform.

Furthermore, training (training processing) of an estimation model (estimation model121described later) includes processing for newly generating (constructing) an unconstructed estimation model and processing for updating an already constructed estimation model. “Updating the estimation model” includes processing for updating a parameter of the estimation model. Furthermore, the estimation model updated (optimized) by the training processing is also referred to as a “trained model”. The pre-trained estimation model and the trained estimation model are collectively referred to as an “estimation model”.

In the first embodiment, the analysis device in which the liquid chromatograph is adopted will be described.FIG.1is a view illustrating a configuration example of an analysis system100. Analysis system100includes an analysis device10, a data analysis device25, an input device61, a display device62, and a training support device30. For example, data analysis device25and training support device30is configured of an information processing device (for example, a personal computer (PC)). Data analysis device25and training support device30are individually illustrated in the example ofFIG.1, but may be integrated.

Input device61is a pointing device such as a keyboard or a mouse, and receives an instruction from a user. For example, display device62includes a liquid crystal display (LCD) panel. Display device62displays various images. When a touch panel is used as a user interface, input device61and display device62are integrally formed. Input device61is connected to data analysis device25and training support device30. Display device62is connected to data analysis device25and training support device30.

Data analysis device25includes a controller20. Controller20controls analysis device10. Analysis device10includes a mobile phase container11, a pump12, an injector13, a column14, and a detector15. Mobile phase container11stores a mobile phase. Pump12sucks the mobile phase stored in mobile phase container11and feeds the mobile phase to column14at a substantially constant flow speed (or flow rate).

Injector13injects a prescribed amount of sample solution into the mobile phase at prescribed timing according to an instruction from controller20. The injected sample solution is introduced into column14along the flow of the mobile phase. Various compounds contained in the sample solution are separated and eluted in a time direction while passing through column14. That is, column14separates compounds contained in the sample liquid according to a retention time.

Detector15detects compounds in an eluent eluted from column14. Detector outputs a detection signal having intensity corresponding to a compound amount to data analysis device25. For example, an optical detector or the like adopting a photodiode array (PDA) detector or the like is used as detector15.

In addition to controller20, data analysis device25includes a data collection unit110, a peak detection processing unit111, and an analysis unit117.

Data collection unit110samples the detection signal output from detector15at prescribed time intervals, and converts the detection signal into digital data. Data collection unit110stores the digital data in a prescribed storage region (not illustrated). The digital data is data (hereinafter, also referred to as “chromatogram data”) indicating the chromatogram waveform.

Peak detection processing unit111estimates (derives) the peak of the chromatogram by the chromatogram data collected by data collection unit110using artificial intelligence (AI).

In the first embodiment, peak detection processing unit111includes a model storage114and a peak determination unit116. For example, model storage114stores an estimation model121(neural network) generated by machine learning. For example, estimation model121is expressed by a prescribed function. For example, the prescribed function is an exponentially modified gaussian (EMG) function.

Peak determination unit116inputs the chromatogram based on the chromatogram data collected by data collection unit110to estimation model121. Estimation model121outputs the peak of the chromatogram. As described above, peak detection processing unit111estimates the peak of the chromatogram by the chromatogram data collected by data collection unit110, and outputs the peak to analysis unit117.

The time at which the peak is observed (retention time) corresponds to the type of the compound. The chromatogram is transmitted to the data analysis device. The data analysis device specifies the compound from the retention time of the peak included in the chromatogram. This identification is also referred to as “qualitative analysis”.

A height of the peak and an area of the peak in the chromatogram correspond to a concentration or a content of the compound in the sample. The data analysis device specifies the concentration and content of the compound of the sample from the height or area value of the peak included in the chromatogram. This identification is also referred to as “quantitative analysis”.

In the peak output from peak determination unit116, analysis unit117obtains a position (time) of the peak top of the peak and an area value (or height) of the peak. Analysis unit117specifies the compound from information about the position of each peak on the chromatogram. In addition, analysis unit117calculates the content of each compound from the peak area value (or the height value) using a previously-prepared calibration curve. In this manner, analysis unit117executes qualitative analysis and quantitative analysis of each compound contained in the sample. Analysis unit117displays a qualitative analysis result and a quantitative analysis result on display device62.

Training support device30will be described below. As described above, in order to improve accuracy of peak detection by peak detection processing unit111, training support device30optimizes estimation model121. Furthermore, in the first embodiment, a manufacturer may optimize estimation model121at a manufacturing stage of analysis system100. Furthermore, analysis system100may be shipped to a user, and the user may optimize estimation model121. In this case, the user prepares training data optimizing estimation model121, and the user himself/herself executes annotation processing. Accordingly, the user can generate estimation model121desired by the user.

In general, performance of machine-learned estimation model121is not perfect, and is operated on an assumption that some errors is generated in peak detection. As described above, in the first embodiment, the user himself/herself can train estimation model121, so that convenience of the user can be improved.

In general, the performance of estimation model121greatly depends on quality of the training data. In particular, preferably various chromatograms are covered and that an accurate training label is given to the chromatogram.

In the first embodiment, two techniques exist as a technique for optimizing estimation model121by the user. The first technique is a technique in which the user performs correction work of the peak detected by peak detection processing unit111. Specifically, analysis system100displays a chromatogram image of the chromatogram and a peak information image of peak information given to the chromatogram on display device62. The peak information image corresponds to a “detected peak image” described later. The chromatogram image is an image illustrating the chromatogram. The peak information image is an image indicating the peak information. The peak information is information specifying the peak of the chromatogram. Analysis system100receives correction of the displayed peak information by the user.

The second technique is a technique in which the user performs peak designation work on the chromatogram (chromatogram in which the peak is not detected) newly collected by data collection unit110. Specifically, analysis system100displays the chromatogram image on display device62and does not display the peak information image. Then, analysis system100receives input of the peak information with respect to the displayed chromatogram image. In addition, the input peak information is a label or training data that optimizes estimation model121.

As described above, in both the first technique and the second technique, analysis system100receives the input of the peak information from the user as the training data. As a result of the user inputting the peak information, the parameters of estimation model121are updated using the input peak and chromatogram as new training data. Peak detection processing unit111can use updated estimation model121. However, when the user performs the annotation that is not consistent with the past annotation (variation is generated), sometimes the accuracy of estimation model121decreases.

In addition, sometimes the preparation of estimation model121requires the annotation of a large amount of training data.FIG.2is a view illustrating an example in which different training data is assigned to the same chromatogram (inconsistent annotation is executed).FIG.2(A)is a view illustrating that the peak of the chromatogram is widely designated by the user.FIG.2(B)is a view illustrating that the peak of the chromatogram is narrowly designated by the user. InFIG.2and the drawings illustrating the chromatograms described later, a horizontal axis represents time, and a vertical axis represents signal intensity. Furthermore, in the first embodiment, the peak information (training data) input by the user is peak information92A and peak information92B.

For example, it is assumed that the user inputs peak information92A (seeFIG.2(A)) for first-time chromatogram, and the user inputs peak information92B (seeFIG.2(B)) for second-time chromatogram. In this case, different pieces of peak information (training data) are input to the same chromatogram, namely, the annotation varies. When the variation of the annotation is generated in this way, the quality of estimation model121is sometimes degraded.

Accordingly, training support device30of the first embodiment encourages the user to input the peak information so as to prevent variation in annotations (so as to have consistency with past peak information). Thus, training support device30can support training of estimation model121by the user.

[Hardware Configuration of Training Support Device]

FIG.3is a block diagram illustrating a hardware configuration example of training support device30of the first embodiment. As illustrated inFIG.3, training support device30includes a controller51, a storage device52, a media reading device17, a display interface18, and an input interface26as a main hardware element.

Controller51updates estimation model121as described later. For example, controller51includes a central processing unit (CPU), a field programmable gate array (FPGA), and a graphics processing unit (GPU). Controller51may include at least one of the CPU, the FPGA, and the GPU, or may include the CPU and the FPGA, the FPGA and the GPU, the CPU and the GPU, or all of the CPU, the FPGA, and the GPU. Controller51may be configured by an arithmetic circuit (processing circuitry).

Storage device52includes a volatile storage region (for example, working area) that temporarily stores a program code, a work memory, and the like when controller51executes an arbitrary program. For example, storage device52is constructed with a volatile memory device such as a dynamic random access memory (DRAM) or a static random access memory (SRAM). Furthermore, storage device52includes a nonvolatile storage region. For example, storage device52includes a nonvolatile memory device such as a hard disk or a solid state drive (SSD).

In the first embodiment, the example in which the volatile storage region and the nonvolatile storage region are included in the same storage device52has been described. However, the volatile storage region and the nonvolatile storage region may be included in different storage devices. For example, controller51may include the volatile storage region, and storage device52may include the nonvolatile storage region. Training support device30may include a microcomputer including controller51and storage device52.

Storage device52stores an estimation model121, a control program122, and a chromatogram database (DB)123. Estimation model121includes a neural network and parameters used in processing in the neural network. Estimation model121is configured of a convolution neural network (CNN) or the like. Control program122is a program executed by controller51.

Estimation model121includes at least a program capable of the machine learning, and the parameter is optimized (adjusted) by performing the machine learning based on training data (training data). Training support device30transmits optimized estimation model121to data analysis device25. Data analysis device25updates estimation model121stored in model storage114to transmitted estimation model121(optimized estimation model). As described above, peak detection processing unit111can improve estimation accuracy by estimating the peak using updated estimation model121.

Medium reading device17receives recording medium130such as a removable disk, and acquires the data stored in recording medium130. For example, the data is the control program. Furthermore, control program122may be stored in recording medium130(for example, a removable disk) and distributed as a program product. Alternatively, control program122may be provided as the program product that can be downloaded by an information provider through the Internet or the like. Controller51reads the program provided by recording medium130, the Internet, or the like. Controller51stores the read program in a prescribed storage region (storage region of storage device52). Controller51executes the training support processing described later by executing stored control program122.

The recording medium130is not limited to a digital versatile disk read only memory (DVD-ROM), a compact disc read-only memory (CD-ROM), a flexible disk (FD), or a hard disk, but may be a medium that fixedly carries a program, such as a magnetic tape, a cassette tape, an optical disk (magnetic optical disc (MO)/mini disc (MD)/digital versatile disc (DVD)), an optical card, and a semiconductor memory such as a mask ROM, an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash ROM. Recording medium130is a non-transitory medium in which control program122or the like can be read by the computer.

Display interface18is an interface connecting display device62, and implements input and output of data between training support device30and display device62. Input interface26is an interface connecting input device61, and implements input and output of data between training support device30and input device61.

[Functional Block of Training Support Device]

FIG.4is a functional block diagram illustrating training support device30. As described above, training support device30includes controller51and storage device52. Controller51further includes an acquisition unit32, a processing unit34, and an output unit36.

Acquisition unit32acquires information input by the user through input device61. For example, the information is the peak information described above. In addition, data collection unit110transmits chromatogram data to training support device30every time the chromatogram is collected. Acquisition unit32acquires the chromatogram data from data collection unit110. The information (peak information and chromatogram data) acquired by acquisition unit32is output to processing unit34.

Processing unit34executes processing according to a type of information output from acquisition unit32. When the peak information is input by acquisition unit32, the parameter of estimation model121is updated based on the peak information. When the chromatogram data is input by acquisition unit32, a chromatogram DB123is updated. Processing unit34executes various other pieces of processing.

Output unit36outputs various signals or information. For example, output unit36transmits image data of an image displayed on display device62to display device62. Display device62displays the image based on the image data.

Furthermore, every time estimation model121is updated, output unit36outputs the updated estimation model to model storage114. Model storage114stores the updated estimation model.

FIG.5is a view illustrating an example of chromatogram DB123. As described above, chromatogram DB123is stored in storage device52. In chromatogram DB123, the chromatogram data, storage peak information, a feature amount of the chromatogram indicated by the chromatogram data, and an analysis result are associated with a chromatogram identification (ID). The chromatogram data and the storage peak information are also referred to as an “annotated signal”.

S chromatogram IDs (S is an integer of at least 1) are stored in chromatogram DB123. Chromatogram DB123stores the chromatogram prepared in the past by analysis device10or a device equivalent to analysis device10, the feature amount of the chromatogram, the analysis result derived from the chromatogram, and the like. In other words, at least one (or a plurality of) annotated signal is stored in chromatogram DB123.

Chromatogram ID is information identifying the chromatogram. The chromatogram data is data indicating the chromatogram, and is digital data collected by data collection unit110. The chromatogram data corresponds to the “storage signal waveform” of the present disclosure.

The storage peak information is data specifying a peak included in the chromatogram. For example, the peak information is indicated by a second peak information image253inFIG.6and the like described later. The storage peak information is information indicating the peak detected by peak detection processing unit111or peak information input by the user.

The chromatogram feature amount is the feature amount of the chromatogram indicated by the chromatogram data. In the example ofFIG.5, the chromatogram feature amount includes the number of peaks of the chromatogram, a gradient between two points, and an area value. The gradient between two points refers to a gradient of a line segment connecting a start point of the peak and an end point of the peak. The area value is an area value of a region surrounded by the line segment forming the peak of the chromatogram and the line segment by the gradient between two points. The peak indicated by the chromatogram feature amount is a peak indicated by the storage peak information.

In addition, the gradient between two points and the area value are also a feature amount (hereinafter, also referred to as a “peak feature amount”) of the peak. The peak feature amount exists for the number of peaks. For example, the chromatogram in which the chromatogram ID is C1illustrates the number of peaks of 3. E11, E12, E13are illustrated as gradients between two points of three peaks. M11, M12, M13are illustrated as area values of the three peaks.

The analysis result is a result derived based on the peak detected by peak detection processing unit111using current estimation model121as the peak of the chromatogram of the chromatogram data corresponding to the analysis result. The analysis result includes at least one of a qualitative analysis result and a quantitative analysis result. The qualitative analysis result is a result indicating the compound specified by the chromatogram. The quantitative analysis result is a result indicating the compound amount. As a modification, the analysis result may include the qualitative analysis result but may not include the quantitative analysis result.

In the example ofFIG.5, chromatogram data D1, the number of peaks G1, the gradient between two points E1, area value M1, a qualitative analysis result P1, a quantitative analysis result Q1, and storage peak information R1are associated with a chromatogram ID that is C1.

As described above, at least one storage signal waveform output by the analysis device and at least one storage peak information specifying each peak of the at least one storage signal waveform are stored in chromatogram DB123.

A technique of training support for the user by training support device30of the first embodiment will be described below. Training support device30executes the training support for the user by displaying various images in a display region62A of display device62.FIGS.6to8are views illustrating examples of the various images of the first embodiment. In the first embodiment, the image inFIG.6is displayed, then the image inFIG.7is displayed, and then the image inFIG.8is displayed.

When a prescribed operation is performed on input device61by the user, the mode of analysis system100is shifted to the training mode. In the training mode, the user inputs first peak information described later to update estimation model121.

As illustrated inFIGS.6to8, display region62A includes an editing region131A and a past result region132A adjacent to editing region131A. In other words, editing region131A and past result region132A are set to the same screen. The image (hereinafter, also referred to as a “first chromatogram image211”) of the chromatogram (hereinafter, also referred to as a “first chromatogram”) derived by analysis device10is displayed in editing region131A. The pseudo chromatogram corresponds to the “first signal waveform” of the present disclosure. First chromatogram image211corresponds to the “first waveform image” of the present disclosure. When acquiring the chromatogram data collected by data collection unit110, training support device30displays the image of the chromatogram of the chromatogram data as first chromatogram image211.

When acquiring the first chromatogram, training support device30specifies a second chromatogram same as or similar to the first chromatogram from the chromatogram DB (FIG.5) and acquires the specified second chromatogram. Here, a specific technique example of the second chromatogram that is the same as or similar to the first chromatogram will be described.

Training support device30calculates first similarity degree between each of the S pieces of stored chromatogram data stored in the chromatogram DB and the first chromatogram. That is, training support device30calculates S first similarity degrees. The first similarity degree indicates a degree of similarity between one piece of storage chromatogram data and the first chromatogram. The first similarity degree has a larger value as one piece of storage chromatogram data is more similar to the first chromatogram. In the first embodiment, the first similarity degree is expressed by %. In addition, the first similarity degree of the second chromatogram that is the same as the first chromatogram is 100%.

The first similarity degree is calculated from the following two viewpoints. As a first aspect, training support device30calculates the first similarity degree based on the feature amount (hereinafter, also referred to as a “first feature amount”) of the first chromatogram and the feature amount (hereinafter, also referred to as a “second feature amount”) of the same type as the feature amount (first feature amount) of the storage chromatogram.

In the first embodiment, the first feature amount is a feature amount derived based on the peak detected by peak detection processing unit111using current estimation model121as the peak of the first chromatogram. In the first embodiment, the feature amount is the same type as the second feature amount of the storage chromatogram inFIG.5. Specifically, the first feature amount and the second feature amount are the number of peaks, the gradient between two points, and the area value. The first feature amount and the second feature amount may be of other types as long as they are of the same type. The other type may be at least one of a peak width, a peak separation degree, a peak reading, a peak tailing, and the like.

As a second aspect, training support device30calculates the first similarity based on the analysis result indicated (derived) by the first chromatogram and the analysis result indicated by the storage chromatogram. Here, as illustrated inFIG.5, the analysis result illustrated by the storage chromatogram includes the qualitative analysis result and the quantitative analysis result corresponding to the storage chromatogram. In addition, the analysis results indicated by the first chromatogram are the qualitative analysis result and the quantitative analysis result (that is, the analysis result of the same type as the analysis result indicated by the storage chromatogram) indicated by the first chromatogram.

For example, training support device30compares the qualitative analysis result indicated by the first chromatogram with the qualitative analysis result corresponding to the storage chromatogram, and sets the first similarity to “0” when both the qualitative analysis results are different from each other. On the other hand, when both the qualitative analysis results are the same, training support device30calculates the similarity degree (hereinafter, also referred to as a “first temporary similarity degree”) between both the quantitative analysis results. Furthermore, training support device30calculates a second temporary similarity based on the first feature amount and the second feature amount. The second temporary similarity degree is calculated using, for example, the correlation coefficient. In addition, the second temporary similarity may be calculated as the similarity degree between the shape of the first chromatogram and the shape of the storage chromatogram.

Then, training support device30calculates the first similarity degree based on the first temporary similarity degree and the second temporary similarity degree. As described above, training support device30calculates S (the number of chromatogram IDs stored in chromatogram DB) first similarity degrees by combining the similarity degree (first similarity degree) calculated from the first viewpoint and the similarity degree (first similarity degree) calculated from the second viewpoint.

As a modification, training support device30may calculate the first similarity degree from either the first viewpoint or the second viewpoint.

Training support device30specifies the first similarity degree that is greater than or equal to a first threshold from the calculated S first similarity degrees. Training support device30determines the storage chromatogram that is the specified first similarity degree as the second chromatogram. At this point, the first threshold is a predetermined threshold, and for example, the first threshold is 70%. That is, training support device30can determine the chromatogram (second chromatogram) having the similarity degree greater than or equal to 70% to the first chromatogram from the viewpoint of the first feature amount and the analysis result. Furthermore, training support device30acquires the peak information (second peak information) corresponding to the chromatogram ID of the second chromatogram by referring to chromatogram DB123.

When acquiring the first chromatogram, the second chromatogram, and the second peak information, training support device30displays first chromatogram image211of the first chromatogram, a second chromatogram image212of the second chromatogram, and a second peak information image253of the second peak information as illustrated inFIG.6. In the example ofFIG.6, second chromatogram images212A,212B,212C,212D of the four second chromatograms and each second peak information image253of four second chromatogram images212are displayed. Hereinafter, four second chromatogram images212A,212B,212C,212D are also referred to as “second chromatogram images212”.

As described above, second chromatogram image212is an image of the second chromatogram having high similarity degree (greater than or equal to the first threshold) to the first chromatogram. Accordingly, the user can recognize the peak of the second chromatogram by visually recognizing second peak information image253of the chromatogram having the high similarity degree to the first chromatogram.

As illustrated inFIG.6, when first chromatogram image211is displayed, training support device30receives input of the peak information specifying the peak of the first chromatogram indicated by first chromatogram image211from the user. The peak information corresponds to the “first peak information” of the present disclosure.

FIG.7illustrates the image when the first peak information is input by the user. In the example ofFIG.7, the image indicating the input first peak information is displayed as a first peak information image220. Training support device30updates the parameter of estimation model121such that peak detection processing unit111detects the peak specified by the input first peak information as the peak of the first chromatogram.

As described above, in the first embodiment, the user can input the first peak information having consistency with the past second peak information (the second peak information displayed in past result region132A). That is, for example, the input of different pieces of the peak information despite the same chromatogram (seeFIG.2) is prevented. Then, training support device30can update estimation model121based on the first peak information. Therefore, the update accuracy of estimation model121can be improved from the viewpoint of improving the specific accuracy of the peak of the first signal waveform.

In addition, training support device30calculates the first similarity degree between the first chromatogram and each of the S storage chromatograms, and acquires the storage chromatogram in which the first similarity degree is greater than or equal to the first threshold as the second chromatogram. Then, as illustrated inFIG.6, training support device30displays first chromatogram image211, second chromatogram image212of the acquired second chromatogram, and second peak information image253.

Thereafter, as illustrated inFIG.7, training support device30receives the input of the first peak information. Accordingly, the user can input the first peak information of the first chromatogram while visually recognizing the second chromatogram same as or similar to the first chromatogram and prepared in the past and second peak information image253indicating the peak of the second chromatogram. Accordingly, training support device30can encourage the user to input the training data (first peak information) in which the variation in the annotation is prevented. As a result, training support device30can prevent the variations in the annotations to improve the quality of the estimation model.

In addition, training support device30calculates the first similarity degree based on the first feature amount of the first chromatogram and the second feature amount of the same type as the first feature amount of the storage chromatogram. Accordingly, training support device30can calculate the first similarity degree by a relatively simple arithmetic operation.

Training support device30calculates the first similarity degree based on the analysis result indicated by the first chromatogram and the analysis result indicated by the storage chromatogram. Accordingly, training support device30can calculate the first similarity degree by a relatively simple arithmetic operation.

In the examples ofFIGS.6and7, training support device30displays the first similarity degree of the second chromatogram in association with second chromatogram image212indicating the second chromatogram. In the examples ofFIGS.6and7, a similarity degree image271indicating the first similarity degree is displayed. In the examples ofFIGS.6and7, for example, an image of “98%” is displayed as similarity degree image271in association with second chromatogram image212A. Accordingly, the user can visually recognize the first similarity degree of the second chromatogram.

Furthermore, training support device30displays a second feature amount image275in association with second chromatogram image212in past result region132A. Here, second feature amount image275indicates the feature amount of the peak specified by the second peak information indicated by second peak information image253. Second feature amount image275includes an image272indicating the gradient between two points and an image273indicating the area value. The gradient between two points and the area value are feature quantities of each of at least one peak included in the second chromatogram. In the examples ofFIGS.6and7, the chromatogram of second chromatogram image212A includes three peaks. In association with second chromatogram image212A, A1, A2, S3that are 2-point gradients of the three peaks are displayed, and B1, B2, B3that are the area values of the three peaks are displayed.

In the first embodiment, the second chromatogram includes a high similarity chromatogram and a low similarity chromatogram having the first similarity degree lower than that of the high similarity chromatogram. For example, in the example ofFIG.7, second chromatogram image212A of the chromatogram having the first similarity degree of 98% is displayed as an example of the high similarity chromatogram. In addition, a second chromatogram image212D of a chromatogram having the first similarity degree of 80% is displayed as an example of the low similarity chromatogram. Furthermore, in past result region132A, second chromatogram image212A indicating the highly similar chromatogram is displayed in preference to a chromatogram image212B indicating the low similar chromatogram. In this manner, training support device30displays second chromatogram image212and second peak information image253with priority according to the first similarity degree. In the examples ofFIGS.6and7, the highly similar chromatogram is displayed above the low similar chromatogram.

Accordingly, the user can visually recognize the second chromatogram having the high similarity degree to the first chromatogram in preference to the second chromatogram having the low similarity degree. Therefore, training support device30can easily visually recognize the second peak information of the second chromatogram having the high similarity degree to the first chromatogram.

In the example ofFIG.7, second peak information image253is a linear image (hereinafter, also referred to as a “line image”). The region surrounded by second chromatogram image212and second peak information image253is a region indicating the peak of the second chromatogram.

In addition, training support device30receives the input (designation) of a first point201and a second point202by the user while first chromatogram image211, second chromatogram image212, and second peak information image253are displayed. In the first embodiment, a cursor217of input device61(mouse) is displayed. The user can specify first point201by positioning cursor217at a desired position and clicking the mouse. In addition, the user can designate second point202by positioning cursor217at another desired position and clicking the mouse. When first point201and second point202are designated, training support device30displays the line image connecting first point201and second point202as first peak information image220. Then, training support device30recognizes the region surrounded by the line connecting first point201and second point202and the line included in first chromatogram image211as the peak region (training data) specified by the first peak information of first peak information image220. According to such the configuration, the first peak information can be input by designating first point201and second point202of first chromatogram image211. Accordingly, the user can intuitively input the first peak information, so that the convenience of the user can be improved. The line image connecting first point201to second point202inFIG.7is also referred to as “baseline of peak.”

FIG.8is a view illustrating a screen after the first peak information is input by the user. As illustrated inFIG.8, when the first peak information is input, training support device30calculates the first feature amount and displays a first feature amount image231of the first feature amount. Here, first feature amount image231indicates the feature amount of the peak specified by the first peak information indicated by first peak information image220. In the example ofFIG.8, first feature amount image231is an image illustrating the gradient between two points of the peak and the area value of the peak. In the example ofFIG.8, “X1” is displayed as the value of the gradient between two points, and “Y1” is displayed as the area value.

As described above, as illustrated inFIGS.6to8, training support device30displays second feature amount image275in past result region132A. In this manner, training support device30displays first feature amount image231and second feature amount image275. Accordingly, after inputting the first peak information, the user can compare the peak indicated by the first peak information with the past peak from the viewpoint of the feature amount (In the first embodiment, the gradient between two points and the area value).

In addition, first feature amount image231is displayed by the number of peaks specified by the first peak information input by the user. For example, when the number of peaks specified by the first peak information input by the user is “3”, first feature amount images231of the three peaks are displayed.

FIG.9is a flowchart illustrating the processing of training support device30. In step S2, training support device30acquires the first chromatogram. Subsequently, in step S4, training support device30acquires the second chromatogram and the second peak information. Step S4includes step S42, step S44executed after step S42, step S46executed after step S44, step S48executed after step S46, and step S50executed after step S48.

In step S42, training support device30extracts the first feature amount of the first chromatogram and the second feature amount of the storage chromatogram. As described above, the first feature amount and the second feature amount are the number of peaks, the gradient between two points, and the area value.

In step S44, training support device30extracts the first analysis result indicated by the first chromatogram and the second analysis result indicated by the storage chromatogram. The first analysis result and the second analysis result are the qualitative analysis result and the quantitative analysis result.

In step S46, training support device30calculates the first similarity degree between the first chromatogram and the storage chromatogram. Training support device30calculates the first similarity degree based on the first feature amount, the second feature amount, the first analysis result, and the second analysis result.

In step S48, training support device30refers to chromatogram DB123to acquire the storage chromatogram having the first similarity degree equal to or greater than the first threshold as the second chromatogram.

In step S50, training support device30refers to chromatogram DB123to acquire the storage peak information corresponding to the second chromatogram as the second peak information.

When the processing in step S4is ended, in step S6, training support device30displays first chromatogram image211of the first chromatogram acquired in step S2.

Subsequently, in step S8, training support device30displays second chromatogram image212and second peak information image253. Step S8includes step S82and step S84executed after step S82.

In step S82, training support device30displays second chromatogram image212and second peak information image253with the priority according to the first similarity degree. In step S84, similarity degree image271and second feature amount image275are displayed.

In this way, the image inFIG.6is displayed by executing the pieces of processing of steps S2to S8.

Subsequently, in step S10, training support device30determines whether the first peak information is input by the user. Step S10includes the processing of step S102. In step S102, training support device30determines whether first point201and second point202are input by the user. Training support device30repeats the processing of step S102until first point201and second point202are input. When the affirmative determination is made in step S102, the processing proceeds to step S12.

In step S12, training support device30displays the first peak information image of the first peak information determined to be input in step S10. Step S12includes step S122. In step S122, training support device30displays first peak information image220and first feature amount image231. In step S122, training support device displays first peak information image220to display the image inFIG.7. In step S122, training support device30displays first feature amount image231to display the image inFIG.8.

Subsequently, in step S13, training support device30determines whether an end operation is executed by the user. The end operation is an operation performed by the user on input device61. For example, the end operation is a user operation on an end button (not illustrated) displayed on the screens inFIGS.6to8.

The user can input at least one piece of first peak information to first chromatogram image211. When the input of the first peak information ends, the user executes the end operation. When the negative determination is made in step S13, the processing returns to step S102. On the other hand, when the affirmative determination is made in step S13, the processing proceeds to step S14.

In step S14, training support device30trains the estimation model121on the basis of the first chromatogram acquired in step S2and the first peak information determined to be input in step S10. Furthermore, estimation model121may be trained in a plurality of sets using the chromatogram and the peak information as a set.

As illustrated inFIG.9, training support device30receives the input of the first peak information by the user after steps S6and S8. As described above, step S6is processing for displaying first chromatogram image211on display device62. Step S8is processing for displaying second chromatogram image212and second peak information image253on display device62.

With the configuration inFIG.9, the user can input the first peak information about the first chromatogram while visually recognizing the second chromatogram similar to the first chromatogram and prepared in the past and the second peak information indicating the peak of the second chromatogram. Accordingly, the convenience of the user can be improved with respect to the input of the first peak information.

Second Embodiment

FIG.10is a flowchart illustrating processing of training support device30according to a second embodiment. InFIG.10, when the processing of step S2ends, training support device30executes the processing of step S6. Subsequently, training support device30executes the processing of step S10. At this point, the user inputs the first peak information while first chromatogram image211is displayed but second chromatogram image212and second peak information image253are not displayed. When the processing of step S10is executed, the pieces of processing of steps S12and S13is executed. The affirmative determination is made in step S13. The processing proceeds to step S4A. Step S4A is different from step S4in that step S48is replaced with step S52and step S54.

In step S52, training support device30calculates S similarity degrees (hereinafter, also referred to as the “second similarity degree”) between the first peak information input in step S10and the S pieces of storage peak information R (seeFIG.5) stored in chromatogram DB123. The second similarity degree indicates the degree of similarity between one piece of storage peak information and the first peak information. The second similarity degree has a larger value as one piece of storage peak information is more similar to the first peak information. For example, training support device30calculates the parameter (for example, the correlation coefficient) regarding each of the S pieces of storage peak information and the first peak information as the second similarity.

Subsequently, in step S54, training support device30acquires the storage chromatogram having the peak specified by the storage peak information, in which the first similarity degree is greater than or equal to the first threshold and the second similarity degree is greater than or equal to the second threshold, as the second chromatogram. At this point, the second threshold is a predetermined threshold. That is, the second chromatogram in which the first peak information and the chromatogram are similar to each other is acquired in step S54. Subsequently, the second peak information corresponding to the second chromatogram acquired in step S54is acquired in step S50.

In the example ofFIG.10, training support device30executes the processing of step4A after step S6and step S10. Step S6is processing for displaying first chromatogram image211on display device62. Step S10is processing for receiving the input of the first peak information by the user. Step4A is processing for acquiring the second chromatogram and the second peak information from chromatogram DB123. Step S4A includes step S46, step S52, and step S54.

Step S52is processing for calculating the first similarity, and step S54is processing for calculating the second similarity degree. Step S56is processing for acquiring the storage chromatogram having the peak specified by the storage peak information, in which the first similarity degree is greater than or equal to the first threshold and the second similarity degree is greater than or equal to the second threshold, as the second chromatogram.

According to the second embodiment, training support device30can display the storage chromatogram having the peak specified by the storage peak information, in which the first similarity degree is greater than or equal to the first threshold and the second similarity degree is greater than or equal to the second threshold, as the second chromatogram. Consequently, the user can check such the second chromatogram.

Third Embodiment

FIG.11is a flowchart illustrating processing of training support device30according to a third embodiment. InFIG.11, after the processing of step S6inFIG.9, the processing of step S150is executed. The processing of step S150is processing for displaying the detected peak image. The detected peak image is peak information (hereinafter, also referred to as “detected peak information”) specifying the peak of the first chromatogram (the first chromatogram acquired in step S2) detected using current estimation model121. That is, temporary peak information of the first chromatogram acquired in step S2is displayed as the detected peak image. As described above, the first chromatogram image and the detected peak image (temporary peak image of the first chromatogram) are displayed by executing the pieces of processing of steps S6and S150. Thereafter, the pieces of processing of steps S8and S10are executed.

According to the third embodiment, the user can input the first peak information while referring to the detected peak information image. Furthermore, the user can input the first peak information with reference to the detected peak information image and the second peak information image. Accordingly, the convenience of the user can be improved.

When the user determines that the first peak information image remains the detected peak image in step S102(step S10), the user executes a predetermined operation (for example, an operation of pressing an OB button (not illustrated)), so that the affirmative determination is made in step S102and the processing proceeds to next step S122. Furthermore, in step S102(step S10), when the user desires to correct the detected peak image, the first peak information is newly input (first point201and second point202are designated), so that the affirmative determination is made in step S102and the processing proceeds to next step S122.

(1) In the processing ofFIG.11, training support device30may execute the processing in the order of step S2, step S6, step S150, step S4A (seeFIG.10), step S8, step S10, and step S12. In step S52of step S4A, S second similarity degrees between the detected peak information and the S storage chromatograms are calculated. Accordingly, in step S54, training support device30acquires the chromatogram in which the first similarity degree is greater than or equal to the first threshold and the storage chromatogram in which the peak is specified by the storage peak information, in which the second similarity degree is greater than or equal to the second threshold, as the second chromatogram. That is, the first chromatogram to which the detected peak information is added and the second chromatogram image having the same or similar chromatogram and peak information are displayed. Training support device can improve the user convenience by displaying such the second chromatogram image.

(2) In the above-described embodiment, the configuration in which the user inputs the first peak information by designating first point201and second point202has been described. However, the technique of inputting the first peak information may be another technique. For example, a coordinates specifying the peak desired by the user may be input to displayed first chromatogram image211.

(3) In the example ofFIG.7, the configuration in which the user inputs “baseline of peak (first peak information image220)” by designating first point201and second point202has been described. In some cases, however, training support device30may not be able to specify a peak based on its baseline.

FIG.12illustrates an example of first chromatogram image211for which training support device30cannot specify a peak based on its baseline. On first chromatogram image211ofFIG.12, a peak Pa, a peak Pb, and a peak Pc are shown. In the example ofFIG.12, peak Pb and peak Pc are contiguous to each other, and a start point Pb1of peak Pb is indicated while the end point of peak Pb is not indicated (see S inFIG.12). In some cases, a peak having its start point and end point that both are not indicated may also be displayed (not shown). Such a peak where at least one of its start point and its end point is not indicated may be specified by a perpendicular line.

The perpendicular line is a line that is perpendicular or substantially perpendicular to the horizontal axis (time axis) of first chromatogram image211. In the example ofFIG.12, first point201and second point202are designated by a user, and accordingly the perpendicular line is displayed as first peak information image220. Thus, first peak information image220may include at least one of the baseline shown inFIG.7and the perpendicular line shown inFIG.12.

In the example ofFIG.12, while details of past result region132A are not shown, similarity degree image271, second feature amount image275, and second peak information image253such as perpendicular line, for example, are displayed.

It is understood by those skilled in the art that the plurality of embodiments described above are specific examples of the following aspects.

(Clause 1) A training support method according to one aspect is a method for causing a computer to execute processing for supporting a training operation of an estimation model used to detect a peak of a signal waveform acquired by an analysis device. The training support method includes acquiring a first signal waveform output by an analysis device. The training support method includes displaying the first signal waveform on a display device. The training support method includes acquiring a second signal waveform having a high similarity degree with the first signal waveform and second peak information specifying one or more peaks of the second signal waveform from a storage device that stores a plurality of annotated signals. The training support method includes displaying, on the display device, the second signal waveform and a second peak information image indicating second peak information. The training support method includes receiving input, by a user, of first peak information specifying one or more peaks of the first signal waveform. The training support method includes training an estimation model based on the first signal waveform and the first peak information.

According to such the configuration, the user can input the first peak information having consistency with the past second peak information, and can update the estimation model based on the first peak information. Consequently, the user is encouraged to input the training data in which the variation in the annotation is prevented. As a result, the quality of the estimation model can be improved while preventing the variation of the annotation.

(Clause 2) In the training support method described in clause 1, the acquiring the second signal waveform and the second peak information from the storage device includes: calculating a first similarity degree between the first signal waveform and each of a plurality of storage signal waveforms included in the plurality of annotated signals; and acquiring a storage signal waveform having the first similarity degree greater than or equal to a first threshold as the second signal waveform. The receiving input, by a user, of first peak information includes: receiving input of first peak information after the displaying the first signal waveform on the display device and the displaying the second signal waveform and the second peak information image on the display device.

According to such the configuration, the user can input the first peak information about the first chromatogram while visually recognizing the second signal waveform similar to the first signal waveform and prepared in the past and the second peak information indicating the peak of the second signal waveform. Accordingly, the convenience of the user can be improved with respect to the input of the first peak information.

(Clause 3) In the training support method described in claim2, the acquiring a second signal waveform and the second peak information from the storage device includes: acquiring the second signal waveform and the second peak information after the displaying the first signal waveform on the display device and the receiving the input of the first peak information by the user The acquiring the second signal waveform and the second peak information from the storage device includes: calculating a first similarity degree between the first signal waveform and each of a plurality of storage signal waveforms included in the plurality of annotated signals; calculating a second similarity degree between the first peak information and each of a plurality of pieces of storage peak information included in the plurality of annotated signals; and acquiring a storage signal waveform as the second signal waveform, the storage signal waveform having one or more peaks specified by storage peak information, in which the first similarity degree is greater than or equal to a first threshold and the second similarity degree is greater than or equal to a second threshold.

According to such the configuration, the storage signal waveform having the peak specified by the storage peak information, in which the first similarity degree is greater than or equal to the first threshold and the second similarity degree is greater than or equal to the second threshold, can be displayed as the second signal waveform. Consequently, the user can check such the second signal waveform.

(Clause 4) In the training support method described in clause 2 or 3, the second signal waveform includes a highly-similar signal waveform and a low-similar signal waveform having the first similarity degree lower than that of the highly-similar signal waveform. The displaying the second signal waveform and the second peak information image on the display device includes displaying a waveform image indicating the highly-similar signal waveform in preference to a waveform image indicating the low-similar signal waveform.

According to such the configuration, the user can visually recognize the second signal waveform having the high similarity degree to the first signal waveform in preference to the second signal waveform having the low similarity.

(Clause 5) In the training support method described in any one of clauses 2 to 4, the displaying the second signal waveform and the second peak information image on the display device includes displaying the first similarity degree of the second signal waveform in association with a second signal waveform image indicating the second signal waveform.

According to such the configuration, the user can visually recognize the first similarity degree of the second signal waveform.

(Clause 6) In the training support method described in any one of terms 2 to 5, the calculating the first similarity degree includes calculating the first similarity degree based on a first feature amount of the first signal waveform and a second feature amount of an identical type of the first feature amount of the storage signal waveform.

According to such the configuration, the first similarity degree can be calculated by a relatively simple arithmetic operation.

(Clause 7) In the training support method described in any one of clauses 2 to 6, the calculating the first similarity degree includes calculating the first similarity degree based on an analysis result indicated by the first signal waveform and an analysis result indicated by the storage signal waveform.

According to such the configuration, the first similarity degree can be calculated by a relatively simple arithmetic operation.

(Clause 8) In the training support method described in any one of the first to seventh clauses, the training support method further includes displaying a first peak information image indicating the first peak information on the display device.

According to such the configuration, the display device can recognize the first peak information input by the user.

(Clause 9) In the training support method described in clause 8, the displaying the first peak information image on the display device includes displaying a first feature amount image indicating a feature amount of one or more peaks specified by first peak information indicated by the first peak information image In addition, the displaying the second signal waveform and the second peak information image on the display device includes displaying a second feature amount image indicating a feature amount of one or more peaks specified by second peak information indicated by the second peak information image.

According to such the configuration, after the user inputs the first peak information, the user can recognize the feature amount of the peak specified by the peak of the first peak information and the feature amount of the peak specified by the second peak information. Accordingly, the user can check whether the input first peak information is appropriate.

(Clause 10) In the training support method described in any one of clauses 1 to 9, the training support method further includes displaying a detected peak information image indicating detected peak information specifying one or more peaks of the first signal waveform detected using the estimation model. The receiving input, by a user, of first peak information includes: receiving input of first peak information after the detected peak information image and the first signal waveform are displayed.

According to such the configuration, the user can input the first peak information while referring to the detected peak information.

(Clause 11) In the training support method described in any one of clauses 1 to 10, the training support method further includes receiving input of a first point and a second point by the user while the first signal waveform is displayed on the display device. The second peak information image is a line image. A region surrounded by the second signal waveform and the line image is a region indicating the one or more peaks of the second signal waveform. A peak region specified by the first peak information is a region surrounded by a line connecting the first point and the second point and a line included in the first signal waveform.

According to such the configuration, the first peak information can be input by designating the first point and the second point of the first signal waveform. Accordingly, the user can relatively easily input the first peak information, so that the convenience of the user can be improved.

(Clause 12) A training support program according to one aspect is a program for causing a computer to execute processing for supporting a training operation of an estimation model used to detect a peak of a signal waveform acquired by an analysis device. The training support program causes the computer to acquire a first signal waveform output by an analysis device. The training support program causes the computer to display the first signal waveform on a display device. The training support program causes the computer to acquire a second signal waveform having a high similarity degree to the first signal waveform and second peak information specifying one or more peaks of the second signal waveform from a storage device that stores a plurality of annotated signals. The training support program causes the computer to display, on the display device, the second signal waveform and a second peak information image indicating the second peak information. The training support program causes the computer to receive input, by a user, of first peak information specifying one or more peaks of a first signal waveform. The training support program causes the computer to train the estimation model based on the first signal waveform and the first peak information.

According to such the configuration, the user can input the first peak information having consistency with the past second peak information, and can update the estimation model based on the first peak information. Consequently, the user is encouraged to input the training data in which the variation in the annotation is prevented. As a result, the quality of the estimation model can be improved while preventing the variation of the annotation.

For the above-described embodiments and modifications, it is planned from the beginning of the application to appropriately combine the configurations described in the embodiments within a range in which no inconvenience or contradiction occurs including combinations not mentioned in the specification.

Although the embodiments of the present invention has been described, it should be considered that the disclosed embodiment is an example in all respects and not restrictive. The scope of the present invention is indicated by the claims, and it is intended that all modifications within the meaning and scope of the claims are included in the present invention.