Spectroscopic analysis system and spectroscopic analysis method

Abstract

To provide a spectroscopic analysis system, etc. that makes it possible to appropriately conduct measurement even when measurement time, etc is limited.SOLUTION: A spectroscopic analysis system 100 comprises: an operation panel 21 that accepts the upper-limit value of measurement time of a spectroscopic spectrum and / or the lower-limit value of measurement accuracy as a user set condition regarding the spectroscopic spectrum measurement of a sample; and a control unit 32 that derives a prescribed recommended measurement condition that would satisfy the user set condition and causes a display unit 22 to show the recommended measurement condition. The recommended measurement condition is at least one of the wavelength range of light that is used for measuring spectroscopic spectra, the wavelength sampling interval of light, the slit width of diffraction gratings 2a, 8a of a spectroscope that spectrally separates light, and the wavelength sweep speed of light.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a spectroscopic analysis system and the like. [Background technology]

[0002] Spectroscopic analysis is known as a method for determining the composition or quantifying the concentration of a substance by measuring the spectrum of light when the substance absorbs or emits light. Regarding fluorescence fingerprint analysis, which is one of such spectroscopic analysis methods, for example, Patent Document 1 describes that "fluorescence intensity is measured while the excitation wavelength for irradiation and the fluorescence wavelength for observation are changed stepwise for each of a plurality of extracted samples, and a plurality of pieces of fluorescence fingerprint information are obtained as fluorescence fingerprint continuum information."

[0003] Furthermore, Non-Patent Document 1 states that "In fluorescence fingerprint (or excitation-emission matrix) measurement, the fluorescence intensity is measured while changing both the wavelength conditions of the excitation light and the wavelength conditions of the fluorescence being observed (solid arrow in Figure 2). In other words, a brute-force investigation is performed to see whether electronic excitation occurs in the target sample and whether fluorescence is emitted." [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2020-76612 A [Non-patent literature]

[0005] [Non-Patent Document 1] Mizuki Tsuta, et al., "Food quality evaluation technology using fluorescence fingerprinting and its application," Journal of the Agricultural Chemical Society of Japan, "Chemistry and Biology," Vol. 53, No. 5, 2015, pp. 285-292 Summary of the Invention [Problem to be solved by the invention]

[0006] For example, if one tries to obtain high-resolution measurement results over the entire wavelength range in spectroscopic analysis, it takes a long time to perform the measurement. On the other hand, if one measures only specific wavelengths using filter spectroscopy, it may not be possible to obtain important spectral data. It is desirable to be able to perform appropriate measurements even when there are restrictions on the measurement time based on spectroscopic analysis.

[0007] Therefore, an object of the present invention is to provide a spectroscopic analysis system etc. that can perform appropriate measurements even when there are restrictions on measurement time etc. [Means for solving the problem]

[0008] In order to solve such problems, the spectroscopic analysis system of the present invention includes an input unit that accepts input of at least one of an upper limit value of the measurement time of the spectroscopic analysis spectrum and a lower limit value of the measurement accuracy as user-set conditions for measuring the spectroscopic analysis spectrum of a sample, and a control unit that derives predetermined recommended measurement conditions that satisfy the user-set conditions and displays the recommended measurement conditions on a display device, wherein the recommended measurement conditions are at least one of the wavelength range of light used in measuring the spectroscopic analysis spectrum, the sampling interval of the wavelength of the light, the slit width of the diffraction grating of a spectrometer that disperses the light, and the sweep speed of the wavelength of the light. Effect of the Invention

[0009] According to the present invention, it is possible to provide a spectroscopic analysis system etc. that can perform appropriate measurement even when there are restrictions on measurement time etc. [Brief description of the drawings]

[0010] [Figure 1] 1 is a configuration diagram of a spectroscopic analysis system according to a first embodiment. [Figure 2A] FIG. 2 is a diagram showing an example of a spectroscopic analysis spectrum obtained by the spectroscopic analysis system according to the first embodiment. [Figure 2B]FIG. 2 is a diagram showing a state in which a spectroscopic analysis spectrum obtained by the spectroscopic analysis system according to the first embodiment has been subjected to a predetermined pre-processing. [Figure 2C] FIG. 2 is an explanatory diagram of one-dimensional data of a spectroscopic analysis spectrum obtained by the spectroscopic analysis system according to the first embodiment. [Diagram 3] FIG. 2 is a functional block diagram of a control unit included in the spectroscopic analysis system according to the first embodiment. [Figure 4] 5 is a flowchart showing the process of a control unit of the spectroscopic analysis system according to the first embodiment. [Diagram 5] 4 is a display example of a setting screen for measurement conditions and the like in the spectroscopic analysis system according to the first embodiment. [Figure 6] 4 is a display example of a screen showing measurement and analysis results in the spectroscopic analysis system according to the first embodiment. [Figure 7A] FIG. 2 is an explanatory diagram showing how the range of the target wavelength region is changed when the GAWLSPLS method is used in the spectroscopic analysis system according to the first embodiment. [Figure 7B] FIG. 2 is an explanatory diagram showing a measurement wavelength range set based on the GAWLSPLS method when the GAWLSPLS method is used in the spectroscopic analysis system according to the first embodiment. [Figure 8] FIG. 2 is an explanatory diagram showing an example in which the fluorescence wavelength is fixed and the excitation wavelength is swept in a predetermined manner in the spectroscopic analysis system according to the first embodiment. [Figure 9] FIG. 4 is an explanatory diagram showing experimental results of a fluorescence fingerprint analysis performed by the spectroscopic analysis system according to the first embodiment. [Figure 10A] 10 is a flowchart showing the process of a control unit of a spectroscopic analysis system according to a second embodiment. [Figure 10B] 10 is a flowchart showing the process of a control unit of a spectroscopic analysis system according to a second embodiment. [Figure 11A] FIG. 13 is a diagram showing an experimental result when RMSECV is used as an index of goodness of fit in the spectroscopic analysis system according to the fourth embodiment. [Figure 11B]FIG. 13 is a diagram showing experimental results when C1 is used as an index of goodness of fit in the spectroscopic analysis system according to the fourth embodiment. [Figure 12A] FIG. 13 is a diagram showing a prediction result of a verification sample when RMSECV is used as an index of goodness of fit in the spectroscopic analysis system according to the fourth embodiment. [Figure 12B] FIG. 13 is a diagram showing a prediction result of a verification sample when C1 is used as an index of goodness of fit in the spectroscopic analysis system according to the fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] First Embodiment FIG. 1 is a configuration diagram of a spectroscopic analysis system 100 according to the first embodiment. The spectroscopic analysis system 100 shown in FIG. 1 is a system that performs composition discrimination and concentration quantification of a sample based on spectroscopic analysis. Spectroscopic analysis is a method for performing composition discrimination and concentration quantification of a sample based on the spectrum of light when the sample absorbs or emits light. Samples that are the subject of such spectroscopic analysis include, but are not limited to, human or animal cells and blood, culture fluid, food, beverages, dyes, and minerals (e.g., gemstones). Spectroscopic analysis may also be used to measure suspended matter in the air. Spectroscopic analysis includes fluorescence fingerprint analysis and absorption spectroscopy, and the following describes a case where fluorescence fingerprint analysis is used as an example.

[0012] 1, the spectroscopic analysis system 100 includes a photometer unit 10, an operation unit 20, and a data processing unit 30. The photometer unit 10 is a device that irradiates a sample M1 with excitation light and measures the fluorescence emitted from the sample M1 in response to the irradiation. Note that the photometer unit 10 is shielded by a housing (not shown) to prevent light from entering the photometer unit 10 from the outside.

[0013] For example, when light is irradiated onto sample M1, the electrons contained in the molecules of sample M1 absorb the energy of the light, transitioning to a higher energy level and becoming excited. The light that causes this phenomenon is called "excitation light." Moreover, the light emitted when the excited electrons return to their original ground state is called "fluorescence."

[0014] In fluorescence fingerprint analysis, the wavelength of the excitation light irradiated on the sample M1 and the wavelength of the fluorescence to be measured are each changed, and the fluorescence intensity is measured. This results in a spectroscopic analysis spectrum (three-dimensional fluorescence spectrum, fluorescence fingerprint) consisting of three components: excitation wavelength, fluorescence wavelength, and fluorescence intensity. Since such a spectroscopic analysis spectrum is unique to a substance, it is possible to determine the composition and quantify the concentration of the substance (sample). In fluorescence fingerprint analysis, the "light used to measure the spectroscopic analysis spectrum" is the excitation light and the fluorescence.

[0015] 1, the photometer section 10 includes a light source 1, an excitation side spectroscope 2 (spectroscope), a beam splitter 3, a monitor detector 4, an excitation side filter 5, a sample mounting section 6, a fluorescence side filter 7, a fluorescence side spectroscope 8 (spectroscope), and a detector 9. In addition to the components described above, the photometer section 10 also includes an excitation side pulse motor 11, a fluorescence side pulse motor 12, an excitation side filter pulse motor 13, and a fluorescence side filter pulse motor 14.

[0016] The light source 1 emits a predetermined light. For example, a xenon lamp, a halogen lamp, or a medium pressure mercury lamp is used as the light source 1. The excitation side spectroscope 2 separates the light emitted from the light source 1 into a predetermined range of wavelengths. The excitation side spectroscope 2 includes a diffraction grating 2a for extracting light of a predetermined range of wavelengths from light containing various wavelengths. The diffraction grating 2a is configured, for example, such that a plurality of fine grooves are provided in parallel at a predetermined interval on the surface of an optical material. The angle of incidence of light on the diffraction grating 2a changes according to the rotation angle of the diffraction grating 2a, and the wavelength of the light extracted from the diffraction grating 2a changes accordingly. The excitation side pulse motor 11 adjusts the rotation angle of the diffraction grating 2a of the excitation side spectroscope 2 based on a command from the control unit 32.

[0017] The beam splitter 3 splits the light from the excitation side spectroscope 2 into two (splits the light beam into two). The monitor detector 4 measures the intensity of one of the light beams split by the beam splitter 3. The measurement result of the monitor detector 4 is output to the A / D converter 31 as a predetermined electric signal. The excitation side filter 5 is a filter that transmits light of a predetermined wavelength range and blocks the remaining light, and is disposed between the beam splitter 3 and the sample mounting unit 6. The excitation side filter 5 includes, for example, a plurality of cut filters (not shown). A cut filter selected from the plurality of cut filters is disposed on the optical path by the excitation side filter pulse motor 13. The excitation side filter pulse motor 13 moves a predetermined cut filter included in the excitation side filter 5 onto the optical path based on a command from the control unit 32.

[0018] The sample mounting section 6 is a holder for mounting a sample M1 to be subjected to fluorescence fingerprint analysis. When the sample M1 is a liquid or gas, a container or the like (not shown) containing the sample M1 is mounted on the sample mounting section 6. Then, light (excitation light) transmitted through the excitation side filter 5 is incident on the sample M1.

[0019] Fluorescence side filter 7 is a filter that transmits light of a predetermined range of wavelengths and blocks the remaining light, and is disposed between sample mounting unit 6 and fluorescence side spectroscope 8. Fluorescence side filter 7 includes, for example, a plurality of cut filters (not shown). A cut filter selected in advance from the plurality of cut filters is disposed on the optical path by fluorescence side filter pulse motor 14. Fluorescence side filter pulse motor 14 moves a predetermined cut filter included in fluorescence side filter 7 onto the optical path based on a command from control unit 32.

[0020] Fluorescence side spectroscope 8 disperses light (fluorescence) emitted from sample M1, and is equipped with a diffraction grating 8a. Fluorescence side pulse motor 12 adjusts the rotation angle of diffraction grating 8a of fluorescence side spectroscope 8 based on commands from control unit 32. Detector 9 converts the light (fluorescence) from fluorescence side spectroscope 8 into a predetermined electrical signal. The electrical signal (analog signal) from detector 9 is output to A / D converter 31. Note that the configuration shown in FIG. 1 is just an example, and the configuration of photometer unit 10 is not limited to this.

[0021] The operation unit 20 shown in Fig. 1 receives data input by a user and displays the processing results of the control unit 32. The operation unit 20 includes an operation panel 21 (input unit) and a display unit 22 (display device). The operation panel 21 receives input of measurement conditions for fluorescence fingerprint analysis and the like based on user operations, and includes predetermined keys (not shown) and buttons (not shown). Note that a keyboard (not shown) and a mouse (not shown) may be used instead of the operation panel 21. The display unit 22 is, for example, a display, and displays a setting screen for measurement conditions and the like as well as the processing results of the control unit 32 in a predetermined manner.

[0022] The data processing unit 30 shown in FIG. 1 includes an A / D converter 31 and a control unit 32. The A / D converter 31 converts analog signals input from the monitor detector 4 and the detector 9 into digital signals. The control unit 32 executes a predetermined process based on the digital signals input from the A / D converter 31 and data input via the operation unit 20. For example, the control unit 32 repeats a process of sweeping the wavelength of the fluorescence emitted from the sample M1 at a predetermined sampling interval while fixing the wavelength of the excitation light irradiated to the sample M1, and then moving to the next excitation wavelength, thereby comprehensively measuring the intensity of the fluorescence emitted by the combination of the excitation wavelength and the fluorescence wavelength. In this way, a spectroscopic analysis spectrum (three-dimensional fluorescence spectrum, fluorescence fingerprint) having three components, the excitation wavelength, the fluorescence wavelength, and the fluorescence intensity, is obtained. As described above, the spectroscopic analysis spectrum is specific to the composition of a substance. In the following description, the reference numerals of the sample M1 will be omitted as appropriate.

[0023] FIG. 2A is a diagram showing an example of a spectroscopic analysis spectrum obtained by the spectroscopic analysis system. The vertical axis in Fig. 2A is the wavelength of the excitation light irradiated to the sample (excitation wavelength). The horizontal axis in Fig. 2A is the wavelength of the fluorescence emitted from the sample (fluorescence wavelength). In Fig. 2A, the spectroscopic analysis spectrum is shown by contour lines according to the intensity of the fluorescence emitted from the sample (fluorescence intensity). The non-fluorescent region R1 and the regions R2 and R3 related to scattered light shown in Fig. 2A are not particularly related to fluorescence and contain unnecessary data, so they are excluded from the analysis as explained below.

[0024] The non-fluorescent region R1 shown in Figure 2A is a region where the fluorescent wavelength is shorter than the excitation wavelength. In reality, the energy of the fluorescent light is smaller than the energy of the excitation light, so the phenomenon of the fluorescent wavelength being shorter than the excitation wavelength (i.e., the energy being greater) does not occur. Therefore, the non-fluorescent region R1, which does not meet the definition of fluorescence, is excluded from the analysis.

[0025] Furthermore, excitation light reflected from the sample surface and detected as is is first-order scattered light, and is therefore excluded from the analysis. For example, a region R2 within ±30 [nm] of a line (not shown) where the excitation wavelength and the fluorescence wavelength are equal is excluded from the analysis. A region R3 of higher-order (secondary and third-order) scattered light is also excluded from the analysis. The control unit 32 (see FIG. 1) is responsible for the process of excluding non-fluorescent region R1 and regions R2 and R3 related to scattered light from the analysis.

[0026] FIG. 2B shows the spectroscopic analysis spectrum after a predetermined pre-processing has been performed. In addition, the non-fluorescent region R1 and the regions R2 and R3 (see FIG. 2A) related to scattered light, which are not particularly necessary for analysis, are removed from the spectroscopic analysis spectrum (pre-processed) in FIG. 2B. The control unit 32 (see FIG. 1) converts the pre-processed spectroscopic analysis spectrum into a one-dimensional spectrum, as shown in the next FIG. 2C.

[0027] FIG. 2C is an explanatory diagram of one-dimensional data of the spectroscopic analysis spectrum. The horizontal axis of FIG. 2C is the fluorescence wavelength for each excitation wavelength. That is, the horizontal axis of FIG. 2C is the fluorescence wavelength when one of the multiple excitation wavelengths in the spectroscopic analysis spectrum is focused on. The vertical axis of FIG. 2C is the fluorescence intensity. As shown in FIG. 2C, for example, when the excitation wavelength is 250 [nm], a predetermined spectrum is obtained within the range of fluorescence wavelengths of 285 [nm] to 800 [nm]. Note that, the predetermined spectrum can be obtained in the same manner for other excitation wavelengths. The control unit 32 (see FIG. 1) aligns these data for each excitation wavelength, expands them into vectors, and then performs multivariate analysis. Note that the details of multivariate analysis will be described later.

[0028] FIG. 3 is a functional block diagram of the control unit 32 included in the spectroscopic analysis system. The control unit 32 shown in Fig. 3 includes electronic circuits such as a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and various interfaces, which are not shown in the figure as a hardware configuration. The CPU reads out a program stored in the ROM and loads it in the RAM, and executes various processes. Note that the control unit 32 does not necessarily have to be configured as one device, and may be configured as a plurality of devices connected via signal lines, or may include a server (not shown) connected via a network (not shown).

[0029] 3, the control unit 32 includes a storage unit 321, a calculation unit 322, and a communication interface 323. The storage unit 321 stores data including measurement condition data 321a, analysis condition data 321b, measurement data 321c, analysis data 321d, and a regression model 321e.

[0030] The measurement condition data 321a is data indicating the measurement conditions (see FIG. 5) of the spectroscopic analysis spectrum of the sample. The analysis condition data 321b is data indicating the analysis conditions (see FIG. 5) of the spectroscopic analysis spectrum of the sample. The measurement data 321c is data such as the spectroscopic analysis spectrum obtained by measuring the sample. The analysis data 321d is data indicating the analysis results of the sample. The regression model 321e is a prediction model used in the analysis of the sample. In the following description, the symbol of the regression model 321e will be omitted as appropriate.

[0031] The calculation section 322 shown in FIG. 3 includes a condition setting section 322a, a measurement control section 322b, a wavelength region generation section 322c, a model generation section 322d, a model evaluation section 322e, and a display control section 322f. When predetermined measurement conditions or analysis conditions are input via the operation panel 21 (see FIG. 1), the condition setting unit 322a stores them in the storage unit 321 as measurement condition data 321a or analysis condition data 321b. The measurement control unit 322b measures the spectroscopic analysis spectrum of the sample using the photometer unit 10 (see FIG. 1). After the spectroscopic analysis spectrum is measured, the wavelength region generating unit 322c generates a set of candidates for the wavelength region used to generate the regression model 321e. The wavelength region is specified by each range of the excitation wavelength and the fluorescence wavelength.

[0032] The model generating unit 322d generates a regression model 321e for obtaining a predetermined response variable (such as the composition or concentration of the sample) based on the spectroscopic analysis spectrum. The model evaluation unit 322e evaluates the prediction performance and measurement time of the regression model 321e. The display control unit 322f causes the display unit 22 (see FIG. 1) to display the measurement data 321c, the analysis data 321d, and also the evaluation results of the model evaluation unit 322e. The communication interface 323 inputs and outputs data to and from the A / D converter 31 (see FIG. 1), the operation panel 21 (see FIG. 1), and the display unit 22 (see FIG. 1).

[0033] For example, when performing fluorescence fingerprint analysis of samples transported one after another on a conveyer belt (not shown) in a factory or facility (i.e., performing in-line measurement), if the measurement time is too long, the number of samples (products) processed per unit time will be reduced. In addition, if the measurement is performed focusing on only a specific excitation wavelength or fluorescence wavelength, the measurement time will be short, but there is a possibility that important spectra for determining the composition of the sample and quantifying the concentration will not be obtained. Therefore, in the first embodiment, the user sets an upper limit value for the measurement time when performing fluorescence fingerprint analysis of one sample, and the control unit 32 generates a regression model 321e that can obtain highly accurate analysis results within this measurement time.

[0034] FIG. 4 is a flowchart showing the processing of the control unit of the spectroscopic analysis system (also see FIG. 1 as appropriate). 4 is a process that is performed in advance to generate recommended measurement conditions and a regression model prior to in-line measurement in a factory or facility, for example. The use of the spectroscopic analysis system 100 (see FIG. 1) is not limited to in-line measurement, but may be offline measurement performed outside the production line, and may be used for various purposes such as medical-related or food-related inspection and research, as well as individual experiments.

[0035] In step S101, the control unit 32 sets an upper limit value of the measurement time by the condition setting unit 322a (see FIG. 3). That is, the control unit 32 accepts input of an upper limit value of the measurement time of the spectroscopic analysis spectrum as a user-set condition for measuring the spectroscopic analysis spectrum of the sample based on an operation by the user via the operation panel 21 (input unit) (input process). Note that the "upper limit value of the measurement time" is the upper limit value of the range that the user can tolerate with respect to the time required for measuring one sample.

[0036] Next, in step S102, the control unit 32 sets measurement conditions by the condition setting unit 322a (see FIG. 3). That is, the control unit 32 sets the measurement conditions in the spectroscopic analysis system 100 based on an operation by a user via the operation panel 21.

[0037] FIG. 5 shows an example of a setting screen for measurement conditions, etc. The setting screen of FIG. 5 is displayed on the display unit 22 (see FIG. 1) when the user inputs the upper limit of the measurement time based on the spectroscopic analysis method ("maximum measurement time" in FIG. 5) and predetermined measurement conditions. As described above, the "measurement conditions" shown in Fig. 5 are conditions for measuring the spectroscopic analysis spectrum of a sample. In the example of Fig. 5, the excitation wavelength range, fluorescence wavelength range, excitation light sampling interval, fluorescence sampling interval, excitation light slit width, fluorescence slit width, and wavelength scan speed are set as such "measurement conditions" (S102 in Fig. 4).

[0038] The "excitation wavelength range" shown in Figure 5 is the range of excitation wavelengths when performing fluorescence fingerprint analysis. The "fluorescence wavelength range" is the range of fluorescence wavelengths when performing fluorescence fingerprint analysis. The "excitation light sampling interval" is the sampling interval when the excitation wavelength is swept. The "fluorescence sampling interval" is the sampling interval when the fluorescence wavelength is swept.

[0039] The "excitation light slit width" shown in Fig. 5 is the slit width of the diffraction grating 2a of the excitation side spectroscope 2 (see Fig. 1). The "fluorescence slit width" is the slit width of the diffraction grating 8a of the fluorescence side spectroscope 8 (see Fig. 1). The "wavelength scan speed" is the sweep speed (scan speed) when one of the excitation wavelength and the fluorescence wavelength is fixed and the other is swept.

[0040] As these measurement conditions, for example, the excitation wavelength range and the fluorescence wavelength range may each be set to a range of 250 to 750 [nm] (or a part of that range). In addition, the sampling interval of the excitation wavelength may be set to 10 [nm], the sampling interval of the fluorescence wavelength to 5 [nm], the slit width of the excitation light and the fluorescence to 5 [nm], and the wavelength scan speed to 60000 [nm / min]. Note that the above-mentioned numerical values ​​are merely examples and do not particularly limit the measurement conditions. In addition, a predetermined default value may be displayed as the numerical value of each item of the measurement conditions, and the user may be allowed to change the value of each item from the default value as appropriate.

[0041] The "analysis conditions" shown in FIG. 5 are conditions under which a predetermined analysis is performed based on the spectroscopic analysis spectrum of a sample. The "maximum measurement time" relates to both measurement and analysis, but in the example of FIG. 5, it is displayed under the "analysis conditions". When the "maximum measurement time" is input based on the user's operation via the operation panel 21 (see FIG. 1), the control unit 32 sets this "maximum measurement time" as the upper limit of the measurement time (S101 in FIG. 4). The "maximum measurement time" may be, for example, a value of 10 seconds or less, or may be a predetermined value longer than 10 seconds.

[0042] The "wavelength region selection method" shown in FIG. 5 is a method for selecting a predetermined measurement wavelength region from the "excitation wavelength range" and "fluorescence wavelength range" set by the user. That is, the control unit 32 (see FIG. 1) displays a plurality of candidates on the display unit 22 (display device: see FIG. 1) as a wavelength region selection method for selecting a predetermined measurement wavelength region, and executes a predetermined wavelength region selection method selected from the plurality of candidates by the user through an operation via the operation panel 21 (input unit: see FIG. 1). This increases the user's freedom in setting the wavelength region selection method. Note that a method using random numbers or other well-known methods are used as the wavelength region selection method.

[0043] The "regression method" shown in Fig. 5 is a method used to generate a regression model based on multivariate analysis. That is, the control unit 32 (see Fig. 1) displays a plurality of candidates on the display unit 22 (display device: see Fig. 1) as the regression method of the multivariate analysis used in the analysis of the spectroscopic analysis spectrum, and executes a predetermined regression method selected from the plurality of candidates by a user's operation via the operation panel 21 (input unit: see Fig. 1). Although details will be described later, as the candidate regression method, for example, partial least squares (PLS) and Lasso regression, as well as multiple regression analysis, principal component regression analysis, RF regression, and SVM regression are appropriately used. For the "wavelength region selection method" and "regression method," multiple candidates may be displayed in a pull-down menu, and one may be selected from the multiple candidates. Also, it is not necessary for the user to set all of the measurement conditions and analysis conditions shown in FIG. 5, and only some of those shown in FIG. 5 may be set.

[0044] The "exclusion region" shown in Figure 5 is a region that is not particularly necessary for data analysis, such as the non-fluorescent region R1 (see Figure 2A) and scattered light regions R2 and R3 (see Figure 2A). The "measurement result" shown in Figure 5 is the result of measurement and analysis based on spectroscopic analysis (e.g., fluorescent fingerprint analysis). After the result of measurement and analysis based on spectroscopic analysis is obtained, the specific content of the "measurement result" is displayed (see Figure 6).

[0045] Returning to FIG. 4 again, the explanation will be continued. After setting an upper limit for the measurement time (S101) and further setting the measurement conditions (S102), in step S103, control unit 32 measures fluorescence fingerprint data using measurement control unit 322b. That is, control unit 32 performs fluorescence fingerprint analysis on a predetermined sample based on the measurement conditions set in step S102. Note that in order to distinguish the composition of the sample and quantify the concentration, multiple samples with different concentrations and compositions are prepared, and fluorescence fingerprint data is measured for each sample in sequence. Then, a spectroscopic analysis spectrum (see FIG. 2A) is generated by measuring the fluorescence fingerprint data.

[0046] Next, in step S104, the control unit 32 generates a set of candidates for the wavelength region by the wavelength region generating unit 322c (see FIG. 3). That is, the control unit 32 generates a set of candidates for the wavelength region from all regions specified in the respective ranges of the excitation wavelength and fluorescence wavelength set in the measurement conditions (S102). Here, the "wavelength region" is a region specified in the respective ranges of the excitation wavelength and the fluorescence wavelength (see wavelength regions G3 and G4 in FIG. 7A). For example, the control unit 32 generates a set of candidates for the wavelength region used to generate the regression model based on a predetermined random number. Note that it is preferable that the set of candidates for the wavelength region includes a plurality of wavelength regions. By including a plurality of wavelength regions, it is possible to include a plurality of locations that contribute greatly to the composition discrimination and concentration quantification of a sample without making the size of each wavelength region particularly large.

[0047] In step S105 of Fig. 4, the control unit 32 generates a regression model for each wavelength region by the model generation unit 322d (see Fig. 3). That is, the control unit 32 generates a regression model (prediction model) corresponding to each candidate wavelength region based on the spectroscopic analysis spectra of a plurality of samples having different compositions and concentrations. For example, PLS regression analysis or Lasso regression is used as a multivariate analysis method for generating the regression model.

[0048] Next, in step S106, the control unit 32 determines whether or not there is any in the set of wavelength region candidates whose measurement time is equal to or less than a predetermined upper limit. The method of calculating the measurement time depends on the specific measurement method in the photometer unit 10 (see FIG. 1). For example, in a case where the process of sweeping the fluorescence wavelength while fixing the excitation wavelength, and then sweeping the fluorescence wavelength again after changing the excitation wavelength is completed, is repeated, the control unit 32 calculates the measurement time as follows. That is, the control unit 32 calculates the measurement time based on the sum of the fluorescence wavelength sweep time, the fluorescence wavelength return time, and the excitation wavelength movement time.

[0049] In step S106, if there is no measurement time that is equal to or shorter than the upper limit (S106: No), the process of the control unit 32 returns to step S102. In this case, a predetermined message for prompting the user to change the measurement conditions is displayed on the display unit 22 (see FIG. 1). Then, the measurement conditions are appropriately changed based on the user's operation via the operation panel 21 (see FIG. 1).

[0050] Furthermore, in step S106, if there is any wavelength region in which the measurement time is equal to or less than the upper limit (S106: Yes), the process of the control unit 32 proceeds to step S107. In step S107, the control unit 32 verifies the prediction performance of the regression model by the model evaluation unit 322e (see FIG. 3). Note that the subject of step S107 is the regression model associated with the wavelength region in which the measurement time based on the fluorescence fingerprint analysis is equal to or less than a predetermined upper limit.

[0051] Cross-validation, for example, is used as a method for verifying the predictive performance of a regression model. When performing cross-validation, the control unit 32 divides the learning data (spectroscopic analysis spectra of a plurality of samples) into a plurality of groups. As a specific example, consider here the case where the learning data is divided into five groups (5-fold cross-validation). For example, when there are a total of 20 samples whose concentrations and compositions are known, the control unit 32 divides the spectroscopic analysis spectrum data of the total of 20 samples into a total of five groups of four each.

[0052] In 5-fold cross-validation, the control unit 32 reserves one predetermined group for verifying prediction performance, and generates a new regression model using the remaining four groups. The control unit 32 then generates the regression model a total of five times by sequentially changing the group for verifying prediction performance. The control unit 32 then determines hyperparameters based on the predetermined cross-validation. Note that "hyperparameters" are predetermined parameters for setting the behavior of a machine learning algorithm.

[0053] For example, when PLS regression is used to generate a regression model, the number of PLS ​​components becomes a hyperparameter. For example, the root-mean square error (RMSE) or the mean absolute error (MAE) is used as an evaluation index of prediction performance. As a specific example, when RMSE is used to evaluate prediction performance, the control unit 32 sets the average value of RMSE in five repeated cross-validations as the RMSECV, which is the evaluation index of prediction performance.

[0054] Then, the control unit 32 sets the hyperparameters so that the RMSECV, which is an evaluation index of the prediction performance, is minimized. In this manner, in step S108, the control unit 32 calculates the RMSECV based on the hyperparameters (number of PLS ​​components) optimized under the predetermined measurement conditions and wavelength region. Note that the evaluation index of the prediction performance (e.g., RMSECV) is calculated for each regression model that satisfies the condition in step S106.

[0055] Next, in step S108, the control unit 32 selects the regression model with the highest prediction performance. For example, the control unit 32 selects the regression model with the smallest RMSECV value for the optimized number of PLS ​​components. In step S109, the control unit 32 determines whether the prediction performance satisfies a predetermined target value by the model evaluation unit 322e (see FIG. 3). For example, the control unit 32 determines whether the RMSECV in the regression model selected in step S108 is equal to or smaller than a predetermined value. The predetermined value is a target value of the prediction performance, and is set in advance.

[0056] In step S109, if the predicted performance does not satisfy the target value (S109: No), the process of the control unit 32 returns to step S102. In this case, a message for prompting the user to change the measurement conditions is displayed on the display unit 22 (see FIG. 1). In addition, in step S109, if the predicted performance satisfies the target value (S109: Yes), the process of the control unit 32 proceeds to step S110.

[0057] In step S110, the control unit 32 displays the results of the measurement and analysis by the display control unit 322f (see FIG. 3). That is, the control unit 32 derives predetermined recommended measurement conditions that satisfy the user-set conditions regarding the measurement time, and displays the recommended measurement conditions on the display unit 22 (display device: see FIG. 1) (display process). This allows the user to understand the recommended measurement conditions for performing measurements with relatively high prediction performance while keeping the measurement time below a predetermined upper limit value. Such measurement and analysis results are used when the user performs in-line measurements of samples in factories and facilities. After performing the process of step S110, the control unit 32 ends the series of processes (END).

[0058] Figure 6 shows an example of the measurement and analysis results displayed on the screen. In Fig. 6, the recommended measurement conditions for in-line measurement, etc. are displayed on the display unit 22 (see Fig. 1) including the excitation light sampling interval and fluorescence sampling interval in the measurement of the spectroscopic analysis spectrum, as well as the excitation light slit width, fluorescence slit width, excitation wavelength range, wavelength scan speed, and measurement time. These recommended measurement conditions are the measurement conditions when using the regression model (S106: Yes, S108 in Fig. 4) with the highest prediction performance among those whose measurement time satisfies a predetermined upper limit value. Note that, although the numerical values ​​in each column of the recommended measurement conditions are not particularly displayed in Fig. 6, in reality, specific numerical values ​​are displayed. The recommended measurement conditions may also include the fluorescence wavelength range.

[0059] In the example of FIG. 6, the control unit 32 causes the display unit 22 (display device: see FIG. 1) to display the recommended measurement conditions, and also causes the display unit 22 to display the spectroscopic analysis spectrum based on the fluorescence fingerprint analysis. The spectroscopic analysis spectrum displayed on the display unit 22 shows measurement wavelength regions G1 and G2 specified by the range of excitation wavelengths and the range of fluorescence wavelengths as the wavelength range of the recommended measurement conditions. For example, the control unit 32 may highlight the measurement wavelength regions G1 and G2 by displaying them in a color or pattern different from other regions, or by displaying the boundaries of the measurement wavelength regions with a thick frame line. This allows the user to know at a glance which region should be used for measuring the spectroscopic analysis spectrum.

[0060] Moreover, as shown in Fig. 6, it is preferable that both the recommended measurement conditions and the spectroscopic analysis spectrum are displayed on one screen in the display unit 22 (display device: see Fig. 1). This allows the user to check the values ​​of each item of the recommended measurement conditions while also checking the measurement wavelength ranges G1 and G2 in the spectroscopic analysis spectrum on the same screen. This improves visibility when the user checks the recommended measurement conditions and the measurement wavelength ranges G1 and G2.

[0061] Also, the number of measurement wavelength regions G1, G2 (two in the example of FIG. 6) may be set based on a user's operation via the operation panel 21 (input unit: see FIG. 1). This increases the user's freedom when causing the control unit 32 to generate the measurement wavelength regions G1, G2. Note that the range of the number of measurement wavelength regions may be set based on a user's operation via the operation panel 21. For example, when the number of measurement wavelength regions is set to two or more and five or less by the user's operation of the operation panel 21, the wavelength region generation unit 322c (see FIG. 3) generates a set of wavelength region candidates for each of the cases where the number of measurement wavelength regions is two, three, four, and five.

[0062] In order to create a regression model with high prediction performance, it is important to perform verification for many wavelength regions. However, if wavelength regions are generated by a brute force method, it may take a long time to improve the prediction performance. Therefore, for example, when optimizing the wavelength region, the control unit 32 may perform GAWLSPLS (Genetic Algorithm based Wavelength Selection Partial Least Squares) based on a genetic algorithm. The following description of GAWLSPLS corresponds to the processing of steps S104 to S108 in FIG. 4.

[0063] FIG. 7A is an explanatory diagram showing how the range of the target wavelength region is changed when the GAWLSPLS method is used. In the GAWLSPLS method, the control unit 32 (see FIG. 1) first specifies the number of wavelength regions to be selected. In the example of FIG. 7A, two wavelength regions G3 and G4 are specified. These wavelength regions G3 and G4 are defined by the starting excitation wavelength, starting fluorescence wavelength, and size of each region (length of excitation wavelength range, length of fluorescence wavelength range) when sweeping the excitation wavelength / fluorescence wavelength. In the genetic algorithm, the starting excitation wavelength and region size of each wavelength region G3 and G4 are assigned to a specified chromosome.

[0064] Then, the wavelength region to be used for analysis is derived from each chromosome. Based on these wavelength regions, for example, a prediction model (calibration model) for the concentration (objective variable) of a predetermined substance contained in a sample is constructed. In GAWLSPLS, for example, the above-mentioned RMSECV is used as an index of the fitness of the genetic algorithm. Then, an analysis wavelength region (wavelength region to be analyzed) is determined from the chromosome, and the fitness for this analysis wavelength region is calculated.

[0065] In the GAWLSPLS method, a suitable chromosome is selected from the viewpoint of minimizing RMSECV, which is an index of fitness based on a genetic algorithm. Note that a constraint is set such that the time required for measuring the wavelength region is equal to or less than a predetermined upper limit (maximum measurement time). The control unit 32 (see FIG. 1) generates chromosomes (initial population) of a predetermined number of wavelength regions, and calculates and evaluates the fitness. Then, the control unit 32 selects from these chromosomes those whose measurement time is equal to or less than the predetermined upper limit.

[0066] When the predetermined convergence condition specified by the user is satisfied, the chromosome with the lowest RMSECV, which is an index of fitness, in the population becomes the solution. When the predetermined convergence condition is not satisfied, the control unit 32 generates a next-generation population by selecting, crossing over, and mutating chromosomes from the population, and evaluates this population. The control unit 32 repeats such a series of processes until the predetermined convergence condition is satisfied, thereby deriving a suitable analysis wavelength region. In addition, the control unit 32 may obtain multiple analysis wavelength regions by appropriately changing the number of wavelength regions and the method of generating random numbers.

[0067] FIG. 7B is an explanatory diagram showing measurement wavelength regions G5 and G6 set based on the GAWLSPLS method. In the example of Fig. 7B, two trapezoidal measurement wavelength ranges G5 and G6 are set as wavelength ranges used for fluorescence fingerprint analysis such as in-line measurement. Note that the wavelength range specified by the range of excitation wavelength and fluorescence wavelength does not necessarily have to be rectangular, and may be trapezoidal as shown in Fig. 7B. By using the above-mentioned GAWLSPLS method, it is possible to efficiently specify a wavelength range that satisfies a predetermined condition based on a genetic algorithm.

[0068] Furthermore, when measurements are performed under a plurality of conditions, it may take a long time to measure the spectroscopic analysis spectrum. Therefore, as described below, the control unit 32 may use a spectroscopic analysis spectrum obtained under a specific measurement condition to generate a pseudo spectroscopic analysis spectrum under a different measurement condition. An example of an experimental result using such a method is shown below.

[0069] In the experiment, the vitamin E concentration in edible oil (15 samples) was used as the objective variable, and measurements were performed under the following conditions: excitation wavelength range 250-450 [nm], excitation wavelength sampling interval 1 [nm], fluorescence wavelength range 250-450 [nm], and fluorescence wavelength sampling interval 2 [nm]. Measurement conditions included the above-mentioned 1 [nm], as well as 2 [nm] and 3 [nm], for a total of three different excitation wavelength sampling intervals. The GAWLSPLS method was used to optimize the excitation wavelength and fluorescence wavelength regions. In addition, the upper limit of the measurement time (maximum measurement time) was set to 50 seconds. When the excitation wavelength sampling interval was expanded, the fluorescence intensity was integrated (i.e., the sum of the fluorescence intensity was taken) to generate a spectroscopic analysis spectrum.

[0070] FIG. 8 is an explanatory diagram showing an example in which the fluorescence wavelength is fixed and the excitation wavelength is swept in a predetermined manner. For example, consider a case where data is acquired at sampling intervals of 1 [nm], such as 250 [nm], 251 [nm], 252 [nm], ... as the excitation wavelength sampling during measurement, as shown in Figure 8. By utilizing the data acquired by sampling the excitation wavelength every 1 [nm] in this way, it is also possible to generate a pseudo spectroscopic analysis spectrum when the excitation wavelength sampling is set to 3 [nm].

[0071] One method for generating a pseudo-spectroscopic spectrum is to not use data with excitation wavelengths of 251[nm] or 252[nm]. In other words, data with an excitation wavelength of 250[nm]+3k (k is an integer) is used, while data with 251[nm]+3k and 252[nm]+3k are not used in the analysis. In this way, the measurement time can be shortened by thinning out the data, so to speak.

[0072] Another method is to integrate the fluorescence intensities when the excitation wavelengths are, for example, 250 [nm], 251 [nm], and 252 [nm]. In other words, the sum of the fluorescence intensities at the excitation wavelengths of 250 [nm] + 3k (k is an integer), 251 [nm] + 3k, and 252 [nm] + 3k may be taken and associated with, for example, 251 [nm] + 3k. In this way, the measurement time can be shortened by associating the sum of the fluorescence intensities at three adjacent excitation wavelengths at a predetermined sampling interval with one of the three excitation wavelengths (or the average value of the three excitation wavelengths). Another method is to integrate the fluorescence intensities when the excitation wavelengths are 250 [nm] and 252 [nm]. In other words, the sum of the fluorescence intensities when the excitation wavelengths are 250 [nm] + 3k (k is an integer) and 251 [nm] + 3k may be calculated and associated with 250 [nm] + 3k. By using such a method, it is also possible to generate a pseudo-spectroscopic spectrum. The pseudo-spectroscopic spectrum is treated as separate data from the original spectroscopic spectrum measured at a sampling interval of 1 nm. The same can be said when sweeping the fluorescence wavelength instead of the excitation wavelength.

[0073] In this way, when measuring the spectroscopic analysis spectrum based on the fluorescence fingerprint analysis, the control unit 32 fixes one of the excitation wavelength and the fluorescence wavelength, and sweeps the other wavelength at a predetermined sampling interval. In this case, the control unit 32 generates data for each of the n other wavelengths (n is a natural number) swept at the predetermined sampling interval, in which a value obtained by summing some or all of the n fluorescence intensities corresponding one-to-one to the n other wavelengths corresponds to one of the n other wavelengths. The control unit 32 may generate data in which the value obtained by summing the above-mentioned sum corresponds to an average value of the n other wavelengths. Then, the control unit 32 generates a new pseudo spectroscopic analysis spectrum based on the generated data, and generates a prediction model for analyzing the spectroscopic analysis spectrum based on the pseudo spectroscopic analysis spectrum.

[0074] In the experiment, we adopted a method of integrating the fluorescence intensities at excitation wavelengths of 250[nm], 251[nm], and 252[nm] (i.e., taking the sum of the fluorescence intensities). This improved the S / N ratio in the fluorescence fingerprint analysis. This is because integrating the fluorescence intensities produced the same effect as actually obtaining measurement results when the amount of fluorescent light is increased.

[0075] When integrating (i.e., adding up) the fluorescence intensities at two excitation wavelengths with different values, the control unit 32 may double the slit width of the excitation light. When integrating the fluorescence intensities at three excitation wavelengths, the control unit 32 may triple the slit width of the excitation light. This can improve the S / N ratio. Additionally, the control unit 32 may appropriately adjust the scanning speed of the fluorescence wavelengths, etc.

[0076] FIG. 9 is an explanatory diagram showing the experimental results of the fluorescent fingerprint analysis. In the example of Figure 9, the RMSECV, which is an index of prediction performance, was smallest when the sampling interval of the excitation wavelength was 2 [nm], and the measurement time in this case was 49.5 seconds. In this way, by setting the sampling interval of the excitation wavelength to 2 [nm], it was possible to identify recommended measurement conditions with relatively high prediction accuracy in a measurement time below a predetermined upper limit value (maximum measurement time). In addition, when the sampling interval of the excitation wavelength was expanded to 3 [nm], although the RMSECV, which is an index of prediction performance, was slightly inferior, the measurement time was 15.3 seconds, and the same level of prediction accuracy was obtained in a shorter time.

[0077] <Effects> According to the first embodiment, the control unit 32 derives recommended measurement conditions and regression models for performing highly accurate substance discrimination and concentration quantification within a time equal to or less than the upper limit of the measurement time according to the needs of the user. This allows highly accurate substance discrimination and concentration quantification even in use cases where measurement time constraints are important, such as in-line measurement for industrial processes. It also becomes possible to appropriately set the upper limit of the measurement time according to the specified conditions required in the industrial process. In this way, according to the first embodiment, it is possible to provide a spectroscopic analysis system 100 that allows appropriate measurement even when there are constraints on the measurement time, etc.

[0078] Second Embodiment The second embodiment differs from the first embodiment in that, when the measurement conditions are optimized based on the spectroscopic analysis spectrum, the learning data is measured again under the optimized measurement conditions, and a regression model is generated using the remeasured learning data. Note that the rest (such as the configuration of the spectroscopic analysis system 100: see FIG. 1) is the same as the first embodiment. Therefore, only the parts that are different from the first embodiment will be described, and the description of the overlapping parts will be omitted.

[0079] 10A and 10B are flowcharts showing the processing of the control unit 32 of the spectroscopic analysis system according to the second embodiment (also see FIG. 1 as appropriate). Note that steps S101, S102, and S104 to S109 in Fig. 10A are the same as those in the first embodiment (see Fig. 4), and therefore will not be described. In step S103a shown in Fig. 10A, control unit 32 not only actually measures the fluorescence fingerprint data (spectroscopic analysis spectrum) of a predetermined sample, but also calculates pseudo fluorescence fingerprint data based on this fluorescence fingerprint data.

[0080] For example, the control unit 32 calculates the sum of the fluorescence intensities at excitation wavelengths of 250[nm]+3k (k is an integer), 251[nm]+3k, and 252[nm]+3k, and associates the sum of the fluorescence intensities with the excitation wavelength of 251[nm]+3k. Note that the method of calculating the pseudo-spectroscopic analysis spectrum is the same as that described in the first embodiment, and therefore will not be described here. Furthermore, if the predicted performance satisfies the predetermined target value in step S109 (S109: Yes), the process of the control unit 32 proceeds to step S120 in FIG. 10B.

[0081] 10B, the control unit 32 measures the fluorescence fingerprint data (spectroscopic analysis spectrum) again under the optimized measurement conditions. Note that in the process of step S121, the spectroscopic analysis spectrum of the sample is actually measured. In step S122, the control unit 32 generates a regression model of the wavelength region. This "wavelength region" is a predetermined wavelength region associated with a predetermined regression model whose prediction performance satisfies a target value (S109: Yes in FIG. 10A). In this manner, the control unit 32 actually measures the spectroscopic analysis spectrum under recommended measurement conditions based on the pseudo spectroscopic analysis spectrum (S121), and generates again a regression model (prediction model) for analyzing the spectroscopic analysis spectrum based on the spectroscopic analysis spectrum obtained by this measurement (S122). By the control unit 32 generating the regression model again, it is possible to reduce the influence of errors caused by using the pseudo spectroscopic analysis spectrum in step S103a (see FIG. 10A), and obtain a regression model with high prediction performance.

[0082] Next, in step S123, the control unit 32 verifies the prediction performance of the regression model. Note that the method of verifying the prediction performance of the regression model is similar to that in step S107 (see FIG. 10A), and therefore the description thereof will be omitted. In step S124, the control unit 32 determines whether the prediction performance of the regression model meets a predetermined target value. In step S124, if the prediction performance does not meet the target value (S124: No), the process of the control unit 32 returns to step S102 (see FIG. 10A). In this case, a message for prompting the user to change the measurement conditions is displayed on the display unit 22 (see FIG. 1).

[0083] Furthermore, in step S124, if the predicted performance satisfies the predetermined target value (S124: Yes), the process of the control unit 32 proceeds to step S125. In step S125, the control unit 32 causes the measurement results and analysis results to be displayed on the display unit 22. After performing the process of step S125, the control unit 32 ends the series of processes (END).

[0084] <Effects> According to the second embodiment, a pseudo spectroscopic analysis spectrum is generated based on a predetermined mathematical process. Therefore, it is not necessary for an inspector to measure a spectroscopic analysis spectrum under various measurement conditions, and therefore the workload of the inspector can be reduced and the time required to identify a regression model with high predictive performance can be shortened. In addition, the control unit 32 measures the spectroscopic analysis spectrum again under optimized measurement conditions and generates a regression model based on the measurement results. As a result, even when a pseudo spectroscopic analysis spectrum is used, the prediction accuracy of the regression model can be sufficiently ensured.

[0085] Third Embodiment The third embodiment differs from the first embodiment in that the sampling intervals and the like under the recommended measurement conditions are different for a plurality of wavelength regions. Note that the rest (such as the configuration of the spectroscopic analysis system 100: see FIG. 1) is similar to the first embodiment. Therefore, only the parts that differ from the first embodiment will be described, and the description of the overlapping parts will be omitted.

[0086] The third embodiment will be described with reference to FIG. 6. For example, it is desirable to set the sampling interval of the excitation wavelength and the sampling interval of the fluorescence wavelength for each of the multiple measurement wavelength regions G1 and G2. As a specific example, by setting a high resolution in a predetermined measurement wavelength region G1 in the spectroscopic analysis spectrum, data on the detailed shape of the spectroscopic analysis spectrum can be obtained. This can improve the accuracy when the control unit 32 performs composition discrimination and concentration quantification of the sample. In addition, for another measurement wavelength region G2 in the spectroscopic analysis spectrum, information obtained by integrating the peak intensity may be important. In such a case, since integration takes time, the control unit 32 can obtain high prediction performance in a relatively short time by lowering the resolution. In this way, the control unit 32 may change the measurement conditions according to the characteristics of each of the multiple regions included in the spectroscopic analysis spectrum.

[0087] In this way, it is preferable that at least one of the excitation wavelengths and the fluorescence wavelengths included in the predetermined recommended measurement conditions have different sampling intervals in the multiple measurement wavelength regions. In addition, the sampling interval of at least one of the excitation wavelengths and the fluorescence wavelengths in the multiple measurement wavelength regions is set based on, for example, a genetic algorithm. Note that an inspector may set the sampling intervals of the excitation wavelength and the fluorescence wavelength in the measurement wavelength regions G1 and G2 based on past experimental data.

[0088] <Effects> According to the third embodiment, the control unit 32 sets different measurement conditions based on the characteristics of a plurality of measurement wavelength regions G1, G2 specified in each range of the excitation wavelength and the fluorescence wavelength, thereby enabling the composition determination and concentration quantification of the sample to be performed with high accuracy.

[0089] Fourth Embodiment The fourth embodiment differs from the first embodiment in that a predetermined evaluation index different from RMSECV is used to avoid so-called overfitting (overlearning). The rest is the same as the first embodiment. Therefore, only the parts that are different from the first embodiment will be described, and the description of the overlapping parts will be omitted.

[0090] The fourth embodiment will be described with reference to Fig. 1. In the first embodiment described above, the case where RMSECV is used as an index of prediction performance has been described, but when RMSECV is minimized by regression analysis such as PLS regression, overfitting (overlearning) may occur. Therefore, in order to suppress excessive adaptation to the spectroscopic analysis spectrum data used when creating the prediction model and to improve generalization performance, the control unit 32 may perform the following processing.

[0091] For example, in the GAWLSPLS method, as a method for dealing with overfitting when selecting a suitable wavelength region from among multiple analysis wavelength regions, a prediction model may be created using an index instead of RMSECV. Specifically, overfitting can be suppressed by using the following evaluation index. In the following formula (1), j is the number of PLS ​​components, B2 is the Euclidean norm of the regression coefficient vector, and b is the regression coefficient vector. In addition, DW in formula (2) is the first derivative of the normalized regression coefficient vector. In addition, J in formula (3) is the Euclidean norm of the change in the regression coefficient.

[0092]

number

[0093]

number

[0094]

number

[0095] For example, when a noise component is included in the regression coefficient, the sum of the absolute values ​​of the regression coefficient increases, and the value of each index (B2, DW, J) increases. Therefore, each of these indexes (B2, DW, J) may be appropriately used as an evaluation index indicating the complexity of the regression coefficient vector. For example, even if the RMSECV is relatively small, it is desirable to penalize it when the complexity of the prediction model is high. Specifically, in order to improve the prediction performance for an unknown model, it is desirable for the values ​​of each index (B2, DW, J) to be small. The smaller the RMSECV and the smaller the indexes (B2, DW, J), the higher the prediction performance for an unknown sample.

[0096] Since RMSECV and each index (B2, DW, J) have different units, the generalization performance of the regression model can be improved by determining the number of PLS ​​components C1, which will be explained next. Note that j in formula (4) is the number of PLS ​​components, and RMSECV min is the minimum value of RMSECV, and RMSECV max is the maximum value of RMSECV. In addition, I in equation (4) is one of B2, DW, and J, and Ij is the value when the number of PLS ​​components is j. In addition, I min is the minimum value of I, and I max is the maximum value of I. The first term on the right hand side of equation (4) is the value of RMSECV normalized by the maximum-minimum value. The second term on the right hand side of equation (4) is the value of I normalized by the maximum-minimum value.

[0097]

number

[0098] In the fourth embodiment, the above-mentioned B2 (Euclidean norm of regression coefficient vector) is used as I included in formula (4), and the GAWLSPLS method is applied with C1 as an index of prediction accuracy. In this way, the control unit 32 (see FIG. 1) searches for a measurement wavelength region based on a genetic algorithm, and in the genetic algorithm, for example, C1 is assigned as a predetermined penalty function indicating the degree of overlearning to RMSECV, which is an index of the fitness of a prediction model for analyzing a spectroscopic analysis spectrum. Then, the control unit 32 evaluates the prediction model based on the penalty function. As described above, RMSECV is the average value of the root mean square error (RMSE) in cross-validation.

[0099] <Experimental Results> As an example, we will explain the verification of quantitative determination of glucose concentration in culture medium by near-infrared spectroscopy. CRL-12445 (ATCC) was used for CHO cells, and DMEM-low glucose (Sigma-Aldrich) was used for the medium. After CHO cells were dispersed in the medium, the cell count was measured using an automatic fluorescent cell counter LUNA-FL (Logos Biosystems) to determine the cell count at 1 x 10 5 ~3×10 5 This culture solution was seeded in a spinner flask, and the culture solution was stirred with a stirrer and stored in an incubator (temperature: 37°C, CO2 concentration: 5%, air concentration: 95%).

[0100] The incubator used was a Personal CO2 MULTIGAS INCUBATOR APM50DR (manufactured by Astex). Cultivation was performed four times to create samples for constructing the calibration model (22 samples). In addition, cultivation was performed six times to create samples for verifying the predictive performance of the created prediction model (calibration model), and a total of 23 samples were prepared.

[0101] In addition to the actual culture solution samples, the culture solution before and after culture, and glucose were mixed to create mock culture solution samples, which were used for the measurements. The culture solution after culture was used to create mock culture solution samples after 7 days from the start of culture, after removing cells and other substances with a 0.2 μm filter. A total of 102 samples were prepared so that the glucose concentration of these mixed solutions was in the range of 0.5 to 0.6 g / L and the total glucose concentration was within the range of 0 to 8 g / L.

[0102] In addition, the simulated culture fluid was processed as data for constructing the calibration model in a lump sum, and transfer learning was performed using the simulated culture fluid data. The Frustratingly Easy Domain Adaption method was used as the transfer learning method. As a preprocessing method, the number of wavelength points when fitting the Savitzky-Golay method was set to 21, the degree of the polynomial for fitting was set to 2, and the degree of differentiation afterwards was set to 1.

[0103] A prediction model was constructed based on the thus obtained spectral data (spectroscopic analysis spectrum) and the glucose concentration measured by the enzyme electrode method. The GAWLSPLS method was used to select the wavelength region. The maximum measurement time was set to 600 seconds. Two indices of fitness for the genetic algorithm were used: RMSECV and C1. The number of wavelength regions was set to a range of 1 to 10, and 10 repeated calculations were performed for each number of regions, to obtain data for a total of 100 wavelength regions. The glucose concentration of the verification samples (23 samples) was predicted using the prediction model based on the data for these 100 wavelength regions, and the RMSEP (Root-Mean Square Error Prediction) was calculated from the prediction results. The results are shown in Figures 11A and 11B.

[0104] FIG. 11A shows the experimental results when RMSECV is used as the goodness of fit index. In Fig. 11A, the horizontal axis is RMSECV and the vertical axis is RMSEP. When RMSECV is used as an index of goodness of fit, as shown in Fig. 11A, there is no particular tendency for RMSEP to become smaller when the RMSECV value becomes smaller.

[0105] FIG. 11B shows the experimental results when C1 is used as the index of goodness of fit. The horizontal and vertical axes in FIG. 11B are the same as those in FIG. 11A. When C1 was used as the goodness of fit index, as shown in Figure 11B, it was confirmed that as RMSECV became smaller, RMSEP also tended to become smaller. In other words, by using C1 as the goodness of fit index, overfitting was suppressed and the prediction performance for the validation sample was improved.

[0106] Next, under each condition (fit index: RMSECV or C1), a prediction was made for the validation sample using the analytical wavelength range in which RMSECV was minimized. In FIG. 11A, the data with the smallest RMSECV is indicated by a circle Q1. Similarly, in FIG. 11B, the data with the smallest RMSECV is indicated by a circle Q2. Thus, the prediction results in the analytical wavelength range in which RMSECV was minimized are shown in FIG. 12A and FIG. 12B.

[0107] FIG. 12A is a diagram showing prediction results of validation samples when RMSECV is used as an index of goodness of fit. The vertical axis of FIG. 12A is the predicted value of the glucose concentration of the verification sample. The horizontal axis of FIG. 12A is the actual measured value of the glucose concentration measured by the enzyme electrode method. FIG. 12A also shows a line L1 where the predicted value and the actual measured value are equal. The fact that data points are concentrated on this line indicates that the prediction performance for the verification sample is high.

[0108] FIG. 12B is a diagram showing prediction results of a validation sample when C1 is used as the index of goodness of fit. The vertical and horizontal axes in FIG. 12B are the same as those in FIG. 12A. FIG. 12B also shows a straight line L2 where the predicted value and the measured value are equal. As shown in FIG. 12B, when C1 is used as the index of goodness of fit, the prediction performance is improved compared to when RMSECV is used as the index of goodness of fit and transfer learning is not performed (see FIG. 12B). The RMSEP is improved from 0.46 [g / L] to 0.33 [g / L] when C1 is used as the index of goodness of fit. In addition, when the index of goodness of fit is C1 and transfer learning is performed, the RMSEP is 0.26 [g / L]. By changing the goodness of fit and applying transfer learning at the same time, the prediction accuracy can be further improved.

[0109] <Effects> According to the fourth embodiment, overlearning can be suppressed by using the C1 value or the like as an index of goodness of fit, and the prediction performance of the prediction model can be improved. Therefore, composition discrimination and concentration quantification of a sample can be performed with high accuracy.

[0110] <<Variations>> Although the spectroscopic analysis system 100 according to the present invention has been described in each embodiment, the present invention is not limited to these descriptions and various modifications can be made. For example, in the first embodiment, the case where the upper limit of the measurement time of the spectroscopic analysis spectrum is set by the user has been described, but this is not limiting. That is, the lower limit of the measurement accuracy of the spectroscopic analysis spectrum may be set by the user. As a processing flow of the control unit 32, as a processing replacing step S101 in FIG. 4, the control unit 32 sets the lower limit of the measurement accuracy in response to an input operation by the user. As a processing replacing step S106, the control unit 32 judges whether or not there is a measurement accuracy that is equal to or greater than the lower limit. By performing such processing, the user can set the measurement accuracy when measuring the spectroscopic analysis spectrum, which improves usability for the user. As a measurement accuracy, when RMSECV, which has a smaller value, is used, the maximum value in the allowable range of RMSECV is used as the "lower limit of the measurement accuracy". Also, both the upper limit of the measurement time and the lower limit of the measurement accuracy may be set by the user. That is, as a user-set condition for measuring the spectroscopic analysis spectrum of a sample, input of at least one of the upper limit of the measurement time of the spectroscopic analysis spectrum and the lower limit of the measurement accuracy may be accepted by operating the operation panel 21 (input unit: see FIG. 1). This makes it possible to search for recommended measurement conditions desired by the user in terms of either or both of the measurement time and the measurement accuracy. Note that the setting of the lower limit of the measurement accuracy can also be applied to the second to fourth embodiments.

[0111] In each embodiment, when the measurement conditions are set in step S102 (see FIG. 4), a case has been described in which one predetermined value is input for each of the items of the excitation light sampling interval, the fluorescence sampling interval, the excitation light slit width, the fluorescence slit width, and the wavelength scan speed, as shown in FIG. 5, but this is not limiting. That is, the user may input multiple candidate values ​​or predetermined numerical ranges for some or all of the measurement conditions. Then, the control unit 32 may set an optimal value for each item based on a genetic algorithm or the like.

[0112] In addition, in each embodiment, a case where fluorescence fingerprint analysis is used as an example of a spectroscopic analysis method has been described, but the present invention is not limited to this. For example, absorptiometry (also called absorption spectroscopy), which is one of the spectroscopic analysis methods, may be used. When absorptiometry is used, the concentration of a predetermined substance is quantified by measuring the spectrum of light absorbed by the sample out of the light irradiated onto the sample. Note that in absorptiometry, the "light used to measure the spectroscopic analysis spectrum" is the light absorbed by the sample.

[0113] In each embodiment, as shown in Fig. 6, the recommended measurement conditions include the excitation light sampling interval and the fluorescence sampling interval, as well as the excitation light slit width, the fluorescence slit width, the excitation wavelength range, the fluorescence wavelength range, and the wavelength scan speed. However, the recommended measurement conditions are not limited to this. In other words, the recommended measurement conditions may be at least one of the wavelength range of light used in measuring the spectroscopic analysis spectrum, the sampling interval of the wavelength of light, the slit width of the diffraction grating of the spectrometer that disperses the light (for example, the excitation side spectrometer 2 or the fluorescence side spectrometer 8: see Fig. 1), and the sweep speed (scan speed) of the wavelength of light. Even in such a case, the control unit 32 can search for a predetermined recommended measurement condition so that a predetermined measurement time and measurement accuracy are satisfied.

[0114] Also, for example, the control unit 32 may store a plurality of prediction models in the storage unit 321 (see FIG. 3) and use different prediction models depending on the state of the process including the in-line measurement. That is, when the control unit 32 performs in-line measurement of a sample in an industrial plant, the control unit 32 may switch the prediction model actually used for analyzing the spectroscopic analysis spectrum of the sample from among the plurality of prediction models depending on the state of the industrial plant. Here, the measurement times of the spectroscopic analysis spectrum in the measurement wavelength ranges that correspond one-to-one to the plurality of prediction models are different from each other. Also, the "industrial plant" is not limited to a factory, but includes various facilities such as research facilities. For example, when an industrial process including in-line measurement is operating steadily and stably, the control unit 32 sets the time required for measuring one sample to a relatively long predetermined time. Also, when it is detected that the industrial process is in an abnormal state, the control unit 32 shortens the time required for measuring one sample in order to transition the industrial process to a steady and stable state. In this way, the control unit 32 can appropriately control the industrial process including in-line measurement by using different prediction models depending on the state of the industrial process.

[0115] In the first embodiment, the control unit 32 determines whether or not there is a spectroscopic analysis spectrum whose measurement time is equal to or less than the upper limit (S106 in FIG. 4), and then selects the regression model with the highest prediction performance (S108). However, this is not limited to the above. For example, the control unit 32 may determine the measurement time in descending order of prediction performance among the regression models of each wavelength region. This type of processing also produces the same effect.

[0116] In the first embodiment, the photometer section 10 (see FIG. 1) includes the excitation side filter 5 (see FIG. 1) and the fluorescence side filter 7 (see FIG. 1). However, these may be omitted as appropriate. In the first embodiment, the processing result of the control unit 32 (see FIG. 1) is displayed on the display unit 22 (see FIG. 1), but the present invention is not limited to this. For example, the processing result of the control unit 32 may be transmitted to a mobile terminal (not shown) such as a mobile phone, smartphone, or tablet. In this case, the display of the mobile terminal functions as the display unit. In the first embodiment, the case where the measurement time and the like are input by the user operating the input unit (see FIG. 1) has been described, but this is not limited thereto. For example, the measurement time and the like may be input based on the user operating a mobile terminal (see FIG. 1). In this case, the buttons and touch panel of the mobile terminal function as the "input unit." The same can be said about the second to fourth embodiments.

[0117] Furthermore, all or part of the programs for implementing the functions (analysis methods) of the spectroscopic analysis system 100 and the like described in each embodiment may be executed by one or more computers such as a server (not shown). The programs can be provided via a communication line, or can be written onto a recording medium such as a CD-ROM and distributed.

[0118] In addition, each embodiment is described in detail to explain the present invention in an easily understandable manner, and is not necessarily limited to having all of the configurations described. In addition, it is possible to add, delete, or replace some of the configurations of the embodiments with other configurations. In addition, the above-mentioned mechanisms and configurations are those that are considered necessary for explanation, and do not necessarily show all of the mechanisms and configurations in the product. [Explanation of symbols]

[0119] 1 light source 2 Excitation side spectroscope (spectroscope) 2a Diffraction grating 3 Beam splitter 4 Monitor Detector 5. Excitation Filter 6 Sample placement area 7 Fluorescence side filter 8 Fluorescence side spectroscope (spectroscope) 8a Diffraction grating 9. Detector 10 Photometer section 21 Operation panel (input section) 22 Display section (display device) 32 Control section 321e Regression Model (Prediction Model) 100 Spectroscopic Analysis System G1,G2 Measurement wavelength range M1 sample

Claims

1. an input unit that receives an input of at least one of an upper limit value of a measurement time of the spectroscopic analysis spectrum and a lower limit value of a measurement accuracy as a user-set condition related to the measurement of the spectroscopic analysis spectrum of a sample; a control unit that derives predetermined recommended measurement conditions that satisfy the user-set conditions and displays the recommended measurement conditions on a display device; a wavelength range of light used in measuring the spectroscopic analysis spectrum, a sampling interval of the wavelength of the light, a slit width of a diffraction grating of a spectrometer that disperses the light, and a sweep speed of the wavelength of the light.

2. the control unit causes the display device to display the recommended measurement conditions and also causes the display device to display the spectroscopic analysis spectrum based on the fluorescence fingerprint analysis; The spectroscopic analysis spectrum displayed on the display device shows a measurement wavelength region specified by an excitation wavelength range and a fluorescence wavelength range as the wavelength range of the recommended measurement conditions.

2. The spectroscopic analysis system according to claim 1 .

3. On the display device, both the recommended measurement conditions and the spectroscopic analysis spectrum are displayed on one screen.

3. The spectroscopic analysis system according to claim 2 .

4. The number of measurement wavelength regions is set based on an operation by a user via the input unit. Or, The range of the number of measurement wavelength regions is set based on an operation by a user via the input unit.

3. The spectroscopic analysis system according to claim 2 .

5. The control unit causes the display device to display a plurality of candidates as a wavelength region selection method that is a method for selecting the measurement wavelength region, and executes a predetermined wavelength region selection method that is selected from the plurality of candidates by a user's operation via the input unit.

3. The spectroscopic analysis system according to claim 2 .

6. The control unit causes a plurality of candidates for a regression method of multivariate analysis to be used in the analysis of the spectroscopic analysis spectrum to be displayed on the display device, and executes a predetermined regression method selected from the plurality of candidates by a user's operation via the input unit.

2. The spectroscopic analysis system according to claim 1 .

7. When measuring the spectroscopic analysis spectrum based on a fluorescence fingerprint analysis, the control unit, when one of the excitation wavelength and the fluorescence wavelength is fixed and the other wavelength is swept at a predetermined sampling interval, generates a new pseudo spectroscopic analysis spectrum based on data in which a value obtained by summing some or all of n fluorescence intensities corresponding one-to-one to the n other wavelengths for each of the n other wavelengths (n is a natural number) swept at the predetermined sampling interval is associated with any of the n other wavelengths, or data in which the value obtained by summing is associated with an average value of the n other wavelengths, and generates a prediction model for analysis of the spectroscopic analysis spectrum based on the pseudo spectroscopic analysis spectrum.

2. The spectroscopic analysis system according to claim 1 .

8. The control unit actually measures a spectroscopic analysis spectrum under the recommended measurement conditions based on the pseudo spectroscopic analysis spectrum, and generates a prediction model for analysis again based on the analysis spectrum obtained by the measurement.

8. The spectroscopic analysis system according to claim 7,

9. At least one of the excitation wavelengths and the fluorescence wavelengths included in the recommended measurement conditions has a different sampling interval among the plurality of measurement wavelength ranges.

3. The spectroscopic analysis system according to claim 2 .

10. The at least one sampling interval in the plurality of measurement wavelength regions is set based on a genetic algorithm. The spectroscopic analysis system according to claim 9 .

11. the control unit searches for the measurement wavelength region based on a genetic algorithm, and in the genetic algorithm, applies a predetermined penalty function indicating a degree of overlearning to an RMSECV that is an index of a goodness of fit of a prediction model for analyzing the spectroscopic analysis spectrum, and evaluates the prediction model based on the penalty function; The RMSECV is the average value of the mean square error in cross validation.

3. The spectroscopic analysis system according to claim 2 .

12. The control unit switches a prediction model to be actually used for analyzing the spectroscopic analysis spectrum of the sample from among a plurality of prediction models in accordance with a state of the industrial plant when performing in-line measurement of the sample in the industrial plant.

2. The spectroscopic analysis system according to claim 1 .

13. an input process for receiving an input of at least one of an upper limit value of a measurement time of the spectroscopic analysis spectrum and a lower limit value of a measurement accuracy as a user setting condition for the measurement of the spectroscopic analysis spectrum of the sample; A display process of deriving predetermined recommended measurement conditions that satisfy the user-set conditions and displaying the recommended measurement conditions on a display device, the recommended measurement conditions are at least one of a wavelength range of light used in measuring the spectroscopic analysis spectrum, a sampling interval of the wavelength of the light, a slit width of a diffraction grating of a spectrometer that disperses the light, and a sweep speed of the wavelength of the light.

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