Analytical support equipment and analytical systems

Abstract

This makes it possible to properly determine the well to be measured. [Solution] The analysis support device 200 includes a trained model 220 that has been trained to output image data similar to the input image data by inputting input image data including an image of the storage unit when no sample is present in the storage unit; an input unit 210 that inputs first image data including an image of the storage unit to the trained model; a calculation unit 240 that calculates the difference between the first image data and a second image data output from the trained model 220 by inputting the first image data to the trained model; and a determination unit 250 that determines whether or not a sample is present in the storage unit in the first image data based on the difference in images calculated by the calculation unit 240.

Description

Technical Field

[0001] The present invention relates to an analysis support device and an analysis system.

Background Art

[0002] There is a mass spectrometer that uses MALDI (Matrix Assisted Laser Desorption / Ionization). In a mass spectrometer using MALDI, a well plate having a plurality of wells is used. A sample / matrix mixture is dropped into the wells of the well plate. After drying the sample and matrix mixture dropped into the wells, the well plate is set in the mass spectrometer. In a mass spectrometer using the MALDI method, ions are generated by irradiating a laser beam onto the sample contained in the well, and the ions are detected by a detector. Also, a mass spectrum is generated based on the detection signal from the detector.

[0003] In a mass spectrometer, for example, as in Patent Document 1, batch analysis for processing a plurality of samples at once may be performed. In batch analysis, a plurality of samples are analyzed collectively according to a batch table. The batch table includes the execution order and analysis conditions for each analysis of a plurality of samples, and is created in advance before batch analysis.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] On the other hand, well plates may contain a mixture of wells with and without samples. In this case, the operator needs to select the wells to be analyzed (wells containing samples) from among the multiple wells on the well plate before starting batch analysis. Batch analysis in mass spectrometers is ideally performed continuously without operator intervention.

[0006] The object of the present invention is to provide an analytical support device and analytical system capable of appropriately determining the well to be measured. [Means for solving the problem]

[0007] An analytical support device according to one aspect of the present invention is an analytical support device that assists the operation of a mass spectrometer that performs mass analysis by irradiating a sample contained in a containment section with a laser, and comprises: a trained model that has been trained to output image data that approximates the input image data by inputting input image data including an image of the containment section when no sample is present in the containment section; an input unit that inputs first image data including an image of the containment section to the trained model; a calculation unit that calculates the difference between the first image data and a second image data output from the trained model by inputting the first image data to the trained model; and a determination unit that determines whether or not a sample is present in the containment section in the first image data based on the difference in images calculated by the calculation unit.

[0008] An analytical system according to another aspect of the present invention comprises the analytical support device described above and a mass spectrometer. [Effects of the Invention]

[0009] This makes it possible to appropriately determine which well to measure. [Brief explanation of the drawing]

[0010] [Figure 1] This is a schematic diagram illustrating the configuration of the analysis system according to this embodiment. [Figure 2]This is a plan view showing an example of a well play placed on the mounting section. [Figure 3] This figure shows an example of image data generated by the imaging unit. [Figure 4] This figure shows an example of image data generated by the imaging unit. [Figure 5] This figure shows an example of the configuration of an analytical support device. [Figure 6] This is a diagram showing the functional configuration of an analytical support device. [Figure 7] This is a diagram to explain the learning model. [Figure 8] This is a diagram to explain the trained model. [Figure 9] This is a diagram to explain the trained model. [Figure 10] This is a flowchart of the analysis support operation of the analysis support device. [Figure 11] This figure shows an example of image data generated by the imaging unit. [Figure 12] This figure shows an example of image data generated by the imaging unit. [Figure 13] This block diagram shows the functional configuration of an analysis support device according to another embodiment. [Figure 14] This is a diagram illustrating the sample area and irradiation position. [Modes for carrying out the invention]

[0011] (1) Analysis system Figure 1 is a schematic diagram illustrating the configuration of the analysis system 500 according to this embodiment. The analysis system 500 comprises a mass spectrometer 100 and an analysis support device 200. The mass spectrometer 100 is a mass spectrometer using an ion source based on the matrix-assisted laser desorption / ionization (MALDI) method. The mass spectrometer 100 comprises a sample chamber CH, an imaging unit 110, an irradiation unit 120, a mass analysis unit 130, a drive unit 140, and an analysis control unit 150.

[0012] The sample chamber CH houses the placement section PT. A well plate WP is placed on the placement section PT. The well plate WP has a plurality of wells WL. A sample SP1 is housed in the well WL of the well plate WP. After the sample SP1 is dropped into the well WL, the mixture of the analyte and the matrix dries, and the sample SP1 adheres to the well WL. In the present embodiment, the well plate WP includes the well WL in which the sample SP1 is housed and the well WL in which the sample SP1 is not housed.

[0013] The imaging unit 110 has a lens (not shown) facing downward and is a camera that generates image data by imaging the situation below. In the present embodiment, the lens is provided so as to face the well plate WP. The imaging unit 110 images one well WL on the well plate WP located below the lens and generates image data.

[0014] The irradiation unit 120 has an emission unit (not shown) facing downward. In the present embodiment, the emission unit is provided so as to face the well plate WP. The irradiation unit 120 irradiates a laser to the sample SP1 housed in one well WL on the well plate WP located below the emission unit. The laser irradiated to the sample SP1 is, for example, a nitrogen laser. When the sample SP1 is irradiated with the laser, the sample SP1 is ionized. Then, the ions of the sample SP1 are guided to the mass spectrometry unit 130.

[0015] The mass spectrometry unit 130 includes a mass separation unit and a detection unit not shown. In the mass separation unit, the ions of the sample SP1 are separated for each m / z. As the mass separation unit, a quadrupole type, an ion trap type, a time-of-flight type, or the like is used. In the detection unit, the ions separated for each m / z are detected. As the detection unit, a photomultiplier tube or a secondary electron multiplier tube or the like is used. Thereby, in the mass spectrometry unit 130, waveform data including peaks indicating the separated components is generated based on the ion detection signal of the detection unit. The waveform data is, for example, a mass spectrum.

[0016] The drive unit 140 is, for example, an actuator, which moves the mounting unit PT horizontally. This allows the drive unit 140 to position a desired well WL among the multiple wells WL in the well plate WP below the lens of the imaging unit 110. The imaging unit 110 images the desired well WL located below the lens and generates image data including the desired well WL. The drive unit 140 can also position a desired well WL among the wells WL in the well plate WP below the output part of the irradiation unit 120. This allows the irradiation unit 120 to irradiate the sample SP1 in the desired well WL with a laser.

[0017] The analysis control unit 150 includes a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read-Only Memory), and storage devices (not shown). The analysis control unit 150 controls the operation of the imaging unit 110, the irradiation unit 120, the mass spectrometry unit 130, and the drive unit 140. This makes it possible to perform mass spectrometry on the sample SP1 contained in the well WL of the well plate WP.

[0018] Figure 2 is a plan view showing an example of a well plate WP placed on a mounting section PT. The multiple wells WL of the well plate WP are arranged in a matrix in multiple columns and rows in the horizontal plane. Each column and each row is orthogonal. In each column, the wells WL are arranged linearly and at equal intervals. In each row, the wells WL are arranged linearly and at equal intervals. In the well plate WP of Figure 2, the multiple wells WL are arranged in 12 rows and 4 columns (12 × 4).

[0019] In this embodiment, each well WL is assigned a well number. For example, rows 1-1 to 1-4 are assigned well numbers 1 to 4, rows 2-1 to 2-4 are assigned well numbers 5 to 8, and rows 3-1 to 3-4 are assigned well numbers 9 to 12. Thus, the well numbers are assigned in ascending order of row number and, for each row, in ascending order of column number. In the example in Figure 2, the sample SP1 is contained in wells numbered 1, 3, 9, 11, 17, 19, 25, 27, 33, 35, 41, and 43. The storage device of the analysis control unit 150 stores the type of well plate WP, the arrangement of well WLs within the well plate WP, and the well number assigned to each well WL within the well plate WP as a single well plate information.

[0020] Here, we will explain the image data generated by the imaging unit 110. Figures 3 and 4 show examples of image data generated by the imaging unit 110. The image data in Figure 3 is the image data of imaging region A1 in Figure 2 (the area around well WL of well number 4), and is the image data in which sample SP1 is not present in well WL. The image data in Figure 4 is the image data of imaging region A2 in Figure 2 (the area around well WL of well number 1), and is the image data in which sample SP1 is present in well WL.

[0021] (2) Configuration of the analysis support device Figure 5 shows an example of the configuration of the analysis support device 200. The analysis support device 200 consists of a CPU 201, RAM 202, ROM 203, storage unit 204, operation unit 205, display unit 206, and input / output I / F 207. The CPU 201, RAM 202, ROM 203, storage unit 204, operation unit 205, display unit 206, and input / output I / F 207 are connected to bus 208.

[0022] RAM202 is used as the working area for CPU201. ROM203 stores the system program. The storage unit 204 includes a storage medium such as a hard disk or semiconductor memory, and the analysis support program is pre-stored in the storage unit 204. The analysis support program may be provided on a recording medium such as CD-ROM209 and installed in the storage unit 204. Alternatively, the analysis support program may be distributed via a network from a server outside the analysis support device 200 and installed in the storage unit 204.

[0023] The control unit 205 is an input device such as a keyboard, mouse, or touch panel. The input / output interface 207 is connected to a network. The storage medium for storing the analysis support program is not limited to hard disks or semiconductor memory, but may also be optical discs (MO (Magnetic Optical Disc) / MD (Mini Disc) / DVD (Digital Versatile Disc)), IC cards, optical cards, mask ROMs, EPROMs (Erasable Programmable ROMs), or other semiconductor memory media.

[0024] Figure 6 is a block diagram showing the functional configuration of the analysis support device 200. The analysis support device 200 includes an input unit 210, a learned model 220, an acquisition unit 230, a calculation unit 240, a determination unit 250, an analysis information generation unit 260, and an acquisition command unit 270. Each component (210 to 270) of the analysis support device 200 is realized by executing a computer program, such as an analysis support program, stored in ROM or a storage unit 204, on RAM. Some or all of the components (210 to 270) of the analysis support device 200 may be made up of hardware such as electronic circuits.

[0025] First, the trained model 220 in this embodiment will be described. Figure 7 is a diagram illustrating the trained model 220a. Figures 8 and 9 are diagrams illustrating the trained model 220. The trained model 220a is the trained model before training the trained model 220. The trained model 220a is an autoencoder and includes an input layer 221, an intermediate layer 222, and an output layer 223. The input layer 221, the intermediate layer 222, and the output layer 223 are composed of neural networks. Input image data ID1 is input to the input layer 221. In this embodiment, the input image data ID1 is an image of a well WL that does not have a sample SP1. The trained model 220a is trained so that the output layer 223 outputs output image data OD1 that approximates the input image data ID1. In this way, the trained model 220 is generated by performing unsupervised learning on the trained model 220a. Hereafter, the image data input to the trained model 220 will be referred to as the first image data, and the image data output from the trained model 220 will be referred to as the second image data.

[0026] Therefore, as shown in Figure 8, when a first image data containing a well WL without sample SP1 is input to the trained model 220, a second image data containing a well WL without sample SP1 is output. For example, as shown in the figure, a second image data containing a well WL with sample SP1 is output from the trained model 220. In this case, the difference between the first and second image data is small. On the other hand, as shown in Figure 9, when a first image data containing a well WL with sample SP1 is input to the trained model 220, the well WL containing sample SP1 is not well reproduced in the second image data. For example, as shown in the figure, a second image data containing a well WL without sample SP1 is output from the trained model 220. Therefore, the difference between the first and second image data becomes large.

[0027] Returning to Figure 6, the input unit 210 acquires image data generated by the imaging unit 110 of the mass spectrometer 100. The image data acquired by the input unit 210 is the first image data described above. The input unit 210 inputs the acquired first image data to the trained model 220. As a result, the trained model 220 outputs the second image data. The acquisition unit 230 acquires the second image data output from the trained model 220. The calculation unit 240 calculates the difference between the first image data and the second image data. For example, the calculation unit 240 calculates the difference by accumulating the differences of each pixel in the first image data and the second image data. The determination unit 250 determines whether or not the sample SP1 is present in the well WL where the first image was captured, based on the difference between the first image data and the second image data calculated by the calculation unit 240. For example, the determination unit 250 determines whether or not the sample SP1 is present in the well WL from which the first image data was captured, based on whether or not the difference value calculated by the calculation unit 240 is greater than a predetermined value.

[0028] The well numbers of all wells WL on the well plate WP, and the judgment results corresponding to each well number, are stored in the storage unit 204. The analysis information generation unit 260 generates analysis information related to the analysis operation of the mass spectrometer 100. The analysis information includes the well numbers of all wells WL on the well plate WP, and the judgment results corresponding to each well number. The analysis information generation unit 260 transmits the generated analysis information to the analysis control unit 150. The acquisition command unit 270 acquires information related to the analysis of the mass spectrometer 100 from the analysis control unit 150 and commands the analysis control unit 150 to operate each component of the mass spectrometer 100.

[0029] (3) Analysis support operation Figure 10 is a flowchart of the analysis support operation of the analysis support device 200. First, the acquisition command unit 270 in Figure 6 obtains the number of wells WLs K of the well plate WP from the analysis control unit 150 (step S1). In the well plate WP in Figure 2, the number of wells WLs K is 48. Next, the acquisition command unit 270 sets the variable N to 1 (step S2). Subsequently, the acquisition command unit 270 commands the analysis control unit 150 to image the well WL of well number N (step S3). At this time, the analysis control unit 150 moves the mounting unit PT by controlling the drive unit 140 so that the well WL of well number N is located below the imaging unit 110. In this state, the analysis control unit 150 commands the imaging unit 110 to image the well WL and generate first image data including the imaged well WL, and acquires the first image data generated by the imaging unit 110.

[0030] The input unit 210 acquires the first image data of well number N and inputs it to the trained model 220 (step S4). As a result, the trained model 220 outputs the second image data. The acquisition unit 230 acquires the second image data output from the trained model 220 (step S5). The calculation unit 240 calculates the difference between the first image data and the second image data (step S6). The determination unit 250 determines whether or not the sample SP1 is present in the well WL from which the first image data was captured (step S7).

[0031] If the difference value in step S7 is greater than a predetermined value, the storage unit 204 stores that there is a sample in well WL of well number N (step S8). If the difference value in step S7 is less than or equal to a predetermined value, the storage unit 204 stores that there is no sample in well WL of well number N (step S9). Next, the acquisition command unit 270 adds 1 to the variable N (step S10). In this state, the acquisition command unit 270 determines whether the variable N is greater than or equal to the number of well WLs K (step S11). If the variable N is less than or equal to the number of well WLs K, the process returns to step S3. Therefore, the operations from steps S3 to S11 are repeated until the variable N is greater than K. As a result, the storage unit 204 stores each well number and the presence or absence of the sample SP1 in the well WL of each well number. In step S11, if the variable N is greater than the number of wells WL K, the analysis information generation unit 260 generates analysis information and transmits it to the analysis control unit 150 of the mass spectrometer 100 (step S12). In this case, the analysis control unit 150 of the mass spectrometer 100 controls the mass spectrometry unit 130 based on the analysis information. This makes it possible to perform mass spectrometry only on the wells WL containing the sample SP1 of the well plate WP.

[0032] (4) Effects of the embodiment According to the analysis support device 200 of the above embodiment, the trained model 220 is machine-trained so that when input image data ID1 is input to the input layer 221, output image data OD2, which approximates the input image data ID1, is output from the output layer 223. Therefore, when first image data in a state where the sample SP1 is not included in the well WL is input to the trained model 220, second image data in a state where the sample SP1 is not included in the well WL is output. In this case, the difference value between the first image data (where the sample SP1 is not included in the well WL) and the second image data (where the sample SP1 is not included in the well WL) is small. On the other hand, when first image data in a state where the sample SP1 is included in the well WL is input to the trained model 220, the well WL in a state where the sample SP1 is included is not well reproduced in the second image data. In this case, the difference value between the first image data (where the sample SP1 is included in the well WL) and the second image data (where the sample SP1 is not well reproduced in the well WL) is large. Therefore, based on the difference value of the images, it is possible to easily determine whether or not the sample SP1 is included in the well WL in the first image data.

[0033] (5) Other embodiments (5-1) Figures 11 and 12 show examples of image data generated by the imaging unit 110. The image data generated by the imaging unit 110 contains image data that was not generated properly. For example, as shown in Figure 11, when imaging is performed to include one well WL in the first image data, other wells WLX may be captured in the image. Also, for example, as shown in Figure 12, whiteout (WO) may occur in the first image data. When image data like that in Figures 11 and 12 is input to the trained model 220, the wells WLX and whiteout, etc., are not reproduced well in the second image data. As a result, the difference value between the first image data and the second image data calculated by the calculation unit 240 becomes large. Consequently, the determination unit 250 may determine that the sample SP1 is contained in the well WL even though the sample SP1 is not contained in the well WL.

[0034] To suppress such misjudgments by the determination unit 250, the input image data ID1 input to the learning model 220a during machine learning may include at least one of the image data in which other wells WLX are reflected (Figure 11) and the image data in which overexposure occurs (Figure 12). In this case, the learning model 220a is trained to output output image data OD1 that approximates the input image data ID1. Therefore, in the second image data output to the trained model 220, it becomes possible to reproduce the image data in which other wells WLX are reflected and the image data in which overexposure occurs. As a result, even if the first image data includes overexposure and other containment parts, the overexposure and other wells WLX in the first image data are suppressed from affecting the image difference value calculated by the calculation unit 240. As a result, it becomes possible to more accurately determine whether or not the sample SP1 is present in the well WL.

[0035] (5-2) In the above embodiment, the analysis information generation unit 260 generates analysis information to be transmitted to the mass spectrometer 100. An example of the analysis information is described which includes the well numbers of all wells WL on the well plate WP and the determination result corresponding to each well number, but the analysis information may further include other information. Figure 13 is a block diagram showing the functional configuration of the analysis support device 200A according to another embodiment. Figure 14 is a diagram for explaining the sample area SR and the irradiation position IP. The analysis support device 200A further comprises a sample area determination unit 280 and an irradiation position determination unit 290.

[0036] The sample region determination unit 280 determines the sample region SR, which indicates the region in the well WL where the sample SP1 is located, based on the first image data. For example, the sample region determination unit 280 identifies the contour OE of the sample SP1 in the well WL by detecting areas where the pixel value changes significantly in adjacent pixels (edge ​​detection), as shown in Figure 14. The sample region determination unit 280 determines the region enclosed by the contour OE as the sample region SR. In this case, it becomes possible to automatically determine whether or not the sample SP1 is contained in the well WL and to determine the laser irradiation range that can appropriately ionize the sample during mass spectrometry. Therefore, mass spectrometry can be performed appropriately.

[0037] Furthermore, the irradiation position determination unit 290 in Figure 13 determines the irradiation position IP, which indicates the position in the mass spectrometer 100 where the laser is irradiated within the sample region SR. In this case, the irradiation position IP is determined to be located within the sample region SR, so that the mass spectrometer 100 can appropriately irradiate the sample SP1 with the laser. Alternatively, the irradiation position determination unit 290 may set the irradiation position IP to the position of the pixel with the largest difference value among the calculated differences within the sample region SR. In this case, for example, it becomes possible to irradiate the sweet spot of the sample SP1 with the laser. Thus, the analysis information generated by the analysis information generation unit 260 may include each well number, the presence or absence of sample SP1 in the well WL of each well number, and the sample region SR and irradiation position IP for the well WL containing the sample SP1. In this case, it becomes possible to transmit analysis information to the mass spectrometer 100 that enables more appropriate mass analysis.

[0038] (6) Aspect Those skilled in the art will understand that the above-described exemplary embodiments are specific examples of the following embodiments. (Section 1) An analytical support device according to one aspect of the present invention is: An analytical support device that assists the operation of a mass spectrometer that performs mass analysis by irradiating a sample contained in a containment section with a laser, A trained model that has been trained to output image data that approximates the input image data by inputting input image data including an image of the storage unit when no sample is present in the storage unit, An input unit that inputs first image data, including the image of the storage unit, to the trained model, A calculation unit that calculates the difference between the first image data and the second image data output from the trained model by inputting the first image data into the trained model, The system includes a determination unit that determines whether or not a sample is present in the storage unit within the first image data based on the difference of the image calculated by the calculation unit.

[0039] According to the analysis support device described in paragraph 1, the trained model is trained to output image data that approximates the input image data when input image data is received. Therefore, when first image data showing a state where the container does not contain a sample is input to the trained model, second image data showing a state where the container does not contain a sample is output. In this case, the difference between the first image data (where the container does not contain a sample) and the second image data (where the container does not contain a sample) is small. On the other hand, when first image data showing a state where the container contains a sample is input to the trained model, the container with the sample is not well reproduced in the second image data. In this case, the difference between the first image data (where the container contains a sample) and the second image data (where the container with the sample is not reproduced) is large.

[0040] Therefore, based on the difference between the images, it is easy to determine whether or not a sample is contained in the containment section in the first image data. As a result, it becomes possible to automatically and appropriately determine whether or not a sample is contained in the containment section without the need for human intervention.

[0041] (Paragraph 2) In the analytical support device described in Paragraph 1, The difference of the image calculated by the calculation unit is the cumulative value of the differences between each pixel of the first image data and the second image data. The determination unit may determine whether or not a sample is present in the storage area within the first image data based on whether or not the accumulated difference of each pixel is greater than a predetermined value.

[0042] According to the analysis support device described in paragraph 2, it becomes possible to determine whether or not a sample is contained in the containment section with a simple process.

[0043] (3) In the analytical support device described in paragraph 1 or 2, The input image data may include image data that includes overexposure, and / or image data that includes the housing unit and other housing units different from the housing unit.

[0044] According to the analysis support device described in Section 3, the input image data that the trained model learns includes image data containing overexposed areas and image data containing areas other than the containment area. Therefore, if the first image data includes image data containing overexposed areas and image data containing areas other than the containment area, the overexposed areas and other containment areas can be reproduced in the second image data. As a result, even if the first image data contains overexposed areas and other containment areas, the influence of the overexposed areas and other containment areas in the first image data on the difference of the image calculated by the calculation unit is suppressed, making it possible to more accurately determine whether or not a sample is present in the containment area.

[0045] (Article 4) The analytical support device described in any one of paragraphs 1 to 3 is: The system further comprises an analysis information generation unit that generates analysis information related to the operation of the mass spectrometer, The analysis information may include the determination result by the determination unit indicating whether or not a sample is present in the containment unit.

[0046] According to the analysis support device described in paragraph 4, the analysis information includes a determination result indicating whether or not a sample is present in the containment unit imaged by the imaging unit. Therefore, it becomes possible to provide the analysis device in real time with information on whether or not mass spectrometry can be performed on the containment unit included in the first image data.

[0047] (Article 5) The analytical support device described in any one of paragraphs 1 to 3 is: The system may further include a sample region determination unit that determines a sample region indicating the area of ​​the sample within the storage unit based on the first image data.

[0048] According to the analysis support device described in Section 5, it is possible to determine the sample area within a storage section where a sample is determined to be present. This makes it possible to automatically determine whether or not a sample is present in the storage section and to define the laser irradiation range that can appropriately ionize the sample during mass spectrometry. Therefore, mass spectrometry can be performed appropriately.

[0049] (Section 6) The analytical support device described in Section 5 is: The system may further include an irradiation position determination unit that determines the irradiation position, which indicates the position where the laser is irradiated during mass spectrometry, to be within the sample region determined by the sample region determination unit.

[0050] According to the analysis support device described in Section 6, the laser irradiation position is determined within the sample area, allowing for more appropriate mass spectrometry to be performed.

[0051] (Section 7) The analytical support device described in Section 6 is: The system further comprises an analysis information generation unit that generates analysis information related to the operation of the mass spectrometer, The aforementioned analysis information is, The determination result by the determination unit indicating whether or not a sample is present in the containment unit may include the sample area and the irradiation position within the containment unit if a sample was present in the containment unit.

[0052] According to the analysis support device described in Section 7, the analysis information includes a determination result indicating whether or not a sample is present in the containment area captured by the imaging unit. It becomes possible to provide the analysis device in real time whether or not mass spectrometry can be performed on the containment area included in the first image data, and if a sample is present in the containment area, the sample region and irradiation position. This makes it possible to perform mass spectrometry on the sample more appropriately.

[0053] (Clause 8) Analysis systems relating to other embodiments are: An analytical support device as described in any one of paragraphs 1 to 7, The system comprises the aforementioned mass spectrometer and

[0054] According to the analysis system described in Section 8, it becomes possible to perform appropriate mass spectrometry on the sample.

[0055] (Paragraph 9) In the analysis system described in Paragraph 8, The aforementioned mass spectrometer is A mass spectrometer using the MALDI method may also be used.

[0056] According to the analysis system described in Section 9, it is possible to provide a mass spectrometer utilizing the MALDI method that can appropriately perform mass analysis on a sample. [Explanation of Symbols]

[0057] 100…Mass spectrometer, 110…Imaging unit, 120…Irradiation unit, 130…Mass spectrometry unit, 140…Drive unit, 150…Analysis control unit, 200…Analysis support device, 200A…Analysis support device, 201…CPU, 202…RAM, 203…ROM, 204…Storage unit, 205…Operation unit, 206…Display unit, 207…Input / Output I / F, 208…Bus, 209…CD-ROM, 210…Input unit, 220…Trained model, 220a…Trained model, 221…Input layer, 222… Intermediate layer, 223…Output layer, 230…Acquisition unit, 240…Calculation unit, 250…Determination unit, 260…Analysis information generation unit, 270…Acquisition command unit, 280…Sample area determination unit, 290…Irradiation position determination unit, 500…Analysis system, A1…Imaging area, A2…Imaging area, CH…Sample chamber, IP…Irradiation position, ID1…Input image data, OD1, OD2…Output image data, PT…Placement unit, SP1…Sample, SR…Sample area, WL, WLX…Well, WP…Well plate

Claims

1. An analytical support device that assists the operation of a mass spectrometer that performs mass analysis by irradiating a sample contained in a containment section with a laser, A trained model that has been trained to output image data that approximates the input image data by inputting input image data including an image of the storage unit when no sample is present in the storage unit, An input unit that inputs first image data, including the image of the storage unit, to the trained model, A calculation unit that calculates the difference between the first image data and the second image data output from the trained model by inputting the first image data into the trained model, An analysis support device comprising: a determination unit that determines whether or not a sample is present in the storage unit within the first image data based on the difference of the image calculated by the calculation unit.

2. The difference of the image calculated by the calculation unit is the cumulative value of the differences between each pixel of the first image data and the second image data. The analysis support apparatus according to claim 1, wherein the determination unit determines whether or not a sample is present in the storage area within the first image data based on whether or not the accumulated value of the differences between each pixel is greater than a predetermined value.

3. The aforementioned input image data is Image data including overexposure, and / or image data including the said storage unit and other storage units different from the said storage unit, The analytical support apparatus according to claim 1 or 2, including the following:

4. The system further comprises an analysis information generation unit that generates analysis information related to the operation of the mass spectrometer, The analysis support apparatus according to claim 1 or 2, wherein the analysis information includes a determination result by the determination unit indicating whether or not a sample is present in the containment unit.

5. The analysis support apparatus according to claim 1 or 2, further comprising a sample region determination unit that determines a sample region indicating a region of a sample within the storage unit based on the first image data.

6. The analytical support apparatus according to claim 5, further comprising an irradiation position determination unit that determines the irradiation position, which indicates the position where a laser is irradiated during mass spectrometry, within the sample region determined by the sample region determination unit.

7. The system further comprises an analysis information generation unit that generates analysis information related to the operation of the mass spectrometer, The aforementioned analysis information is, The analytical support apparatus according to claim 6, comprising: a determination result by the determination unit indicating whether or not a sample is present in the containment unit; and the sample area and irradiation position within the containment unit when a sample is present in the containment unit.

8. The analytical support device according to claim 1 or 2, The aforementioned mass spectrometer and, An analytical system equipped with [the necessary features].

9. The aforementioned mass spectrometer is Mass spectrometer using the MALDI method, The analysis system according to claim 8, including the following:

Images