Analysis system

Abstract

Provided is an analysis system 1 comprising an analysis device, wherein the analysis device 10 is provided with: a determination unit 105 that calculates the degree of difference between the shapes of a plurality of spectra measured at multiple points within a minute range of the surface of a first measurement object 100a, and determines whether the degree of difference between the shapes is equal to or less than a threshold value; an estimation unit 106 that, if the degree of difference between the respective shapes is equal to or less than a threshold value, estimates the plurality of spectra as being pure-substance spectra derived from a compound having a single composition; a registration unit 107 that registers, in a pure-substance spectrum DB 40, one or more types of pure-substance spectrum data estimated from one or more first measurement objects; an extraction unit 109 that extracts, from a spectrum group measured at multiple points on the surface of a second measurement object 100b, spectrum data that matches the shape of a pure-substance spectrum registered in the pure-substance spectrum DB and / or spectrum data that does not match said shape; and an output unit 110 that outputs the extracted spectrum data as a component of the second measurement object.

Description

Analysis System

[0001] The present disclosure relates to an analysis system.

[0002] In the technical field of materials science, spectroscopic measurements that show the chemical characteristics of a material are performed to identify the chemical composition and estimate the total amount of the material. In Patent Document 1, the measured spectral shape is compared with spectral shapes in a database to analyze the components of the material being investigated.

[0003] Japanese Patent Application Laid-Open No. 2001-215193

[0004] However, there was a problem in that the accuracy of analyzing the components of the measurement object was low.

[0005] The present disclosure has been made in consideration of the above circumstances, and an object of the present disclosure is to provide a technology that can accurately analyze components of a measurement target.

[0006] An analysis system according to one aspect of the present disclosure is an analysis system including an analysis device for analyzing components of a measured object, the analysis device including: a determination unit that calculates the degree of difference between the shapes of a plurality of spectra measured at multiple points within a minute range on the surface of a first measured object and determines whether the degree of difference between the shapes is equal to or less than a threshold; an estimation unit that, if the degree of difference between the shapes is equal to or less than the threshold, estimates the plurality of spectra to be pure substance spectra derived from a compound of a single composition; a registration unit that registers one or more types of pure substance spectral data estimated from one or more first measured objects in a pure substance spectrum DB; an extraction unit that extracts spectral data that matches and / or does not match the shapes of the pure substance spectra registered in the pure substance spectrum DB from a group of spectra measured at multiple points on the surface of a second measured object; and an output unit that outputs the extracted spectral data as components of the second measured object.

[0007] According to the present disclosure, a technique can be provided that enables accurate analysis of components of a measurement target.

[0008] FIG. 1 is a diagram showing the configuration of an analysis system. FIG. 2 is a diagram showing a method for changing a measurement position. FIG. 3 is a diagram showing measurement data of a Raman scattering spectrum. FIG. 4 is a diagram showing an example of the configuration of a pure substance spectrum DB and an update method. FIG. 5 is a diagram showing an image of spectral decomposition. FIG. 6 is a diagram showing an image of calculation of spectral decomposition by NMF. FIG. 7 is a diagram showing an analysis flow. FIG. 8 is a diagram showing an analysis flow. FIG. 9 is a diagram showing the result of spectral decomposition. FIG. 10 is a diagram showing an example of a multi-core optical fiber. FIG. 11 is a diagram showing an image of measurement of a Raman scattering spectrum. FIG. 12 is a diagram showing an image of measurement of a Raman scattering spectrum. FIG. 13 is a diagram showing an image of measurement of a Raman scattering spectrum. FIG. 14 is a diagram showing an image of measurement of a Raman scattering spectrum. FIG. 15 is a diagram showing an image of measurement of a Raman scattering spectrum.

[0009] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the description of the drawings, the same parts are designated by the same reference numerals and the description thereof will be omitted.

[0010] [Summary of the Present Disclosure] To solve the above problems, the present disclosure estimates a pure substance spectrum derived from a single compound, provided that the measurement range on the surface of a measurement object is a small range and the distance between multiple spectra measured within that small range (the degree of difference in spectral shape) is sufficiently small, and registers the pure substance spectrum in a database.The pure substance spectrum registered in the database is then used as a teacher spectrum to analyze the components of the measurement object being investigated.

[0011] In this way, the spectrum of a pure substance in the database that is compared during component analysis of the object to be measured is estimated under the above conditions, which increases the likelihood that the spectrum corresponds to a single compound, thereby enabling accurate analysis of the components of the object to be investigated.

[0012] [Configuration of Analysis System] FIG. 1 is a diagram showing the configuration of an analysis system 1 according to this embodiment.

[0013] The analysis system 1 is an apparatus capable of measuring Raman scattering spectra at a plurality of different positions on the surface of the measurement object 100. For example, the analysis system 1 is an apparatus capable of multi-point Raman measurement in a small range, such as a microscopic Raman spectrometer.

[0014] The analysis system 1 includes an analysis device 10 , a multi-core optical fiber 20 , a probe head 30 , and a shared database 40 .

[0015] The analysis device 10 is a device that analyzes components of a measurement object 100. A component is a constituent element of the measurement object 100. In this embodiment, the measurement object 100 is a first measurement object 100a and a second measurement object 100b.

[0016] The first measurement object 100a is a sample for constructing the shared database 40, which is a pure substance spectrum DB. The second measurement object 100b is a sample to be investigated, from which components are to be separated and extracted while referring to the shared database 40. Both samples are various kinds of minerals that exist in the world.

[0017] The analyzing apparatus 10 includes a laser light source 101 that outputs laser light, a spectrometer 102 that disperses input scattered light, a detector 103 that detects the dispersed light, a computer 104 that measures the Raman scattering spectrum from the detected light, a determining unit 105 that determines whether the distance between the Raman scattering spectra is equal to or less than a threshold, an estimating unit 106 that estimates a pure substance spectrum from the Raman scattering spectrum of the first measured object 100a if the distance between the Raman scattering spectra is equal to or less than the threshold, a registering unit 107 that registers the estimated pure substance spectral data in the shared database 40, an updating unit 108 that updates the pure substance spectral data in the shared database 40, an extracting unit 109 that separates and extracts spectral data from the Raman scattering spectrum of the second measured object 100b using the pure substance spectral data in the shared database 40 if the distance between the Raman scattering spectra is not equal to or less than the threshold, an output unit 110 that outputs the separated and extracted spectral data, and a control unit 111 that controls each unit that constitutes the analyzing apparatus 10 and the control unit 303 of the probe head 30.

[0018] The multi-core optical fiber 20 is an optical fiber that connects the analysis device 10 and the probe head 30 to each other, and includes a first core 201 that transmits the laser light output from the laser light source 101 to the first optical system 301 of the probe head 30, and a second core 22 that transmits the scattered light output from the second optical system 302 of the probe head 30 to the spectrometer 102.

[0019] The probe head 30 includes a first optical system 301 that irradiates the laser light transmitted by the first core 201 onto the object to be measured 100, a second optical system 302 that outputs scattered light scattered on the surface of the object to be measured 100 by the laser light to the second core 202, and a control unit 33 that controls the first optical system 301 and the second optical system 302.

[0020] The shared database 40 has a function of storing the pure substance spectral data estimated by the estimation unit 106. The shared database 40 is a pure substance spectrum DB in which one or more types of pure substance spectral data are registered as candidates for pure substance spectral data, and is a shared database that can be referenced by multiple users.

[0021] [Functions of the Analysis Device] The functions of the analysis device 10 will be described in detail.

[0022] The judgment unit 105 has the function of calculating the distance (the degree of difference in the spectral shape) between multiple Raman scattering spectra measured at multiple points within a small area on the surface of the measurement object 100 (first measurement object 100a, second measurement object 100b) and judging whether the distance is below a threshold value.

[0023] The estimation unit 106 has a function of estimating that multiple Raman scattering spectra of the object to be measured 100 (first object to be measured 100a) are pure substance spectra derived from a compound of a single composition when the distance between the multiple Raman scattering spectra is equal to or less than a threshold value.

[0024] The registration unit 107 has the function of registering one or more types of pure substance spectral data estimated from one or more measurement objects 100 (first measurement objects 100a) in the shared database 40 so that they can be referenced, by associating them with the user ID of the user who measured the pure substance spectrum, the reliability of the pure substance spectral data, and the sharing range of users who can use the pure substance spectral data (the public disclosure range of the pure substance spectral data).

[0025] The update unit 108 has a function of increasing the reliability of pure substance spectrum data when another user measures and extracts spectrum data whose distance (degree of difference in shape) from the pure substance spectrum data registered in the shared database 40 is equal to or less than a threshold. Additionally, the update unit 108 increases the reliability of pure substance spectrum data when the pure substance spectrum data registered in the shared database 40 is referenced by another user. The other user is a user with a user ID different from the user ID of the user who measured the pure substance spectrum data.

[0026] The extraction unit 109 has a function of extracting spectral data that matches and / or does not match the shape of the pure substance spectrum registered in the shared database 40 from a group of Raman scattering spectra measured at multiple points on the surface of the object to be measured 100 (second object to be measured 100b) if the distance between multiple Raman scattering spectra is not below a threshold.

[0027] In this case, the extraction unit 109 refers to the shared range of the user registered in the shared database 40 and extracts spectral data from the pure substance spectra that can be shared by the user currently measuring the object to be measured 100 (second object to be measured 100b).

[0028] The output unit 110 has a function of outputting the extracted spectral data as components of the measurement object 100 (second measurement object 100b). When outputting spectral data that matches the shape of the pure substance spectrum registered in the shared database 40, the output unit 110 also outputs the reliability of the pure substance spectral data.

[0029] [Operation of Analysis System] First, a method for measuring the Raman scattering spectrum will be described.

[0030] Laser light emitted from the laser light source 101 is incident on the first core 201. The first core 201 guides the laser light to the first optical system 301, and the first optical system 301 irradiates the laser light onto the object 100 to be measured.

[0031] The scattered light scattered on the surface of the measurement object 100 is collected by the second optical system 302, and the scattered light is output to the second core 202. The second core 202 guides the scattered light to the spectrometer 102. Then, the computer 104 obtains a Raman scattering spectrum from the scattered light.

[0032] Thereafter, the position of the first optical system 301 is moved in a direction perpendicular to the optical axis of the laser light by the control unit 303. For example, as shown in Fig. 2, the control unit 303 causes the position of the condenser lens 301b constituting the first optical system 301 to scan on the XY plane.

[0033] In this way, the Raman scattering spectrum is measured at a plurality of different positions on the surface of the object 100 while changing the focusing position of the laser light on the surface of the object 100.

[0034] Next, a method for estimating the spectrum of a pure substance will be described.

[0035] The analytical device 10 measures the Raman scattering spectrum by scanning a minute area on the surface of the first measurement object 100a. The minute area is, for example, a rectangular area of ​​several tens to several hundreds of micrometers.

[0036] The determination unit 105 acquires the measured Raman scattering spectra from the calculator 104, performs appropriate spectrum exclusion processing, and then calculates the distance between the Raman scattering spectra. As described above, the distance between the Raman scattering spectra is the degree to which the spectral shapes of the Raman scattering spectra differ.

[0037] At this time, the determining unit 105 may measure each distance for all combinations of Raman scattering spectra, but calculates each distance for a predetermined number of combinations of a sufficient number of Raman scattering spectra.

[0038] Then, the determining unit 105 determines whether the distance is sufficiently small.

[0039] If the distance is sufficiently small, the estimation unit 106 estimates that the measured Raman scattering spectra are spectra derived from a compound of a single composition or a single three-dimensional structure, and determines that the measured Raman scattering spectra are pure substance spectra. For example, if the average value of all distances or the maximum value of the distances is equal to or less than a predetermined threshold, the estimation unit 106 determines that the measured Raman scattering spectra are pure substance spectra.

[0040] This estimation method is based on the fact that a single compound can easily form crystals within a spatially small area without artificial manipulation. By setting the measurement range to a small area and ensuring that the distances between multiple Raman scattering spectra measured within that small area are sufficiently small, it is possible to increase the likelihood that the multiple Raman scattering spectra are spectra of a pure substance derived from a single compound.

[0041] Figure 3 shows multiple Raman scattering spectra measured repeatedly at different measurement locations over a small area of ​​750 μm x 1000 μm on the surface of an oxide sample. The spectral shapes of the Raman scattering spectra are sufficiently similar within this small area, suggesting that the same compounds are being produced with a regularity.

[0042] Next, a method for constructing the pure substance spectrum data DB will be described.

[0043] The registration unit 107 registers the pure substance spectral data in the shared database 40 so that the data can be referenced. When the user A changes the type of the first object to be measured 100a and the estimation unit 106 estimates the pure substance spectrum for the changed first object to be measured 100a, the registration unit 107 adds the pure substance spectral data to the shared database 40. By repeating this operation, multiple types of pure substance spectral data are registered in the shared database 40 as needed.

[0044] The spectral data descriptor of the pure substance spectral data is also registered in the shared database 40 so that it can be referenced. As shown in Fig. 4, the spectral data descriptor includes the user ID of user A who measured the pure substance spectrum, the reliability of the pure substance spectral data, and the sharing range of users who can use the pure substance spectral data. The initial value of the reliability is, for example, 10%, Lo.

[0045] When user B measures and extracts spectral data that is close to user A's pure substance spectral data in the shared database 40, the update unit 108 increases the reliability of the pure substance spectral data of user A. Additionally, the update unit 108 checks whether the pure substance spectral data has been referenced, and when user B references the pure substance spectral data, increases the reliability of the referenced pure substance spectral data. For example, it changes the reliability to 20% or Hi.

[0046] In this way, user B can refer to the pure substance spectrum data measured by user A, and the reliability of the pure substance spectrum data is increased by user B's reference, etc., so the reliability of the pure substance spectrum data is more likely to increase through user B's activities, and the incentive for data sharing is improved.

[0047] Next, a method for analyzing the components will be described.

[0048] This is an operation performed when user C grasps the components of the second object 100b that he or she is interested in investigating. In this case, the distances between the multiple Raman scattering spectra of the second object 100b that are calculated by the determination unit 105 are considered to be sufficiently large.

[0049] In this case, the extraction unit 109 acquires pure substance spectral data available to user C as teacher spectra (known basis spectra) from the shared database 40, as shown in Fig. 5. The pure substance spectral data available to user C is determined based on the shared range of the spectral data descriptor.

[0050] Then, the extraction unit 109 performs spectral decomposition on the multiple Raman scattering spectra of the second measured object 100b, compares each spectrum separated and extracted by the spectral decomposition with the above-mentioned teacher spectrum, and determines the components of the second measured object 100b.

[0051] For example, as shown in Figure 5, the output unit 110 outputs a known basis spectrum A (reliability: Hi) that matches the shape of the teacher spectrum, a known basis spectrum B (reliability: Lo) that matches the shape of the teacher spectrum, and an unknown basis spectrum C (reliability: none) that does not match the shape of the teacher spectrum as components of the second measured object 100b.

[0052] For spectral decomposition, a low-rank approximation algorithm can be used, which approximates a matrix by adding together low-rank vectors. For example, nonnegative matrix factorization (NMF) is used. NMF is a technique for decomposing a matrix X into matrices F and G. This technique is applied to two matrix pairs (matrix H / matrix U and matrix F / matrix G), and matrix H is decomposed to only the spectra (vectors) registered in the pure substance spectrum database.

[0053] Specifically, as shown in FIG. 6 , a plurality of measured Raman scattering spectra are organized into a matrix to form a measurement matrix X, which is expressed as the sum of two low-rank approximation matrices, a known basis spectrum matrix H and an unknown basis spectrum matrix F, and an error matrix ε is added to the matrix.

[0054] The column vectors of the known basis spectral matrix H are composed of pure substance spectral data obtained from the shared database 40. The column vectors of the unknown basis spectral matrix F are composed of spectrally decomposed information that cannot be expressed by the known basis spectra.

[0055] To minimize the error matrix ε, the elements of the weighting matrix U of the known basis spectral matrix H, the unknown basis spectral matrix F, and the weighting matrix G of the unknown basis spectral matrix F are determined by an appropriate optimization algorithm. As the optimization algorithm, various gradient descent methods can be applied.

[0056] Next, the analysis flow will be described.

[0057] 7 and 8 are diagrams showing the analysis flow performed by the analysis device.

[0058] The case where measurement is performed by user A will be described.

[0059] First, the determining unit 105 calculates the distance between a plurality of Raman scattering spectra measured at multiple points within a small area on the surface of the measurement object 100, and determines whether the distance is equal to or smaller than a threshold value (step S1).

[0060] If the distance is equal to or less than the threshold, the estimation unit 106 estimates the plurality of Raman scattering spectra as pure substance spectra (step S2).Then, the determination unit 105 refers to the shared database 40 and determines whether or not the shared database 40 contains pure substance spectral data whose distance from the estimated pure substance spectral data is equal to or less than the threshold (step S3).

[0061] If the pure substance spectrum data does not exist in the shared database 40, the registration unit 107 newly registers the estimated pure substance spectrum data in the shared database 40 (step S4). If the pure substance spectrum data exists in the shared database 40, the update unit 108 updates the pure substance spectrum data registered in the shared database 40 to increase its reliability (step S5).

[0062] On the other hand, if the distance between the multiple Raman scattering spectra is not below the threshold, that is, if a large number of spectra with different composition ratios of the mixture are measured, the extraction unit 109 performs spectral decomposition on the object to be measured 100 (step S6).

[0063] First, the extraction unit 109 separates and extracts spectra while appropriately referencing multiple pure substance spectral data registered in the shared database 40 (step S61). The appropriateness of this extraction depends on individual algorithms such as NMF. It is empirically known that spectral data that is likely to be correct can be detected probabilistically, but this is not 100% certain.

[0064] Next, the extraction unit 109 refers to the shared database 40 and determines whether or not the shared database 40 contains pure substance spectral data whose distance from the separated and extracted spectral data is equal to or less than a threshold value (step S62).

[0065] If the pure substance spectrum data exists in the shared database 40, the update unit 108 updates and increases the reliability of the pure substance spectrum data registered in the shared database 40 (step S62). Thereafter, the output unit 110 outputs the pure substance spectrum data and the reliability. If the pure substance spectrum data does not exist in the shared database 40, the pure substance spectrum data may or may not be newly registered in the shared database 40.

[0066] When measurements are taken by users B and C, the same analysis flow is carried out.

[0067] Next, the effects of utilizing the pure substance spectrum DB will be described.

[0068] 9A and 9B are diagrams showing the spectral decomposition results of the second measurement object 100b. Fig. 9A shows the results when pure substance spectrum data from the pure substance spectrum DB is used as the teacher spectrum. Fig. 9B shows the results when the teacher spectrum is not used.

[0069] Comparing FIG. 9(a) and FIG. 9(b), the supervised spectral decomposition shown in FIG. 9(a) reveals that γFeOOH and αFeOOH are isolated from the measured spectral data set shown in FIG. ２ O ３ On the other hand, in the unsupervised spectral analysis shown in Figure 9(b), γFeOOH and αFeOOH were successfully separated and extracted. ２ O ３ are not separated but become one, and the spectral shape of βFeOOH is distorted.

[0070] The difference in the spectral decomposition results for the same group of measured Raman scattering spectrum data confirms the effectiveness of using teacher spectra. This teacher spectrum is estimated under the condition that the measurement range is minute and the distance between spectra is sufficiently small. This allows for accurate analysis of the components of the object being investigated.

[0071] [Example of multi-core optical fiber] A supplementary explanation will be given of the multi-core optical fiber 20. The multi-core optical fiber 20 used in this embodiment is a Y-branch optical fiber in which a first core 201 located at the center of the fiber cross section and a plurality of second cores 202 located around the center are branched midway in the longitudinal direction, as shown in Fig. 10. Of course, optical fibers other than the Y-branch optical fiber may be used.

[0072] [Method for Measuring Raman Scattering Spectrum (Details)] A method for measuring Raman scattering spectrum will be further explained with reference to FIGS.

[0073] As shown in FIG. 11, a laser beam emitted from a laser light source 101 is focused by a condenser lens 51 and made incident on a first core 201 .

[0074] Next, as shown in Figure 12, the laser light emitted from the opposite side of the first core 201 is converted into parallel rays by a collimator lens 301a in the first optical system 301, and the control unit 303 controls the position of the focusing lens 301b in the first optical system 301 to move back and forth in the z-axis direction so that the parallel rays are focused on the object to be measured 100.

[0075] Next, as shown in FIG. 13, scattered light from the measurement object 100 is collected by a plurality of collecting lenses 302 a in the second optical system 302 , and the collected light is input to a plurality of second cores 202 , respectively.

[0076] Next, as shown in Figure 14, each scattered light emitted from each of the multiple second cores 202 is converted into parallel rays by multiple collimator lenses 52, and each parallel beam is focused by a focusing lens 53 and directed to a spectrometer 102, and the dispersed light is detected by a detector 103.

[0077] 15, the control unit 303 moves the position of the first optical system 301 on an XY plane perpendicular to the optical axis of the laser light, thereby focusing the laser light at a plurality of different positions on the surface of the measurement object 100. As a result, a plurality of Raman scattering spectra can be measured while changing the measurement position.

[0078] At this time, the control unit 303 uses an electrically controlled MEMS (Micro Electro Mechanical Systems) device such as a cantilever to perform micrometer-order position control on the first optical system 301. As a result, it is possible to measure the Raman scattering spectrum while changing the measurement position on the surface of the measurement object 100 with micrometer-order precision.

[0079] [Effect] According to this embodiment, when the distance between multiple Raman scattering spectra measured within a small area on the surface of a sample (first measured object 100a) is equal to or less than a threshold value, the multiple Raman scattering spectra are estimated to be pure substance spectra, and the pure substance spectrum DB in which one or more pure substance spectra are registered is used to perform spectral decomposition of the investigation target (second measured object 100b), and the spectral data separated and extracted by this execution is output as components of the target of interest.

[0080] In this way, the measurement range is set to a minute range, and the pure substance spectrum is estimated under the condition that the distance between multiple Raman scattering spectra measured within that minute range is sufficiently small, thereby improving the likelihood that the multiple Raman scattering spectra are pure substance spectra derived from a single compound.

[0081] Furthermore, when performing spectral decomposition, the pure substance spectrum DB is used as a source of known single compound spectra (teacher spectra), thereby improving the accuracy of spectral decomposition of multiple Raman scattering spectra (mixed spectra) related to the target of interest.

[0082] Furthermore, according to this embodiment, when spectral data measured and extracted by another user differs in shape from pure substance spectral data registered in the pure substance spectrum DB by a threshold or less, the reliability of the pure substance spectral data is increased. This makes it easier for the reliability of the pure substance spectral data to be increased by the work of another user, thereby improving the incentive for data sharing.

[0083] Furthermore, according to this embodiment, the pure substance spectrum DB is successively updated with new data, which allows the amount of spectral data registered in the pure substance spectrum DB to increase continuously, thereby improving the value of the pure substance spectrum DB.

[0084] As a result, a technique can be provided that enables accurate analysis of the components of a measurement target.

[0085] [Others] The present disclosure is not limited to the above-described embodiment. Numerous modifications of the present disclosure are possible within the scope of the gist of the present disclosure. Furthermore, the determination unit 105, estimation unit 106, registration unit 107, update unit 108, extraction unit 109, and output unit 110 constituting the analysis device 10 can be realized, for example, by a computer.

[0086] REFERENCE SIGNS LIST 1 Analysis system 10 Analysis device 20 Multi-core optical fiber 30 Probe head 40 Shared database 51 Condenser lens 52 Collimator lens 53 Condenser lens 101 Laser light source 102 Spectrometer 103 Detector 104 Calculator 105 Determination unit 106 Estimation unit 107 Registration unit 108 Update unit 109 Extraction unit 110 Output unit 111 Control unit 201 First core 202 Second core 301 First optical system 301a Collimator lens 301b Condenser lens 302 Second optical system 303 Control unit 100 Measurement object 100a First measurement object 100b Second measurement object

Claims

1. An analytical system comprising an analytical device for analyzing components of a measured object, the analytical device comprising: a determining unit that calculates the degree of difference between the shapes of a plurality of spectra measured at multiple points within a minute range on the surface of a first measured object and determines whether the degree of difference between the shapes is equal to or less than a threshold; an estimating unit that, if the degree of difference between the shapes is equal to or less than the threshold, estimates the plurality of spectra to be pure substance spectra derived from a compound of a single composition; a registering unit that registers one or more types of pure substance spectral data estimated from one or more first measured objects in a pure substance spectrum DB; an extracting unit that extracts spectral data that matches and / or does not match the shapes of the pure substance spectra registered in the pure substance spectrum DB from a group of spectra measured at multiple points on the surface of a second measured object; and an output unit that outputs the extracted spectral data as the components of the second measured object.

2. The analysis system according to claim 1, further comprising an update unit, wherein the registration unit registers the pure substance spectral data in the pure substance spectrum DB in a referable manner in association with the user ID and reliability of the pure substance spectral data, the update unit increases the reliability of the pure substance spectral data when spectral data measured and extracted by another user differs in shape from the pure substance spectral data registered in the pure substance spectrum DB by a threshold or less, and the output unit, when outputting spectral data that matches the shape of the pure substance spectrum registered in the pure substance spectrum DB, also outputs the reliability of the pure substance spectral data.

3. The analysis system described in claim 1, wherein the registration unit registers the pure substance spectrum data in the pure substance spectrum DB in association with the user ID and sharing range of the pure substance spectrum data, and the extraction unit extracts spectrum data from the pure substance spectra that can be shared by the user currently measuring the second object to be measured.

4. The analysis system according to claim 1, further comprising: a multi-core optical fiber that transmits laser light output from the analysis device through a first core and transmits light scattered by the laser light to the analysis device through a second core; and a probe head that irradiates the laser light from a first optical system onto the object to be measured, outputs light scattered on the surface of the object to be measured by the laser light from a second optical system to the second core, and moves the first optical system in a direction perpendicular to the optical axis of the laser light.

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