JP2010266380A - Component distribution analysis method and component distribution analyzer - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a component distribution analysis method and a component distribution analyzer capable of removing an influence of a solvent content rate between a measuring object and a sample, and analyzing clearly a specific component in the measuring object, even when analyzing a difference of each component distribution between the measuring object and the sample extracted from the measuring object based on excitation/emission matrix (EEM). <P>SOLUTION: Each EEM of the measuring object and the sample is measured, and analyzed by using main component analysis, and a component axis orthogonal to a solvent axis is extracted in main component score plots which is a result of the main component analysis. Then, a difference of a component distribution between the measuring object and the sample is discriminated on the component axis, and a color axis is correlated with the component axis, and each plot is colored along the color axis, to thereby visualize each component distribution of the measuring object and the sample. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a component distribution analysis method and a component distribution analysis apparatus.

  Conventionally, a technique for visualizing the internal structure of a measurement object by fluorescently observing the fluorescence-stained measurement object has been reported.

  For example, in the tissue structure analysis methods described in Patent Documents 1 and 2, a multi-component dispersed food containing starch, protein, lipid and the like is stained with a fluorescent dye (for example, Congo red, fluorescamine, Nile blue, etc.). After that, the tissue structure of the multi-component dispersed food is analyzed as a three-dimensional image by observing fluorescence.

  In addition, in the method for observing flour products described in Patent Document 3, by performing double staining in which lipids and proteins are stained with different staining solutions (for example, oil red O, hematoxylin, etc.) in different colors, The state of the gluten skeleton, oil droplets, and starch granules is simultaneously observed with an optical microscope.

  Moreover, in the formation method of the tissue structure image described in Patent Document 4, the processed food or the raw material for manufacturing the processed food dyed with a fluorescent dye having a triphenylmethane skeleton (for example, magenta II) is excited and irradiated and dyed. The processed food or the raw material for manufacturing the processed food is observed using at least three types of observation light, and a clear image showing the fine structure of the internal structure of the processed food is formed based on the observation result.

  Here, an example of protein visualization by fluorescent staining will be described with reference to FIG. As an example of the measurement object, flour foods (for example, breads, noodles, confectionery, etc.) are eaten all over the world, and the texture is an important factor in considering the taste of flour foods. . Gluten, which has a great influence on the texture of flour foods, is a viscoelastic proteinaceous substance that is produced when flour and water are kneaded, and its shape changes as the flour dough is kneaded. Therefore, the texture that leads to the deliciousness of the flour food is determined by the shape of the gluten. Therefore, in the technique for visualizing proteins by fluorescent staining (Patent Documents 1 to 4 and the like), a cross section of bread dough, which is a measurement object as shown in FIG. 1, was observed. In FIG. 1, the stained part shows the protein of the component of the measurement object, and the white part shows the starch granules of the component of the measurement object.

  However, conventional techniques for visualizing proteins by fluorescent staining (Patent Documents 1 to 4 and the like) cannot be used on a production line because artificial substances are added by staining. Further, as shown in FIG. 1, since a reagent that stains only the protein is used, not only a specific component (eg, gluten) of the measurement target but also other proteins contained in the flour of the measurement target In other words, only specific components of the measurement object could not be visualized.

  Therefore, in recent years, it has been desired to develop a technique for more clearly analyzing a specific component of a measurement object without staining the measurement object.

  For example, in the component distribution visualization method described in Patent Document 5 by the present applicant, the excitation wavelength applied to the measurement object and the fluorescence wavelength observed from the measurement object are stepped without staining the measurement object. The fluorescence intensity is extracted by compressing the excitation / fluorescence matrix (Excitation Emission Matrix: EEM) obtained by measuring the fluorescence intensity while changing it in a statistical analysis process, and using the color corresponding to the extracted fluorescence characteristic By color mapping, a plurality of components of the measurement object are simultaneously displayed as images.

Japanese Patent No. 3762308 Japanese Patent No. 3771861 JP 2008-122401 A International Publication No. 2008/078752 Japanese Patent No. 3706914

  However, in the prior art (Patent Document 5 and the like), a plurality of components of the measurement object can be simultaneously displayed based on the excitation / fluorescence matrix (EEM) without staining the measurement object. Bread dough etc.) and samples extracted from the measurement object (eg gluten and primary starch (pure starch extracted from dough dough) etc.) are analyzed together, a solvent such as water is added during the sample extraction process. Therefore, the moisture content of the sample before extraction (such as starch) in the measurement object is different from the moisture content of the extracted sample alone (such as primary starch). In addition, there is a problem that it is difficult to clearly analyze the difference in component distribution between the measurement object and the sample. .

  That is, in the prior art (Patent Document 5, etc.), since the influence of the solvent content between the measurement object and the sample extracted from the measurement object cannot be removed, the measurement object is based on the excitation / fluorescence matrix (EEM). When analyzing the difference in the component distribution between the sample and the sample, there is a problem that a specific component of the measurement object cannot be clearly analyzed.

  The present invention has been made in view of the above problems, and is a case where a difference in component distribution between a measurement object and a sample extracted from the measurement object is analyzed based on an excitation / fluorescence matrix (EEM). In addition, the influence of the solvent content between the measurement object and the sample can be removed, and a specific component (for example, gluten etc.) of the measurement object can be analyzed more clearly. An object is to provide a distribution analyzer.

  In order to achieve such an object, the component distribution analysis method of the present invention is a component distribution analysis method for analyzing the component distribution of a measurement object and a sample extracted from the measurement object, and has a predetermined excitation wavelength range. And measuring the fluorescence intensity at a plurality of measurement locations on the measurement object and the sample while changing the excitation wavelength to be irradiated and the fluorescence wavelength to be observed in a stepwise manner within a predetermined fluorescence wavelength range. Excitation / fluorescence matrix information acquisition step for acquiring excitation / fluorescence matrix information of each measurement location on the sample and the sample, and the excitation / fluorescence matrix acquired for each measurement location in the excitation / fluorescence matrix information acquisition step Principal component analysis is performed on the information, and the main component that is the value of the component of the measurement object and the sample represented by the principal component Create a principal component score plot by plotting the score as coordinates, and on the principal component score plot, out of the plot group showing the components of the solvent axis determination samples with different solvent contents contained in the sample, the solvent An axis passing through the centroid of the first plot group having a high content rate and the centroid of the second plot group having a low solvent content rate or an approximate straight line of the plot group of the sample for determining the solvent axis is set as the solvent axis. Then, an axis orthogonal to the solvent axis is set as a component axis that is not affected by the solvent content, and a plot showing the measurement object and the sample component is projected onto the component axis, whereby each measurement is performed. A data analysis step of parameterizing the difference between the components at the locations.

  Further, the component distribution analysis method of the present invention is the component distribution analysis method described above, wherein a color to be colored at each measurement location is determined based on the plot projected on the component axis in the data analysis step. A color determination step, and a component distribution visualization step of visualizing the component distribution of the measurement object and the sample by color mapping the color to be colored at each measurement location determined in the color determination step; Is further included.

  The component distribution analysis method of the present invention is the component distribution analysis method described above, wherein the excitation wavelength to be irradiated and the fluorescence wavelength to be observed are measured for the measurement object and the sample in the excitation / fluorescence matrix information acquisition step. A plurality of fluorescent images with different values are taken, and the excitation / fluorescence matrix information at each measurement location on the measurement object and the sample is acquired from the fluorescence intensity of each pixel in the fluorescence image.

  The component distribution analysis method of the present invention is characterized in that, in the component distribution analysis method described above, the measurement object includes at least a flour product.

  The component distribution analysis method of the present invention is characterized in that, in the component distribution analysis method described above, the component includes at least one selected from the group consisting of protein, starch, polysaccharides, minerals, and lipids. .

  The component distribution analysis method of the present invention is characterized in that, in the component distribution analysis method described above, the solvent includes at least one selected from the group consisting of water and alcohol.

  Further, the component distribution analyzer of the present invention includes a spectroscopic illumination device that irradiates a measurement object and a sample extracted from the measurement object with a predetermined excitation wavelength, and the measurement object and the sample at a predetermined fluorescence wavelength. A component distribution analyzer for analyzing a component distribution of the measurement object and the sample, comprising a spectroscopic imaging device for observation, wherein at least a plurality of measurement points on the measurement object and the sample are excited and fluorescent A principal component analysis is performed on the excitation / fluorescence matrix measurement unit that acquires matrix information, and the excitation / fluorescence matrix information acquired for each measurement point by the excitation / fluorescence matrix measurement unit, and the matrix information is expressed in principal components. Principal component score by plotting the principal component score, which is the component value of the measurement object and the sample, as coordinates The center of gravity of the first plot group having a high solvent content is included in the plot group indicating the components of the solvent axis determination samples having different solvent contents contained in the sample on the principal component score plot. And an axis passing through the center of gravity of the second plot group having a low solvent content, or an approximate straight line of the plot group of the solvent axis determination sample, is set as the solvent axis, and the axis orthogonal to the solvent axis is the axis Data that is set as a component axis that is not affected by the solvent content rate, and that plots the measurement object and the sample components on the component axis to parameterize the difference between the components at each measurement location An analysis unit is provided.

  Further, the component distribution analyzer of the present invention, in the component distribution analyzer described above, determines a color to be colored at each measurement location based on the plot projected on the component axis by the data analysis unit, It further includes a component distribution visualization unit that visualizes the component distribution of the measurement object and the sample by color-mapping the color to be colored at each measurement location.

  According to this invention, even when analyzing the difference in component distribution between the measurement object and the sample extracted from the measurement object based on the excitation / fluorescence matrix (EEM), the measurement object and the sample It is possible to remove the influence of the solvent content rate, and there is an effect that the specific component of the measurement object can be analyzed more clearly.

FIG. 1 is a diagram showing an example of protein visualization by conventional fluorescent staining. FIG. 2 is a diagram illustrating an example of an excitation / fluorescence matrix (EEM). FIG. 3 is a flowchart showing the basic processing of the component distribution analysis method of the present invention. FIG. 4 is a diagram illustrating an example of a measurement object and a sample creation process. FIG. 5 is a diagram illustrating an example of a measurement object and a sample. FIG. 6 is a diagram illustrating an example of a measurement cell. FIG. 7 is a diagram illustrating an example of a fluorescence image. FIG. 8 is a diagram illustrating an example of the EEM in each pixel. FIG. 9 is a diagram illustrating an example of the solvent axis and the component axis on the principal component plot. FIG. 10 is a diagram illustrating details of an example of plots on the color axis and the component axis. FIG. 11 is a diagram illustrating an example of a visualized image of the component distribution. FIG. 12 is a block diagram showing an example of the configuration of the component distribution analysis system of this embodiment. FIG. 13 is a block diagram illustrating an example of a fluorescent image photographing device. FIG. 14 is a perspective view showing an example of a fluorescence image photographing apparatus. FIG. 15 is a diagram illustrating an example of a precipitate. FIG. 16 is a diagram illustrating an example of the F7000 optical system. FIG. 17 is a diagram showing an example of measurement conditions of the spectrofluorometer. FIG. 18 is a diagram showing an example of EEM of gluten without addition of salt. FIG. 19 is a diagram showing an example of EEM of salt-added gluten. FIG. 20 is a diagram illustrating an example of an EEM of a primary starch to which water is not added. FIG. 21 is a diagram showing an example of an EEM of a primary starch having a powder to water ratio of 10: 7. FIG. 22 is a diagram illustrating an example of an EEM of bread dough. FIG. 23 is a diagram illustrating an example of a principal component analysis contribution rate. FIG. 24 is a diagram illustrating an example of the main component addition amount of the first main component. FIG. 25 is a diagram illustrating an example of the main component addition amount of the second main component. FIG. 26 is a diagram illustrating an example of the principal component addition amount of the third principal component. FIG. 27 is a diagram illustrating an example of a principal component score plot of the first principal component and the second principal component. FIG. 28 is a diagram illustrating an example of a principal component score plot and a “solvent axis”. FIG. 29 is a diagram illustrating an example of a principal component score plot of the first principal component and the third principal component. FIG. 30 is a diagram illustrating an example of excitation / fluorescence matrix imaging. FIG. 31 is a diagram illustrating an example of measurement conditions of EMM-IS. FIG. 32 is a diagram illustrating an example of an imaging range and an analysis range. FIG. 33 is a diagram illustrating an example of dimension adjustment. FIG. 34 is a diagram illustrating an example of a “solvent axis” and a “component axis”. FIG. 35 is a diagram illustrating an example of coordinates in the “component axis”. FIG. 36 is a diagram illustrating an example of correspondence with color axes. FIG. 37 is a diagram showing an example of a fluorescence image at an excitation wavelength / fluorescence wavelength: 350/500 nm. FIG. 38 is a diagram showing an example of a fluorescence image at an excitation wavelength / fluorescence wavelength: 430/710 nm. FIG. 39 is a diagram showing an example of the contribution ratio of the main component load amounts of gluten and primary starch (water). FIG. 40 is a diagram illustrating an example of the main component load amount of the first main component. FIG. 41 is a diagram illustrating an example of a principal component load amount of the second principal component and a detailed diagram. FIG. 42 is a diagram illustrating an example of a principal component load amount and a detailed diagram of the third principal component. FIG. 43 is a diagram showing an example of a main component score plot of gluten and PS (hydrous). FIG. 44 is a diagram illustrating an example of calculation of “solvent axis”. FIG. 45 is a diagram illustrating an example of images of bread dough, gluten, and primary starch. FIG. 46 is a diagram showing the main component contribution ratio of eigenvectors of bread dough. FIG. 47 is a diagram illustrating an example of the principal component load amount of the first principal component. FIG. 48 is a diagram illustrating an example of the main component load amount of the second main component. FIG. 49 is a diagram illustrating an example of the principal component load amount of the third principal component. FIG. 50 is a diagram showing a main component score plot of bread dough. FIG. 51 is a diagram illustrating another example of the calculation of the “solvent axis”. FIG. 52 is a diagram illustrating another example of images of bread dough, gluten, and primary starch.

  Examples of preferred embodiments of the [I] component distribution analysis method, the [II] component distribution analysis system, and the program according to the present invention will be described below in detail with reference to FIGS. Note that the present invention is not limited to the embodiments.

[I] Component Distribution Analysis Method First, the excitation / fluorescence matrix (EEM) will be described with reference to FIG. In the present invention, the “excitation / fluorescence matrix” means, as shown in the left diagram of FIG. 2, the excitation wavelength λ _Ex irradiated to a sample (for example, a measurement object and a sample), and the fluorescence wavelength λ of light emitted from the sample. _{It is} a contour graph (fluorescent fingerprint) of three-dimensional data consisting of three axes of _{Em and} fluorescence intensity _IEx , _{Em of} a sample.

  As shown in the right diagram of FIG. 2, the excitation / fluorescence matrix (EEM) can be represented in a planar manner by plotting the fluorescence intensity at each point in a contour line with the horizontal axis representing the fluorescence wavelength and the vertical axis representing the excitation wavelength. The excitation / fluorescence matrix has the advantages of being able to characterize the sample without pretreatment such as fluorescent staining, being easy to operate and measuring in a short time, and having higher sensitivity than UV absorbance. Therefore, it is a widely used technique for analyzing the internal structure of foods, measuring suspended matters in the atmosphere, and specifying dye raw materials. In the present invention, by using this excitation / fluorescence matrix, only target component information is extracted from an enormous amount of information, and the component distribution inside the substance is visualized.

  As an example, the gluten distribution in bread dough is visualized by measuring and analyzing the cross-section of bread dough as a measurement object and gluten extracted from bread dough as a sample and primary starch having different moisture contents based on EEM. The processing will be described with reference to FIGS. 4 to 11 as appropriate along the flowchart of FIG.

  As shown in FIG. 3, in the component distribution analysis method of the present invention, first, a measurement object and a sample for analyzing the component distribution are prepared by a measurer as a premise (step S1).

In the present invention, the “measurement object” means a sample that is a measurement object when analyzing the component distribution using EEM. For example, the measurement object is a flour product (for example, bread dough, udon, soba, etc.). Noodles and confectionery such as cookies and cakes). The “sample” means a sample extracted by a predetermined process described later. For example, when the measurement object is bread dough, the sample is gluten extracted from bread dough, or pure starch extracted from bread dough. It may be a primary starch (PS) that is minutes. Also,"
The `` solvent axis determination sample '' means a sample having a different solvent content used for determining the solvent axis among samples. For example, when the measurement object is bread dough, the moisture content extracted from the bread dough Primary starches (for example, 70% hydro PS, 60% hydro PS, 50% hydro PS, 40% hydro PS, 30% hydro PS, 20% hydro PS, non-hydro PS, etc.) may be used. Further, the “solvent” may include at least one selected from the group consisting of water and alcohol. In addition, the “component” may include at least one selected from the group consisting of proteins (eg, gluten), starch (eg, primary starch), polysaccharides, minerals, and lipids.

  Here, with reference to FIG. 4 and FIG. 5, the detail of an example of the preparation process of the measurement object and sample of step S1 is demonstrated.

  As shown in FIG. 4, first, the measurer creates bread dough (see FIG. 5A) that becomes a measurement object by mixing strong flour and water using a mixer or the like. Then, the measurer pours the prepared bread dough in water to cause starch and the like to flow out together with the water, and extracts only gluten as a sample (see FIG. 5B). Then, the measurer centrifuges the liquid containing the spilled starch and the like. Then, the measurer takes out the centrifuged precipitate portion mainly composed of starch (ie, primary starch) and freeze-drys it (see FIG. 5C). Then, the measurer adds starch to the freeze-dried starch to create, for example, a starch having a water content of 70% (see FIG. 5D). In this way, in the measurement object and sample creation processing in step S1, the measurer creates bread dough that is a measurement object, and gluten and primary starch that are samples extracted from the measurement object.

  Returning to FIG. 3, in the component distribution analysis method of the present invention, in the excitation / fluorescence matrix information acquisition step, the measurement object created in step S1 and the EEM of the sample cross section are measured for each pixel (step S2). ).

  That is, in the excitation / fluorescence matrix information acquisition step in step S2, the excitation wavelength to be irradiated and the fluorescence wavelength to be observed are changed stepwise in the predetermined excitation wavelength range and the predetermined fluorescence wavelength range, while the measurement object and the sample are scanned. By measuring the fluorescence intensities at the plurality of measurement locations, excitation / fluorescence matrix information of each measurement location on the measurement object and the sample is acquired.

  Here, with reference to FIGS. 5-8, the detail of an example of the excitation and fluorescence matrix information acquisition process of step S2 is demonstrated.

  As shown in FIG. 5, the measurer creates bread dough (see FIG. 5 (a)) as a measurement object, and gluten (see FIG. 5 (b)) or starch as a sample (see FIG. 5). EEM is measured for (see (c), (d)). Here, the starch consists of a powdery sample (see FIG. 5 (c)) and a dumpling sample (see FIG. 5 (d)) with a water content of 70%, for example, with water added at the same ratio as the bread dough. Prepare two types. Then, the measurer packs the object to be measured and the sample in a measurement cell as shown in FIG. 6, and takes a fluorescent image using a rectangular area of, for example, about 1.9 × 1.5 mm surrounded by a square as a measurement location. To measure the EEM. Then, as shown in FIG. 7, the measurer measures the EEM for each of 1344 × 1024 pixels (pixels) within the measurement range of, for example, 561 fluorescent images taken. At this time, the measurer measures the measurement object and the sample cross section while changing the combination of the excitation wavelength and the fluorescence wavelength. Here, as shown in FIG. 8, for example, when EEM data measured from a cross section of a sample is viewed for one pixel, the EEM is for that pixel.

  That is, in the excitation / fluorescence matrix information acquisition step, a plurality of fluorescence images with different excitation wavelengths and observation fluorescence wavelengths are photographed for the measurement object and sample, and the measurement object is obtained from the fluorescence intensity of each pixel in the fluorescence image. And excitation / fluorescence matrix information at each measurement location on the sample.

  Returning to FIG. 3, in the component distribution analysis method of the present invention, in the data analysis process, in order to efficiently extract only necessary information from the EEM measured for the number of pixels in step S2, principal component analysis is performed. It analyzes using (step S3).

  That is, in the data analysis process of step S3, principal component analysis is performed on the excitation / fluorescence matrix information acquired for each measurement location in the excitation / fluorescence matrix information acquisition process of step S2, and the principal component analysis is performed. Create a principal component score plot by plotting the principal component score, which is the value of the component to be measured and the sample, as coordinates, and determine the solvent axis with a different solvent content in the sample on the principal component score plot. Among the plot groups indicating the components of the sample, the axis passing through the centroid of the first plot group having a high solvent content and the centroid of the second plot group having a low solvent content, or the sample for determining the solvent axis The approximate straight line of the plot group is set as the solvent axis, the axis perpendicular to the solvent axis is set as the component axis that is not affected by the solvent content, and the measurement target And sample a plot showing the component by projecting onto the component axis, to parameterize the difference of the components of each measurement point (numerically).

  In the present invention, “principal component analysis” is one of information compression methods that accurately expresses the variation of a set of variable groups (multivariate data) with as few variables as possible. There are various methods for compressing information depending on the relationship between variables, but the principal component analysis focuses on the correlation between variables, and is used when there is no external standard. . The external standard is a variable representing an event to be predicted, and specifically indicates a component content (sugar content, acidity, protein content, etc.), a physical index (hardness, temperature, etc.) and the like.

  Here, with reference to FIG. 9, the detail of an example of the data analysis process of step S3 is demonstrated.

  As shown in FIG. 9, in the data analysis step, first, a principal component analysis is performed to summarize multivariate data into a small number of principal components, and each measurement is performed on a plane having the first principal component and the second principal component as axes. Create principal component score plots that locate objects and samples.

  In the principal component score plot shown in FIG. 9, the difference between each measurement object and each sample is visually expressed. Specifically, in the main component score plot of the measurement object and sample measured by EEM shown in FIG. 9, the sample contains gluten (the cross mark in FIG. 9), powdered starch and hydrolyzed starch. Types of starch (circled and squared plots in FIG. 9) and bread dough (crossed plot in FIG. 9) are distributed as “measuring objects”. Of the samples, powdered starch and hydrolyzed starch correspond to “sample for determining the solvent axis”.

  In the main component score plot shown in FIG. 9, the plots of powdered starch and hydrolyzed starch are arranged on a straight line. As shown in FIG. 9, the powdered starch and the hydrolyzed starch differ only in the amount of water that is the solvent content, and the amount of water changes along this straight line. Thereby, in the main component score plot shown in FIG. 9, the centroid of the distribution of hydrolyzed starch (in FIG. 9, the first plot group) and the centroid of the powdered starch distribution (in FIG. 9, the second plot group). And the axis through these centroids can be subtracted. That is, the difference between the two starch samples is only in the water content, and this axis represents the difference in the water content, so that it can be set as a “solvent axis” (water axis) as shown in FIG. Further, in the present embodiment, in the main component score plot shown in FIG. 9, the distribution of hydrolyzed starch (first plot group in FIG. 9) and the distribution of powdered starch (second plot group in FIG. 9). It is also possible to draw an approximate line passing through and set this approximate line as the “solvent axis”. Here, as an example, the approximate straight line may be subtracted from two groups of high and low plot groups having a high content ratio and low plot groups, or may be subtracted from plots having various content ratios scattered. Good.

  Subsequently, as shown in FIG. 9, an axis orthogonal to the “solvent axis” can be set as the “component axis” (gluten-starch axis). Thereby, in the component axis | shaft orthogonal to a solvent axis | shaft, since the influence of the water | moisture content which is a solvent is removed, the difference of gluten and starch can be analyzed more clearly. In FIG. 9, by projecting a plot (principal component score) of each pixel indicating the dough, gluten, and powdered starch other than hydrolyzed starch on the component axis, Differences can be determined.

  As described above, in the data analysis process of step S3, the excitation / fluorescence matrix information of each pixel is preferably compressed into two-dimensional information by principal component analysis. While compressing the excitation / fluorescence matrix information of each pixel from n-dimensional (for example, 561-dimensional in FIG. 7) to two-dimensional information using principal component analysis, the characteristics of the excitation / fluorescence matrix information are maintained. In the color determination process and component distribution visualization process in step S4 described later, it becomes possible to visualize the difference in excitation / fluorescence matrix information by the difference in color.

  Returning to FIG. 3, in the component distribution analysis method of the present invention, in the color determination step, the color of each pixel is determined from the result of the principal component analysis in step S3, and further in the component distribution visualization step, according to the determined color. An image is created by coloring (step S4).

  That is, in the color determination process in step S4, the color to be colored at each measurement location is determined based on the plot projected on the component axis in the data analysis process in step S3. In the component distribution visualization process of step S4, the component distribution of the measurement object and the sample is visualized by color-mapping the color to be colored at each measurement location determined in the color determination process.

  Here, with reference to FIG. 10 and FIG. 11, the detail of an example of the color determination process of step S4 and a component distribution visualization process is demonstrated.

  As shown in FIG. 10, in the color determination step, the component axis is extracted, and the color axis is applied to the component axis, whereby the color is determined based on the plot position on the component axis. As a result, the principal component analysis values of the EEM (with respect to each plot of the measurement object and the sample other than the first plot group of the solvent axis determination sample projected on the component axis (in FIG. 9, hydrolyzed starch) ( Since the color can be determined from the principal component score), an image in which the same component is shown in the same color as shown in FIG. 11 can be created.

  Therefore, as shown in FIG. 11, images of bread dough, gluten, and starch can be obtained in the component distribution visualization step. In the image of bread dough shown in FIG. 11 (a), the portion showing blue near gluten shown in FIG. 11 (b) is rich in gluten, and the portion showing red near starch shown in FIGS. 11 (c) and 11 (d) There is a lot of starch. Thus, this invention can visualize the gluten distribution which is a specific component in the bread dough of a measurement object.

  As described above, the component analysis method of the present invention measures the EEM of gluten, starch and bread dough, analyzes using principal component analysis, and in the principal component score plot which is the result of principal component analysis, Extract the component axis orthogonal to, discriminate gluten and starch on the component axis, visualize the gluten distribution in the dough by coloring each plot along the color axis with the color axis corresponding to this component axis .

  In the present embodiment, the component distribution analysis method of the present invention has been described by taking as an example the visualization of the component distribution of the cut surface of bread dough, gluten, and starch. Various conditions such as an information compression method such as principal component analysis and color mapping can be selected as appropriate without departing from the object of the present invention.

[II] Component Distribution Analysis System Next, the configuration of the component distribution analysis system of the present invention will be described with reference to FIGS. The fluorescence imaging apparatus and the component distribution analyzer provided in the component distribution analysis system of the present invention can be suitably used for the above-described component distribution analysis method of the present invention. The measuring device is not limited to this.

  Here, FIG. 12 is a block diagram showing an example of the configuration of the component distribution analysis system of the present embodiment, and conceptually shows only the portion related to the present invention in the configuration.

  As shown in FIG. 12, the component distribution analysis system includes a fluorescence image capturing device 10 and a component distribution analysis device 20. The fluorescence imaging device 10 is a device that acquires excitation / fluorescence matrix information, and includes a spectral illumination device 11 and a spectral imaging device 12. The component distribution analyzer 20 is a device that analyzes and visualizes the component distribution of the measurement object / sample 13 from the excitation / fluorescence matrix information acquired by the fluorescence imaging apparatus 10, and includes a memory 21, a control unit 23, and a calculation. A processing unit 24 is provided, and a measurer inputs measurement conditions and the like to the component distribution analyzer 20 using a keyboard / mouse 22.

  In other words, in the present invention, the spectral illumination device 11 irradiates the measurement object and the sample extracted from the measurement object with a predetermined excitation wavelength, and the spectroscopic imaging device 12 uses the measurement object and the sample at a predetermined fluorescence wavelength. Observe. The component distribution analyzer 20 includes a spectral illumination device 11 and a spectral imaging device 12, and analyzes the component distribution of the measurement object and the sample.

  Here, FIG. 13 is a block diagram showing an example of the fluorescent image photographing device 10, and conceptually shows only a part related to the present invention in the configuration.

  As shown in FIG. 13, the fluorescent image photographing device 10 includes a spectral illumination device 11 and a spectral photographing device 12. The spectroscopic illumination device 11 is a device that irradiates the measurement object / sample 13 with excitation light having a predetermined wavelength to generate fluorescence from the components of the measurement object / sample 13. The spectral illumination device 11 has excitation wavelength variable means for arbitrarily changing the excitation wavelength to be irradiated.

  The spectroscopic imaging device 12 is a device that captures a fluorescence image of the measurement object / sample 13 at a predetermined fluorescence wavelength and transmits the image information to the component distribution analyzer 20. The spectroscopic imaging device 12 selectively captures a specific fluorescence wavelength among the fluorescence emitted from the measurement object / sample 13 and captures a fluorescence image. The spectroscopic imaging device 12 has a fluorescence wavelength variable means for arbitrarily changing the observed fluorescence wavelength.

  Here, FIG. 14 is a perspective view showing an example of the fluorescent image photographing apparatus 10.

  In the example of FIG. 14, the spectral illumination device 11 includes a light source 110, a spectral device 112, an excitation wavelength adjustment unit 114, an optical fiber 116, and an excitation light irradiation unit 118. The spectral imaging device 12 includes an optical unit 120, a spectral device 122, and an image imaging device 124.

  As the light source 110 of the spectral illumination device 11, a xenon lamp, a tungsten lamp, a wavelength tunable laser, or the like can be used. The light emitted from the light source 110 is split into excitation light having a predetermined wavelength in the spectroscopic device 112. The measurer can set the excitation light to an arbitrary wavelength by adjusting the excitation wavelength adjusting unit 114. When a wavelength tunable laser is used as the light source 110, excitation light having a desired wavelength can be obtained directly from the light source, so that the spectroscopic device 112 is unnecessary.

  As the spectroscopic device 112, an AOTF (Acoustic Optical Tunable Filter), a liquid crystal tunable filter, an interference filter, a diffraction grating, or the like can be used.

  The light converted into the excitation light having a predetermined wavelength in the spectroscopic device 112 is sent to the excitation light irradiation unit 118 via the optical fiber 116, and is irradiated to the measurement object / sample 13 from the excitation light irradiation unit 118. By irradiating excitation light having a predetermined excitation wavelength, the measurement object / sample 13 emits a fluorescence having a component-specific pattern.

  The measurement object / sample 13 is enlarged to a size suitable for image capturing by the optical unit 120. The optical unit 120 is, for example, a microscope, a zoom lens, a macro lens, a wide angle lens, or the like. If the measurement object / sample 13 has a sufficient size for taking an image, the image can be taken without using the optical unit 120.

  By irradiating the excitation light, the fluorescence emitted from the measurement object / sample 13 is split into light having a predetermined wavelength by the spectroscopic device 122. The spectroscopic device 122 has means for arbitrarily setting the fluorescence wavelength to be observed. As the spectroscopic device 122, an AOTF (Acoustic Optical Tunable Filter), a liquid crystal tunable filter, a PGP (prism, grating, prism), an interference filter, a diffraction grating, or the like can be used.

  By passing through the spectroscopic device 122, only the light having a predetermined wavelength among the fluorescence emitted from the measurement object / sample 13 is captured as an image by the image capturing device 124. As the image photographing device 124, a CCD camera, a scanning image camera, or the like can be used.

From the brightness of each pixel of the image obtained by the image capturing device 124, the fluorescence intensity at each measurement location on the measurement object / sample 13 is determined. That is, for each measurement location on the measurement object / sample 13, from the brightness of the pixel at the corresponding location of each measurement location, at a specific excitation wavelength λ _Ex (irradiation wavelength) and a specific fluorescence wavelength λ _Em (observation wavelength) Information on the fluorescence intensities I _{Ex and Em} is obtained. Therefore, by using the fluorescence imaging apparatus 10, the fluorescence intensity at a specific excitation wavelength and fluorescence wavelength can be measured at a time at all points (pixels) on the measurement object / sample 13.

  When obtaining n-dimensional excitation / fluorescence matrix information at each point on the measurement object / sample 13, n fluorescence images are taken while changing the combination of the excitation wavelength and the fluorescence wavelength. The image information of the n fluorescent images photographed by the image photographing device 124 is transferred to the component distribution analyzing device 20.

  The excitation wavelength to be irradiated and the fluorescence wavelength to be observed can be arbitrarily changed manually or automatically by the measurer. Preferably, the fluorescence imaging apparatus 10 includes means for automatically changing the excitation wavelength and the fluorescence wavelength.

  Here, returning to FIG. 12, the component distribution analyzer 20 will be described.

  As shown in FIG. 12, the component distribution analyzer 20 acquires excitation / fluorescence matrix information for each pixel from the image information of n fluorescence images captured by the image capturing device 124 of the spectral capturing device 12 ( In FIG. 12, the processing by the excitation / fluorescence matrix measurement unit 24-1). Next, principal component analysis is performed on the obtained excitation / fluorescence matrix information to create a principal component plot, a component axis orthogonal to the solvent axis is set on the principal component plot, and the solvent content of the solvent axis determination sample is determined. The difference between the components at each measurement location is parameterized by projecting a plot indicating the components of the measurement object and the sample other than the high first plot onto the component axis (in FIG. 12, the processing by the data analysis unit 24-2 ). Next, a color to be colored at each measurement location is determined based on the plot projected on the component axis, and the determined color is displayed with pixels corresponding to each measurement location, and color mapping is performed. Is visualized (processing by the component distribution visualization unit 24-3 in FIG. 12). Details of the excitation / fluorescence matrix measurement unit 24-1, the data analysis unit 24-2, and the component distribution visualization unit 24-3 will be described later in the description of the calculation processing unit 24.

  The memory 21 is transferred from the image capturing device 124 to the component distribution analysis device 20, and the image information acquired by the excitation / fluorescence matrix measurement unit 24-1 of the calculation processing unit 24 and the data analysis unit 24-of the calculation processing unit 24. 2 stores data (for example, target component information extracted from excitation / fluorescence matrix information) indicating the difference in components at each measurement location parameterized from the fluorescence characteristics extracted by principal component analysis.

  The control unit 23 irradiates the spectral illumination device 11 with the excitation wavelength and the spectral wavelength so as to capture the fluorescence image of the measurement object / sample 13 in the excitation wavelength range, fluorescence wavelength range, and wavelength pitch input by the measurer. The main component analysis is performed on the data analysis unit 24-2 of the calculation processing unit 24 so as to give an instruction to adjust the fluorescence wavelength observed by the imaging device 12 and to extract target component information from the obtained excitation / fluorescence matrix information. Command to do.

  The excitation / fluorescence matrix measurement unit 24-1 of the calculation processing unit 24 acquires the excitation / fluorescence matrix information (that is, transferred from the fluorescence imaging apparatus 10) at a plurality of measurement locations on the measurement object / sample 13. The image information of n fluorescent images) is stored in the memory 21 of the component distribution analyzer 20. When a measurer instructs the principal component analysis to the data analysis unit 24-2 of the calculation processing unit 24 through the keyboard / mouse 22, the data analysis unit 24-2 of the calculation processing unit 24 stores the n stored in the memory 21. N-dimensional excitation / fluorescence matrix information is extracted for each pixel from the image information of the single fluorescence image. Next, the data analysis unit 24-2 of the calculation processing unit 24 performs n-dimensional excitation / fluorescence matrix information obtained for each pixel by the principal component analysis method designated by the measurer, preferably two-dimensional information. Compress. The excitation / fluorescence matrix information compressed in two dimensions is stored in the memory 21.

  That is, the data analysis unit 24-2 performs principal component analysis on the excitation / fluorescence matrix information acquired for each measurement location by the excitation / fluorescence matrix measurement unit 24-1, and the measurement target represented by the principal component The principal component score plot is created by plotting the principal component score, which is the value of the product and the component of the sample, as coordinates, and on the principal component score plot, the solvent axis determination sample having a different solvent content contained in the sample is created. Among the plot groups indicating components, an axis passing through the centroid of the first plot group having a high solvent content and the centroid of the second plot group having a low solvent content, or a plot group of samples for determining the solvent axis Is set as the solvent axis, the axis perpendicular to the solvent axis is set as the component axis that is not affected by the solvent content, and the measurement object and sample components are By projecting to plot on the component axis, to parameterize the difference of the components of each measurement point.

  When the measurer inputs an instruction to perform color mapping to the control unit 23 through the keyboard / mouse 22, the control unit 23 stores the principal component analysis stored in the memory 21 in the component distribution visualization unit 24-3 of the calculation processing unit 24. Data indicating the difference in components at each measurement location parameterized from the fluorescence characteristics extracted by (for example, a plot projected on the component axis indicating target component information extracted from excitation / fluorescence matrix information) Reading and instructing each pixel to designate a color to be colored based on the color index designated by the measurer.

  When the color to be colored for each pixel is determined, the component distribution visualization unit 24-3 of the calculation processing unit 24 transmits information on the color to be colored to each pixel to the display 30. By color-mapping each pixel on the display 30, the component distribution of the measurement object is visualized. Note that the component distribution visualization is not necessarily performed on the display 30, and any method that can visualize the component distribution of the measurement object, such as printing a component distribution visualization image with a printer, may be used.

  That is, the component distribution visualization unit 24-3 determines the color to be colored at each measurement location based on the plot projected on the component axis by the data analysis unit 24-2, and the color to be colored at each measurement location. By color mapping, the component distribution of the measurement object / sample 13 is visualized.

  Then, the Example in this component distribution analysis system mentioned above is described with reference to FIG.5, FIG.6, FIG.8 and FIGS.

[Step 1. Sample preparation]
(1-1. Preparation of flour fraction)
In the present embodiment, the component analysis system measures bread dough as a measurement object and gluten or primary starch EEM as a sample as a sample. Therefore, in step 1, a measurement object and sample creation process by the measurer will be described. In addition, this process 1 respond | corresponds to above-mentioned step S1 of FIG.

  In this process 1, two types of bread dough, which is a measurement object that is the basis for creating a sample wheat flour fraction, were prepared: bread dough with 2% salt added and bread dough without salt added. Since the bread dough added with salt is close to the actual bread dough, it becomes a measurement object close to the manufacturing site. In addition, salt-free bread dough becomes a simpler system and has an advantage that analysis of target substances such as gluten and starch in a sample extracted from a measurement object becomes easy.

  The method for preparing the wheat flour fraction of the sample is described below.

  Add 1400 ml of water (tap water) to 2 kg of flour (camellia (trade name), Nissin Foods (company name)), add 40 g of salt to the salt-added bread dough, and add a mixer (SK mixer (company name), D-TM-40 (model number)) was mixed at a low speed (100 rpm) for 2 minutes and at a medium low speed (200 rpm) for 6 minutes. When mixing for a total of 8 minutes, the bread dough of the measurement object was in a state where the elasticity was maximized from the middle to the latter half of the development stage (see 1-2 described later).

  The mixed dough of the measurement object was placed in a bowl filled with solvent water and rested for 30 minutes, and then the dough was kneaded in water. As a result of this work, sample starch and soluble proteins were released from the bread dough to be measured, and only the sample gluten remained. While changing the water, both salt-free bread dough and salt-added bread dough were squeezed for about 60 minutes. After squeezing for about 60 minutes, the water did not become cloudy with starch granules even if it was put in water and squeezed. The gluten of the sample thus obtained was cut into a 2 cm square lump, wrapped in wrap, and stored frozen in a -40 ° C freezer. About two weeks after the preparation of the sample, the sample was transferred to a -80 ° C freezer and stored until use.

  A substance such as starch, which becomes a sample released into the solvent water when the dough as the measurement object was kneaded, was centrifuged to separate it into a precipitate and a liquid. As a result of centrifugation, primary starch (PS), which is a pure starch component, was precipitated in the portion near the bottom as shown in FIG. A gel-like tailing starch containing small starch grains and gluten fragments, broken starch, lipids, ash and the like was deposited on the upper part of the precipitate. Soluble proteins etc. were dissolved in the liquid part. The precipitate was divided into primary starch and tailing starch as samples, and each was freeze-dried. The liquid part was also lyophilized to obtain a powdery substance containing a water-soluble protein as a sample. All were stored frozen at −20 ° C. until use.

  Hereinafter, the pure starch content that is a sample fractionated from the bread dough of the measurement object is called “primary starch (PS)”, and the starch as a component is called “starch” (for example, present in the bread dough of the measurement object) The substance to do is “starch”, not “primary starch”).

  FIG. 5 described above shows a photograph of bread dough as a measurement object, primary starch of a sample, and gluten. The bread dough shown in FIG. 5 (a) and the gluten shown in FIG. 5 (b) are samples immediately before measurement, which were thawed once frozen. The primary starch shown in FIG. 5 (d) is a state in which distilled water as a solvent is added to a freeze-dried powder sample and mixed. The gluten of the sample was strong and returned to its original shape as soon as it was stretched. The elasticity of bread dough as a measurement object was weaker than gluten, and had extensibility. The primary starch of the sample was not elastic even when water was added, and it tended to become granular when a small amount of water was added.

(1-2. Preparation of bread dough sample)
Separately from the flour fraction of the sample, bread dough was prepared as an object to be measured at various mixing stages. It was created to compare the shape of gluten and the state of bread dough.

  Flour and water were mixed at a mass ratio of 10: 7, and kneaded using a mixer, and the dough was sampled in a total of 9 stages: pickup, cleanup, development (3 stages), final (2 stages), over, and letdown.

  Each was divided into small pieces of about 7 g, placed on an aluminum tray and placed in a freezer. When solidified, they were placed in a plastic bag with a chuck and stored in a freezer at −80 ° C.

  The state of the dough of the measurement object at each stage of mixing will be described below.

  The pick-up stage was a stage where water spreads to the flour and the powdery portion disappeared, and the moisture distribution in the bread dough was uneven.

  At the cleanup stage, the dough became uniform and separated from the mixing ball as one. However, because gluten has not yet developed much, it was cut immediately when force was applied in the direction of stretching.

  Gluten was formed through the development stage, and the viscoelasticity of the dough became the maximum from the latter half of the development stage to the final stage. The bread dough stretched thinly like a membrane.

  From the final to the over, the bread dough became easier to grow, but gradually lost its elasticity and became sticky.

  And when it reached the letdown stage, the stickiness became severe, and when it was pulled, it became long and thin.

(1-3. Summary of step 1)
As described above, in step 1, the gluten of the sample was extracted by squeezing the bread dough of the measurement object in which the measurer kneaded strong powder and water in water as a solvent. Then, the starch that flowed into the solvent water during the sample extraction was subjected to a centrifugal separator, and the pure starch precipitated on the bottom was freeze-dried to obtain a primary starch of the sample.

  In Step 1, the dough as a measurement object was sampled in a total of 9 stages: kneading flour and water, picking up, cleaning up, developing (3 stages), final (2 stages), over, and letdown.

  This is the end of the description of step 1.

[Step 2. EEM measurement by spectrofluorometer and gluten discrimination conditions]
(2-1. Purpose of Step 2)
Since the final purpose of the present embodiment is to visualize the gluten of the sample, first, based on the EEM acquired by the excitation / fluorescence matrix measurement unit 24-1, the data analysis unit 24-2 performs the gluten analysis of the sample. It is necessary to investigate whether or not can be distinguished from other components in the bread dough of the measurement object. Therefore, in step 2, the EEM of the bread dough fraction such as gluten and primary starch of the sample is individually measured by the excitation / fluorescence matrix measurement unit 24-1, and the EEM is subjected to principal component analysis by the data analysis unit 24-2. Through analysis, we aimed to establish a method for discriminating gluten from other components. This step 2 mainly corresponds to the processing in step S2 by the excitation / fluorescence matrix measurement unit 24-1 in FIG. 3 and the processing in step S3 by the data analysis unit 24-2.

(2-2. Materials and Methods of Step 2)
(2-2-1. Spectral Fluorometer)
The spectrofluorometer is a device for measuring a fluorescence spectrum when a sample is irradiated with excitation light having a specific wavelength. In the spectrofluorometer (F-7000 (model number), Hitachi High-Technologies (company name): hereinafter F7000) used in Step 2, the fluorescence spectrum at each excitation wavelength is measured while changing the wavelength of the excitation light, and the EEM is measured. Can be measured. Since the fluorescence spectrum of the entire measurement object and sample is measured, one EEM is obtained for each measurement object and sample. Note that the spectrofluorometer of the present embodiment corresponds to the fluorescence image capturing apparatus 10.

  FIG. 16 shows an optical system of F7000. In FIG. 16, L is a lens, S is a slit, and BS is a beam splitter. The light emitted from the xenon lamp used as the light source is condensed on the entrance slit S1 of the excitation side spectroscope by the lenses L1 and L2. The light that has passed through S1 is split by the diffraction grating and exits from the excitation slit through the exit slit S2. A part of the light is separated by the beam splitter BS, irradiated to the detector, and the amount of excitation light is measured. The remaining excitation light passes through the lens L3 and is collected on the sample cell. In addition, the excitation side spectroscope of the present embodiment corresponds to the spectroscopic illumination device 11.

  Fluorescence emitted from a sample (for example, a measurement object and a sample) is collected by lenses L4 and L5 and enters a fluorescence side spectroscope through slit S3. In the fluorescence spectrometer, the light is dispersed by a diffraction grating, passes through the exit slit S4, and finally enters a photomultiplier tube (hereinafter referred to as “photomultiplier”), where it is amplified and the intensity is measured. In addition, the fluorescence side spectroscope and the photomultiplier of the present embodiment correspond to the spectroscopic imaging device 12.

  In F7000, measurement conditions can be manipulated when measuring EEM. The measurement conditions that can be operated are described below.

  Photomal is a machine that amplifies and detects weak fluorescence emitted from a sample, and the multiplication factor can be increased by increasing the voltage applied to the photomal. In F7000, the photomultiplier voltage can be selected from four levels of 250V, 400V, 700V, and 950V. With respect to the photomultiplier voltage of 400V, the amplification factor is about 100 times at 700V. However, noise is amplified along with the fluorescence of the sample.

  The excitation side slits are S1 and S2 in FIG. 16, and the fluorescence side slits are S3 and S4 in FIG. The slit width can be selected from 1.0 nm, 2.5 nm, 5.0 nm, 10.0 nm, and 20.0 nm. The amount of excitation light applied to the sample can be adjusted by the excitation side slit width, and the amount of fluorescence entering the detector can be adjusted by the fluorescence side slit width. It is better to be able to measure strong fluorescence as long as it does not exceed the allowable signal intensity of Photomal, but if the fluorescence side slit width is too large, the wavelength resolution of the fluorescence spectrum will deteriorate, or if the excitation side slit width is large and the excitation light is too strong The sample may be deteriorated. Therefore, it is necessary to adjust the slit width and the photomultiplier voltage to find a condition that can provide sufficient fluorescence intensity and can maintain accuracy.

  The maximum wavelength ranges of excitation light and fluorescence are both 200 nm to 900 nm, the wavelength interval is 1 to 50 nm for fluorescence, and the wavelength interval for fluorescence is 1 to 10 nm. Since the spectrophotometer and photomultiplier of the spectrofluorometer have wavelength characteristics, it is necessary to perform correction in order to accurately measure the spectrum specific to the sample, and the device function for performing correction varies depending on the wavelength band. Therefore, when the measurement wavelength range is increased, there is a high possibility that elements other than the fluorescence of a pure sample such as device characteristics and device functions will be included in the measurement value. It is necessary to determine a wavelength band that is not excessive or insufficient to sufficiently capture the characteristics of the sample.

  The scan speed indicates a wavelength range that can be measured in one minute, and can be set from 30 nm to 60000 nm.

  The response is the time from when the sample is irradiated with the excitation light until the fluorescence is measured, and is automatically determined according to the scan speed or manually set in the range of 0.002 to 8.0 seconds. You can also

(2-2-2. Analysis software)
FL-Solutions (trade name) was used to read the measured values of the spectrofluorometer, Microsoft Office Excel 2003 (trade name) was used to describe the data, and MATLAB (registered trademark) was used to analyze the data. FL-Solutions is software attached to the spectrofluorometer F7000 used in this step 2, and can perform control of F7000, spectrum correction, data output, peak detection, and the like. In this step 2, it was used only for controlling F7000 and correcting the spectrum, and the data was output in an Excel format. The processing of this analysis software mainly corresponds to the processing of the excitation / fluorescence matrix measurement unit 24-1 and the data analysis unit 24-2 of the calculation processing unit 24.

  Numerical analysis software (MATLAB R2007b (trade name), The MathWorks, Inc. (company name)) was used for all data analysis. MATLAB (registered trademark) is software for numerical calculation, and has an abundant library for matrix calculation, data analysis, image processing, and the like. Data stored using Microsoft Excel (trade name) was read, principal component analysis was performed, and MATLAB (registered trademark) was used in the process of outputting as a graph or the like. In accordance with the output data such as the graph of the principal component score and the graph of the principal component load amount, a program was organized as a series of flow from data reading to result output.

(2-2-3. Measurement sample)
Samples to be measured by the excitation / fluorescence matrix measurement unit 24-1 (for example, measurement objects and samples) are the gluten and primary starch of the sample that is the bread dough fraction of the measurement objects, and the bread dough itself of the measurement objects. . These samples were put in the measurement cell (powder holder) of F7000 shown in FIG. 6 and measured.

  The sample gluten consists of two types: gluten extracted from the bread dough of the measurement object to which salt is added (gluten added) and gluten extracted from the bread dough of the measurement object to which no salt is added (gluten-free gluten). Five samples were prepared for each. What was frozen and stored in a freezer at −80 ° C. was thawed at room temperature, placed in a measurement cell, and measured by the excitation / fluorescence matrix measurement unit 24-1.

  Sample primary starch was lyophilized and thawed at -20 ° C. at room temperature, and powdered primary starch without adding solvent water, and powdered primary starch and solvent distilled water 10: 2 Seven types of samples mixed at a ratio of 10: 3, 10: 4, 10: 5, 10: 6, 10: 7 were prepared. Five samples of powdery primary starch and 5: 1 primary starch with a solvent content (powder to water ratio) of 10: 7 were prepared, and two samples were prepared for the others. The flour-to-water ratio of 10: 7 is equal to the flour-to-water ratio of the bread dough itself. If more water is added, the water separates and exudes from the gaps in the measurement cell.

  As the bread dough to be measured, a final stage product (see 1-2 above) in which gluten was sufficiently developed was used. What was frozen and stored in a freezer at −80 ° C. was thawed at room temperature, and 5 samples were measured by the excitation / fluorescence matrix measurement unit 24-1.

(2-2-4. Measurement conditions)
FIG. 17 shows the measurement conditions at F7000.

  Since the peak of the fluorescence spectrum of the amino acid constituting the gluten of the sample can be detected in the range of 700 nm or less, the measurement range was set to 200 to 700 nm.

  In the preliminary experiment, the slit width was set to 5 nm and 10 nm, and measurement was performed by the excitation / fluorescence matrix measurement unit 24-1. As a result, the data measured with the 10 nm slit clearly identified the gluten and the primary starch of the sample. . Therefore, a 10 nm slit was adopted in this step 2.

  The photomultiplier voltage can be selected between 400V and 700V, but at 700V, there was a wavelength condition where the fluorescence intensity exceeded the maximum value, so it was set to 400V.

  The response was set to a minimum of 0.002 seconds.

(2-2-5. Pre-processing of data)
The EEM measured by the excitation / fluorescence matrix measurement unit 24-1 using F7000 cannot be subjected to principal component analysis by the data analysis unit 24-2 as it is, so it is necessary to perform preprocessing on the data. Become. The preprocessing mainly includes deletion of data originating from other than fluorescence and rearrangement of data. The preprocessing is performed in the excitation / fluorescence matrix measurement unit 24-1 of the calculation processing unit 24.

  When the sample absorbs excitation light and emits fluorescence, the fluorescence wavelength becomes longer than the absorbed excitation light. This is because the fluorescence energy of the molecule is always less than or equal to the excitation light energy according to Stokes' law, so that the fluorescence has a longer wavelength than the excitation light. In the EEM measurement using F7000, in order to investigate all the excitation light and fluorescence in the set wavelength range, there is data in which the fluorescence wavelength is shorter than the excitation wavelength.

  Furthermore, when excitation light is applied to a sample such as powder, scattered light having the same wavelength as that of the excitation light, and secondary light and tertiary light having a wavelength twice or three times that of the excitation light are observed. Analysis was carried out except for the data.

  Since the data from which the influence of the scattered light is removed by the excitation / fluorescence matrix measurement unit 24-1 becomes three-dimensional data as it is, the combination of the excitation wavelength and the fluorescence wavelength is treated as a “wavelength condition”, and the data analysis unit 24-2 To develop two-dimensional data. This is because when performing principal component analysis on a certain data set, the data set needs to be described in a two-dimensional matrix of (measurement object / number of samples) × (variable number). As a result, a total of 10201 wavelength conditions consisting of (101 excitation wavelength) × (101 fluorescence wavelength) were obtained.

(2-2-6. Principal component analysis)
The principal component analysis was performed by the data analysis unit 24-2 on the data preprocessed by the excitation / fluorescence matrix measurement unit 24-1 in the above 2-2-5.

  The principal component analysis performed in the data analysis unit 24-2 includes a process of calculating eigenvectors for each variable from the data, and a process of obtaining the principal component score of the data from the eigenvectors. The calculation of eigenvectors can be said to be a process of creating a “measurement” that explains the data, and the calculation of the principal component score is a process of calculating the position of each sample using the “measurement”. Therefore, it is possible to create a “measurement” by calculating eigenvectors in one data set and apply the “measurement” to another data set if the measurement variable (in this example, the wavelength condition) is the same. Become.

  In this step 2, the EEM of 15 samples in total, which is obtained by the excitation / fluorescence matrix measurement unit 24-1, is 5 samples each including 5 samples of salt-free gluten, salt-added gluten, and primary starch with a powder to water ratio of 10: 7. From the data, a principal component eigenvector was obtained by the data analysis unit 24-2, and using the eigenvector, two types of gluten, seven types of primary starch, and a bread dough score of the measurement object were obtained. That is, the data analysis unit 24-2 creates a “measuring” from the main components constituting the bread dough of the measurement object, ie, gluten and starch as samples, and examines the position of each sample in the “measuring”.

(2-3. Results and discussion of step 2)
(2-3-1. EEM)
Sample obtained with the excitation / fluorescence matrix measurement unit 24-1, salt-free gluten, salt-added gluten, primary starch without added solvent water, solvent content (powder to water ratio) 10: 7 primary starch And EEM about each of the bread dough of a measuring object is shown in FIGS.

  As shown in FIGS. 18 and 19, there is almost no difference between the EEM of the sample-free salt-added gluten and the sample of the salt-added gluten obtained by the excitation / fluorescence matrix measurement unit 24-1, and the excitation wavelength. / Fluorescence wavelength: A high fluorescence peak is seen at 300 nm / 330-340 nm, and a 200-220 nm / 330-340 nm, 300 nm / 690 nm lower peak is seen.

  As shown in FIG. 20, in the EEM of the primary starch obtained by the excitation / fluorescence matrix measurement unit 24-1 without adding the solvent water, a low peak is seen at the excitation wavelength / fluorescence wavelength: 280 nm / 330 nm, A peak like a peak of high fluorescence intensity is seen near an excitation wavelength of 200 nm. In particular, in the vicinity of the fluorescence wavelength of 320 nm, it is estimated that a peak exists in the range of the excitation wavelength of 200 nm or less. Excitation wavelength / fluorescence wavelength: The peak at 280 nm / 330 nm is not clear compared to the gluten shown in FIGS.

  As shown in FIG. 21, the EEM of the primary starch having a solvent content (powder to water ratio) of 10: 7 acquired by the excitation / fluorescence matrix measurement unit 24-1 is shown in FIG. Same as primary starch without added solvent water, but with lower peak height. In other words, it does not show a new fluorescence peak due to the addition of solvent water, but simply indicates that the fluorescence of the primary starch of the sample is weakened.

  As shown in FIG. 22, the EEM of the bread dough of the measurement target obtained by the excitation / fluorescence matrix measurement unit 24-1 is close to the EEM of the sample gluten as shown in FIGS. / Fluorescence wavelength: A clear peak is observed at 290 nm / 340 nm. In addition, the fluorescence in the range of 200 to 250 nm / 330 nm is stronger than the gluten EEM shown in FIGS. In the EEM of primary starch shown in FIG. 20 and FIG. 21, since the tail of the peak is seen in this range, this fluorescence peak is considered to be due to the primary starch of the sample.

(2-3-2. Contribution rate)
FIG. 23 shows the contribution ratio, which is the ratio of data explained by each principal component, extracted by the data analysis unit 24-2. As shown in FIG. 23, the contribution ratio of the first principal component exceeds 97%, and most of the data is explained by one axis. On the other hand, after the third main component, the contribution rate is 1% or less. Here, the contribution rate is a variance of the principal component, that is, a value obtained by dividing the eigenvalue by the variance of the entire data. The closer the contribution rate is to 1, the better the principal component represents the original data.

(2-3-3. Principal component loading)
The principal component load amounts from the first principal component to the third principal component extracted by the data analysis unit 24-2 are shown in FIGS. Here, the principal component load amount is a correlation coefficient between the principal component and the original variable, that is, an eigenvector, and indicates the relationship between the principal component and the original variable. By referring to the principal component loading, the principal component can be interpreted. For example, when the principal component loading amount of a certain main component takes a large value in a specific wavelength range, and the fluorescence peak of a substance having a certain wavelength range coincides with the observed wavelength range, the main component can be associated with the substance. it can.

  As shown in FIG. 24, the main component loading amount of the first main component extracted by the data analysis unit 24-2 has negative peaks at excitation wavelengths / fluorescence wavelengths: 300 nm / 340 nm and 300 nm / 690 nm. The positive correlation is large in the wavelength range of 200 to 230 nm. In other words, it can be said that the wavelength band corresponding to the EEM peak of the gluten of the sample has a large negative correlation, and the wavelength band in which the EEM of the sample primary starch shows a high fluorescence intensity has a large positive correlation. It has been shown that these wavelength bands are characteristic of gluten and primary starch, respectively.

  As shown in FIG. 25, in the principal component load amount of the second principal component extracted by the data analysis unit 24-2, the peak position is at the same position as the first principal component shown in FIG. Wavelength / fluorescence wavelength: The center of the peak of 300 nm / 340 nm takes a negative value, and the surroundings take a positive value.

  As shown in FIG. 26, the principal component load amount of the third principal component extracted by the data analysis unit 24-2 is almost opposite to that of the second principal component shown in FIG. A positive peak is seen on the long wavelength side and a negative peak is seen on the short wavelength side.

(2-3-4. Principal component score plot)
FIG. 27 shows a principal component score plot of each measurement object and sample around the principal component scores of the first principal component and the second principal component, created by the data analysis unit 24-2. The gluten and primary starch of the sample can be separated mainly by the first main component, and the solvent content (water content) is mainly explained by the second main component. Here, the principal component score is a value of the measurement object and the sample expressed by a variable called a newly obtained principal component. Principal component score plots position each measurement object and sample with the principal component score as coordinates on a plane with two principal components as axes, so that the difference between the measurement object and the sample explained by the principal component is It is a figure which can be expressed visually.

  More strictly, on the principal component score plot, the primary starch principal component scores of the samples in which the water content of the solvent is changed are arranged on a straight line having a certain slope as shown by the dotted line in FIG. Since the difference between these primary starch samples is explained only by the amount of solvent (water content), this straight line can be defined as the “solvent axis” (water axis) by the data analysis unit 24-2 (FIG. 28). dotted line). Furthermore, in the “component axis” (solid line in FIG. 28) that is set in the data analysis unit 24-2 and is perpendicular to the solvent axis (that is, perpendicular to the solvent axis) (solid line in FIG. 28), the influence of moisture as a solvent is removed. It is considered that the attributes of the measurement object and the sample can be considered without consideration.

  The gluten of the sample is a substance originally contained in the bread dough of the measurement object. However, since the operation of adding water as a solvent is included in the extraction process shown in Step 1 above, There is a possibility that the solvent content (water content) of gluten and the extracted gluten alone are different. Also in the case of the sample primary starch, it is difficult to match the water content of the primary starch measured as the extracted sample alone with the water content of starch in the bread dough of the measurement object. Therefore, when considering the gluten and primary starch of the extracted sample corresponding to the gluten and starch in the bread dough of the measurement object, the data analysis unit 24-2 measures without considering the solvent content (water content). The method of Step 2 for examining the difference between the object and the sample is very effective.

  FIG. 29 shows a principal component score plot of the first principal component and the third principal component created by the data analysis unit 24-2. As shown in FIG. 29, compared to the principal component score plot of the first principal component and the second principal component created by the data analysis unit 24-2, there is a variation in the arrangement of the primary starch samples of the samples. It is difficult to find the “solvent axis”. However, the gluten of the sample, the dough of the measurement object, and the primary starch of the sample can be separated by the first main component.

  In addition, since the presence or absence of salt was not reflected at all in the EEM, the two types of gluten could not be separated even in the principal component analysis by the data analysis unit 24-2.

(2-4. Summary of step 2)
As described above, in step 2, based on the EEM acquired by the excitation / fluorescence matrix measurement unit 24-1, the data analysis unit 24-2 performs gluten of the sample and other components in the bread dough of the measurement target. We investigated the possibility of discrimination and established a method for discrimination.

  Specifically, in step 2, the spectrofluorometer uses the F7000, which is a device for measuring the fluorescence spectrum when the sample is irradiated with excitation light of a specific wavelength, and the excitation / fluorescence matrix measurement unit 24. The EEM can also be measured by -1, and one EEM is measured for one sample (for example, a measurement object and a sample). In step 2, the sample gluten, primary starch, and EEM of the bread dough to be measured are measured by the excitation / fluorescence matrix measurement unit 24-1, and after removing scattered light and adjusting the dimensions, the data analysis unit It was possible to discriminate between the gluten of the sample and other components (mainly primary starch) in the bread dough of the measurement object by performing analysis using principal component analysis according to 24-2. In step 2, the primary starch of the solvent-containing sample with the solvent content (water content) changed is linearly plotted on the principal component score plot of the first principal component and the second principal component by the data analysis unit 24-2. Lined up.

  This is the end of the description of step 2.

[Step 3. Photographing bread dough cross section and visualization of gluten distribution by EEM imaging system]
Subsequently, step 3 will be described below.

(3-1. Purpose of Step 3)
As described above in step 2, gluten and primary starch are obtained by EEM measurement by the excitation / fluorescence matrix measurement unit 24-1 using a fluorescence spectrophotometer and principal component analysis of EEM data by the data analysis unit 24-2. It was distinguishable. Therefore, in this step 3, the method of this step 2 is applied to image measurement, and the component distribution visualization unit 24-3 analyzes the inside of the flat region of the bread dough cross section of the measurement target one pixel at a time. We investigated the gluten distribution of the sample in the bread dough cross section and aimed to display it in the form of a visually understandable image. In addition, this process 3 mainly respond | corresponds to the process of step S4 by the above-mentioned component distribution visualization part 24-3 of FIG.

(3-2. Material and method of step 3)
(3-2-1 Excitation / fluorescence matrix)
There are roughly two types of methods for measuring the excitation / fluorescence matrix (EEM) by the excitation / fluorescence matrix measurement unit 24-1. In the first method, as shown in FIG. 8 described above, the XY scanning method of sequentially measuring the excitation / fluorescence matrix in the region corresponding to one pixel of the sample cross section while changing the position of the element of the measuring instrument in the xy direction. And line scanning method. In the XY scanning method, the excitation / fluorescence matrix of the sample cross section is measured with a single element detector. In the line scanning method, the excitation / fluorescence matrix of a plurality of pixels is measured at once with a detector in which single elements are arranged in a row. To do. However, both methods require time for measurement when the sample cross section is large.

  In the second method, as shown in FIG. 30, the excitation / fluorescence matrix measurement unit 24-1 irradiates the entire cross section of the sample with light having a specific excitation wavelength, and changes the observation fluorescence wavelength one after another for imaging. This is an image capturing method that repeats the above operation while changing the excitation wavelength, and is characterized by rapid image acquisition. In this process 3 as well, this image capturing method is used by the excitation / fluorescence matrix measurement unit 24-1. However, it is difficult to illuminate the sample with completely uniform excitation light, and it may be necessary to remove illumination unevenness.

(3-2-2. EEM Imaging System)
The measuring instrument used in this process 3 is demonstrated below. The outline of the EEM imaging system (EEM Imaging System: EEM-IS) in which the excitation / fluorescence matrix measurement unit 24-1 measures EEM data by an image capturing method is shown in FIG. The EEM imaging system corresponds to the fluorescence image capturing apparatus 10.

  As shown in FIG. 14, the EEM-IS includes a spectral illumination device 11 (spectral illumination unit) and a spectral imaging device 12 (spectral observation unit). The spectroscopic illumination device 11 is a device (a special order product manufactured by Soma Optics (company name)) having a 150-watt xenon lamp light source as a light source 110 and a grating-type spectroscope as a spectroscopic device 112. The wavelength range of excitation light is 200. -1000 nm, wavelength error is ± 1 nm. Further, the half-value width (wavelength range in which the intensity is half of the maximum value of the peak, indicating the width of the peak) is 10 nm.

  The spectroscopic imaging device 12 includes a fluorescent microscope (BXFM (model number), Olympus (company name)) as the optical unit 120, and a liquid crystal tunable filter (VS-VIS2-10-MC-35 (model number), Cambridge Research and) as the spectroscopic device 122. (Instrumentation, Inc. (company name)) and CCD camera (ORCA-ER-1394 (model number) manufactured by Hamamatsu Photonics (company name)) as the image capturing device 124. The liquid crystal tunable filter (LCTF) can change the wavelength of fluorescence transmitted through the filter by changing the voltage applied to the built-in liquid crystal tuning element with a wavelength error of 1.25 nm in the range of 400-720 nm. The half width of the fluorescence is 20 nm. The number of pixels of the CCD camera is 1,344 × 1,024, and the gradation is 12 bits (4096 steps).

  The light emitted from the xenon lamp is split into a single wavelength by the spectroscope and is irradiated to the measurement object / sample 13. Of the fluorescence spectrum emitted from the measurement object / sample 13, only the light having the wavelength transmitted through the liquid crystal tunable filter reaches the CCD camera, and the cross section of the measurement object and the sample 13 is photographed. While keeping the wavelength of the excitation light passing through the spectrometer constant, the fluorescence spectrum under the excitation light can be obtained by taking a sample cross-section while changing the wavelength of the fluorescence that can be transmitted through the liquid crystal tunable filter. It can be measured. Next, the wavelength of the excitation light passing through the spectroscope is changed, and the fluorescence spectrum is measured in the same manner. By repeating this, the EEM can be measured by the excitation / fluorescence matrix measurement unit 24-1.

(3-2-3. Measurement sample)
Samples to be measured by the excitation / fluorescence matrix measurement unit 24-1 (for example, measurement objects and samples) include gluten added with salt as a sample, and primary starch set to two solvent contents (water content) as samples. The bread dough at the development stage was used as the measurement object. The gluten of the sample and the bread dough of the measurement object were returned to room temperature after being frozen and stored in the measurement cell after being completely thawed, and measured by the excitation / fluorescence matrix measurement unit 24-1.

  To match the gluten and primary starch of the sample extracted from the bread dough of the measurement object to the gluten and starch in the bread dough of the measurement object, the sample primary starch contains the same solvent as the starch in the bread dough of the measurement object. It is necessary to adjust the rate (moisture content). In this process 3, it adjusted to two types of solvent content rate (water content). Two types of water content and the reasons for determining the water content are described below.

  According to Baker et al., Starch granules in the bread dough to be measured exist as hydrates, and the amount of moisture absorbed by the starch granules is the amount of moisture absorbed when the starch is left under saturated vapor pressure. be equivalent to. Therefore, in this step 3, as the first primary starch sample, the freeze-dried primary starch is thawed and placed in an evaporation plate and placed in a sealed container together with a saline solution having the same concentration as gluten (2.0%). The sample was left for 48 hours and measured by the excitation / fluorescence matrix measurement unit 24-1. Before and after leaving, the weight increased by 1.91% based on the weight before leaving. The condition was slightly wet but powdery.

  In the second primary starch sample, thawed primary starch and solvent water were mixed at a ratio of 10: 7. This is equal to the ratio of flour and water (solvent content) when making the bread dough of the measurement object. That is, it is assumed that the water content of the solvent added to the wheat flour is uniformly distributed in both starch and gluten. Hereinafter, the first primary starch will be described as “PS (powder)”, and the second primary starch will be described as “PS (water)”.

(3-2-4. Measurement conditions)
The measurement conditions by the excitation / fluorescence matrix measurement unit 24-1 are summarized in FIG.

  As shown in FIG. 31, the fluorescence wavelength range is the maximum range that can be measured in a general liquid crystal tunable filter. In order to remove the influence of scattered light, the measurement fluorescence wavelength is set to the longer wavelength side by 50 nm or more than the excitation wavelength.

  In order to minimize the influence of noise, it is necessary to increase the measured fluorescence intensity. Therefore, the exposure time was set to the longest 10 seconds.

  Binning means that a certain number of pixels of the CCD camera are grouped together and the sensitivity is increased by increasing the light receiving area. Binning 2 × 2 means that a total of 4 pixels, 2 vertical pixels and 2 horizontal pixels, are combined. To do. Increasing binning makes it possible to capture weak fluorescence but lowers the resolution. EEM-IS binning can be specified from 1 × 1 to a maximum of 8 × 8. In Step 3, the binning was set to 2 × 2 in order to increase the resolution even if the fluorescence intensity was sacrificed to some extent.

  The magnification was 1 × for the imaging lens and 2.5 × for the objective lens.

  From the camera pixel count, binning, and microscope magnification, the size of one field of view was 1.89 mm × 1.44 mm, and the resolution was 2.81 μm / pixel. However, since the amount of data is large and the edge portion of the field of view is easily affected by uneven illumination, what is actually used for measurement by the excitation / fluorescence matrix measurement unit 24-1 is the vertical / The horizontal length is a range that is ¼ of the original image (see FIG. 32).

(3-2-5. Dark correction and flat correction)
Data measured by EEM-IS includes camera noise and device illumination unevenness. Therefore, dark correction for removing camera noise and flat correction for correcting illumination unevenness were performed by the excitation / fluorescence matrix measurement unit 24-1.

  The CCD camera has dark current noise generated by thermal noise or the like. The degree of dark current noise depends on the exposure time and temperature. In order to correct this, the apparatus was set to the same exposure time and binning as the EEM by the excitation / fluorescence matrix measurement unit 24-1, and a dark image was taken with the camera shutter closed. An average of 16 dark images obtained by the excitation / fluorescence matrix measurement unit 24-1 was used as a final dark image, and the dark image was subtracted from the sample image taken under each wavelength condition.

  In the above-described image photographing method shown in FIG. 30 used in step 3, there may be illumination unevenness in which excitation light is unevenly applied to the surface of a sample (for example, a measurement object and a sample). In order to correct this, the image of each measurement object and the sample was divided by the luminance value of the flat image obtained by photographing the reflected light of the standard white plate by the excitation / fluorescence matrix measurement unit 24-1. Specifically, the excitation / fluorescence matrix measurement unit 24-1 applies excitation light set every 10 nm in a range of 420 nm to 670 nm to a standard white plate, and four fluorescent images at the same wavelength are taken for each wavelength. . A flat image was obtained by integrating and averaging the images of a total of 104 images of 26 wavelength conditions × 4 images.

As shown in Equation 1 below, a corrected sample image at each wavelength was obtained by the excitation / fluorescence matrix measurement unit 24-1 and used as EEM spectrum data (corresponding to excitation / fluorescence matrix information).
Correction sample image = (sample image−dark image) / (flat image−dark image) (Equation 1)

(3-2-6. Adjustment of dimensions)
As in the case of performing principal component analysis on a simple EEM that does not include spatial information, the data analysis unit 24-2 performs principal component analysis on the EEM data acquired by the excitation / fluorescence matrix measurement unit 24-1. When doing so, it is necessary to adjust the dimensions. The EEM data has a four-dimensional shape as it is (excitation wavelength) × (fluorescence wavelength) × (number of pixels in the x direction of the sample) × (number of pixels in the y direction of the sample). This was developed into a two-dimensional form that can be analyzed by principal component analysis by the data analysis unit 24-2 as follows.

  First, the wavelength was treated as a wavelength condition by combining the excitation wavelength and the fluorescence wavelength as in the case of simple EEM. As for the position information, the data analysis unit 24-2 cuts the two-dimensional image with a width of one pixel as shown in FIG. 33. For example, next to the rightmost pixel in the first row is the leftmost pixel in the second row. Connected to one dimension. As a result, two-dimensional data of the total number of pixels × wavelength conditions is obtained. The principal component analysis is performed by the data analysis unit 24-2 by treating each wavelength condition as a variable with each pixel as one measurement object and sample. Was able to do.

(3-2-7. Principal component analysis)
In step 3, principal component analysis was performed by the data analysis unit 24-2 using two principal component eigenvectors. That is, the eigenvector calculated from the EEM data of the sample gluten and PS (water) and the eigenvector calculated from the EEM data of the bread dough of the measurement object.

  Since the bread dough to be measured contains gluten and starch, if the bread dough cross section is measured with sufficient resolution by the excitation / fluorescence matrix measuring unit 24-1, pixels and starch where gluten is observed are observed. There should be a pixel. That is, data similar to data obtained by separately measuring the gluten and primary starch of the sample by the excitation / fluorescence matrix measurement unit 24-1 should be obtained. Since the EEM data acquired by the excitation / fluorescence matrix measurement unit 24-1 cannot be directly compared, the results obtained from the principal component analysis by the data analysis unit 24-2 are compared.

  Therefore, the data analysis unit 24-2 obtains eigenvectors from the gluten and PS (water) of the sample and the eigenvectors from the bread dough of the measurement object, and the data analysis unit 24-2 obtains each pixel in each measurement object and sample. The main component score of was determined.

  Representing the principal component scores of all the pixels in the principal component score plot has too many pixels. Therefore, when the principal component score plot is created by the data analysis unit 24-2, each measurement object is measured using random sampling. And 50 pixels were selected from the image of the sample cross section, and the points were representative of the entire image.

(3-2-8. Removal of moisture effects)
The principal component score obtained as a result of the principal component analysis by the data analysis unit 24-2 is affected by the difference in the solvent content (moisture content) between the measurement object and the sample (see 2-3-4 above). . On the other hand, since the water of the solvent used for extraction remains in the sample gluten extracted from the bread dough of the measurement object, the gluten in the bread dough of the measurement object and the solvent content (water content) are different. There is a possibility. In addition, the amount of water to be added to the primary starch of the sample in order to achieve the same solvent content (moisture content) as starch in the bread dough of the measurement object was examined (see 2-2-3 above). The solvent content (water content) of is unknown. Therefore, in order to handle the gluten and starch in the bread dough of the measurement object and the gluten and primary starch of the sample extracted from the bread dough of the measurement object in the same row, it is necessary to remove the influence of the moisture of the solvent. In step 3, the data analysis unit 24-2 performed a process for removing the influence of the moisture of the solvent on the main component score by the method described below.

  The processing of the data analysis unit 24-2 will be described using the principal component score plots of the first principal component (x axis) and the second principal component (y axis) shown in FIG. As shown in the upper diagram of FIG. 34, the difference between PS (powder) and PS (hydrous) samples is the solvent content (water content). Therefore, the data analysis unit 24-2 obtains the center of gravity of the distribution in the principal component score plot for each of PS (powder) and PS (hydrous) of the sample, and draws a straight line passing through the two points. It can be called the axis representing the rate (water content) (hereinafter “solvent axis”).

  Next, a new axis was taken in the direction orthogonal to the “solvent axis” by the data analysis unit 24-2. Being orthogonal to the “solvent axis” means that it is not affected by the amount of solvent (water content). This axis is called “component axis” for convenience. As shown in the lower diagram of FIG. 34, when considering a new orthogonal coordinate system in which the x-axis is the “component axis” and the y-axis is the “solvent axis”, the y-coordinate represents the solvent content (water content) of the measurement object and the sample. ), And the x coordinate represents the difference between the measurement object other than the water content of the solvent and the sample.

  FIG. 35 shows centroid coordinates (xG1, yG1) and (xG2, yG2) as an example of coordinates on the “component axis”.

(3-2-9. Imaging of principal component scores)
As shown in FIG. 36, the component distribution visualization unit 24-3 applies the color axis of blue to yellow to red to the “component axis”, and converts the coordinates on the “component axis” to colors.

  A specific calculation method is shown.

  With respect to the coordinates W on the “component axis” obtained by the data analysis unit 24-2 in 3-2-8, the component distribution visualization unit 24-3 sets the average value of all pixels to μ and the standard deviation to σ. . As shown in FIG. 36, the component distribution visualization unit 24-3 associates a value in a range from (μ−σ) to (μ + σ) with a color axis in which blue to yellow to red colors are continuously arranged. It was. The point where the coordinate W was (μ−σ) was dark blue, the point where (μ + σ) was dark red, and the point between them was a color corresponding to the position. When the coordinate W is less than (μ−σ), the color is dark blue, and when the coordinate W is greater than (μ + σ), the color is dark red.

  Through the processing of the component distribution visualization unit 24-3, the color is determined by the main component score of each pixel, and an image reflecting the component distribution is obtained.

(3-3. Results and discussion of step 3)
(3-3-1. Sample cross-sectional image by single excitation fluorescence wavelength)
37 and 38 show cross-sectional images of the respective samples visualized by the component distribution visualization unit 24-3 with single excitation fluorescence wavelengths of excitation wavelength / fluorescence wavelength: 380/500 nm and 430/710 nm. Since photographing is performed with a single wavelength, the fluorescence intensity at the excitation fluorescence wavelength appears as light and dark. The wavelength 48 condition selected here is a wavelength band in which the absolute value of the principal component analysis eigenvector is large (see 3-3-2 and 3-3-3 described later).

  As shown in FIG. 37, at the excitation wavelength / fluorescence wavelength: 350/500 nm, the gluten of the sample appears darkest and the primary starch (powder) of the sample is brightest. The brightness of the bread dough as the measurement object is intermediate between the gluten and the primary starch (powder) of the sample. The brightness of the primary starch (water) of the sample is close to the bread dough of the measurement object. The brightness of the cross-section of the bread dough of the measurement object is almost uniform, and the gluten distribution of the sample cannot be observed by measurement with a single wavelength.

  As shown in FIG. 38, the brightness of the cross-sectional images at excitation wavelength / fluorescence wavelength: 430/710 nm is also different between the measurement object and the sample. Even in this wavelength band, the primary starch (powder) image of the sample is brighter than the others. However, the difference between the measurement object and the sample is small compared to the excitation wavelength / fluorescence wavelength: 350/500 nm.

(3-3-2. Principal component analysis by eigenvectors of gluten and primary starch (water))
It shows from the principal component analysis result by the data analysis part 24-2 using the EEM data of the sample gluten and PS (hydrolysis) acquired by the excitation and the fluorescence matrix measurement part 24-1. First, FIG. 39 shows the contribution ratios of the first to sixth principal components. As can be seen from FIG. 39, the contribution ratio of the first principal component is large.

  The principal component load amounts of the first principal component to the third principal component obtained by the data analysis unit 24-2 are shown in FIGS. 40 to 42, the principal component load amount is displayed in the form of EEM, and the contribution ratio of each wavelength condition to the principal component is represented by a color. In step 2, the data analysis unit 24-2 obtained the principal component loading amount reflecting the EEM pattern of the measurement object and the sample, but no peaks were observed in the measurement data by EEM-IS.

  As shown in FIG. 39, since 95% or more of the data is explained by the first principal component, it can be said that the combination of the excitation wavelength and the fluorescence wavelength that greatly contributes to the first principal component is a wavelength condition that determines the result. As shown in FIG. 40, in the main component load amount of the first main component obtained by the data analysis unit 24-2, the correlation is high in all the fluorescence wavelengths in the range of the excitation wavelength around 400 nm. As a cause of this, it is conceivable that the spectral illumination of EEM-IS has a device characteristic of irradiating excitation light with high intensity at around 400 nm. If the excitation light intensity is high, the fluorescence intensity naturally increases, so it is considered that a high correlation is seen regardless of the fluorescence wavelength. In order to obtain a fluorescence peak of a component as measured by F7000, it is necessary to correct the characteristics of the spectroscopic device by introducing an apparatus function or the like.

  As shown in FIGS. 41 and 42, the principal component load amounts of the second and third principal components obtained by the data analysis unit 24-2 have a low correlation with the principal component except for a certain one wavelength condition. What has a high negative correlation with the main component is a combination of excitation wavelength / fluorescence wavelength: 430 nm / 710 nm. That is, the main component scores of the second main component and the third main component are determined by the fluorescence intensity under this wavelength condition.

  FIG. 43 shows a principal component score plot of the first principal component and the second principal component created by the data analysis unit 24-2. As described above, 50 pixels were selected from each measurement object and sample image acquired by the excitation / fluorescence matrix measurement unit 24-1, and the principal component score created by the data analysis unit 24-2 was shown. As shown in FIG. 43, in the principal component score plot of the first principal component and the second principal component, the PS (powder) of the sample is distributed far away from the other samples. The other three types of samples have a close distribution range, but the gluten and PS (water) distributions of the samples do not overlap each other. The distribution of the bread dough of the measurement object is located in the middle of the gluten and PS (water) of the sample.

As shown in FIG. 44, the influence of the solvent content (water content) was removed by the processing of the data analysis unit 24-2 in 3-2-8 and 3-2-9 described above. First, when the data analysis unit 24-2 obtains the center of gravity of PS (powder) and PS (hydrous) of the sample, the principal component score average of the first main component of the sample PS (powder) is 2.04. The average principal component score of the second main component was -0.0691. Similarly, the first principal component score average of PS (water) of the sample obtained by the data analysis unit 24-2 was 1.03, and the principal component score average of the second principal component was −0.0325. The average value is obtained not only from the points on the principal component score plot but also from the principal component scores of all pixels. From the average of the main component scores of PS (powdered) and PS (hydrous) obtained by the data analysis unit 24-2, the inclination of the solvent axis is as shown in Equation 2 below. In Equation 2, the x-axis is the first principal component axis, and the y-axis is the second principal component axis.
0.0366x + 1.01y (Formula 2)

Accordingly, the coordinates on the “component axis” of the pixel having the principal component score of the first principal component as X and the principal component score of the second principal component as Y are expressed by Equation 3 below. Here, the intersection of the “solvent axis” and the “component axis” is 0.
0.0366X + 1.01Y (Equation 3)

  The result of imaging the principal component score by the component distribution visualization unit 24-3 is shown in FIG. As shown in FIG. 45, the gluten and PS of the sample are displayed in blue and red, which are the colors at both ends of the color axis, respectively, and the bread dough of the measurement object is mottled from blue to red. In the image of bread dough of the measurement object, it is considered that the component displayed in a color close to blue has a component close to gluten, and the portion displayed in a color close to red has a component close to starch.

  As shown in FIG. 45, light blue to green mottle is seen in the entire bread dough image of the measurement object, which is considered to be starch granules, but the outline is not clear. One possible cause is that the resolution was insufficient. When both starch and gluten are included in one pixel, the main component score obtained by the data analysis unit 24-2 is between gluten and starch, and the boundary between starch granules and gluten becomes ambiguous. In order to solve this, it is conceivable to reduce the binning or increase the magnification of the microscope.

  Another cause is that the excitation light is transmitted not only to the surface of the measurement object and the sample but also to a certain depth, and the fluorescence of the substance therebetween is observed by the excitation / fluorescence matrix measurement unit 24-1. Can be considered. Even when gluten is present on the surface of the object to be measured and the cross section of the sample, the observed fluorescence is intermediate between gluten and starch if there is a starch grain immediately below it and excitation light reaches the starch grain. In order to prevent this, it is conceivable that the sample is observed after being sliced or the intensity of the excitation light is not increased too much.

(3-3-3. Principal component analysis by eigenvectors of bread dough)
The principal component analysis result calculated | required by the data analysis part 24-2 from the EEM data of the bread dough of a measuring object acquired by the excitation and the fluorescence matrix measurement part 24-1 is shown.

  FIG. 46 shows the principal component contribution rates of the first to sixth principal components obtained by the data analysis unit 24-2. Although the contribution ratio of the first principal component is smaller than the contribution ratio of the eigenvector obtained from the EEM of the gluten and PC (water) of the sample obtained by the excitation / fluorescence matrix measurement section 24-1, it is obtained by the data analysis section 24-2. The contribution ratio after the second main component is large.

  47 to 49 show principal component load diagrams of the first to third principal components obtained by the data analysis unit 24-2. Similar to the principal component eigenvector obtained from the gluten and primary starch EEM of the sample obtained by the excitation / fluorescence matrix measurement unit 24-1 by the data analysis unit 24-2, the fluorescence intensity at the excitation wavelength around 400 nm is large, and the spectral illumination It is considered that the characteristics of the device 11 are reflected. In the eigenvector obtained by the data analysis unit 24-2 from the EEM of the sample gluten and the primary starch obtained by the excitation / fluorescence matrix measurement unit 24-1, the principal component load amounts of the second and third principal components are the first. Although it has a completely different distribution from the main component load amount of the main component, as shown in FIGS. 47 to 49, all have a characteristic around an excitation wavelength of 400 nm.

  FIG. 50 is a principal component score plot of the first principal component and the third principal component created by the data analysis unit 24-2. As shown in FIG. 50, since the calculation maximizes the dispersion of the pixel values in the bread dough image of the measurement object, the sample gluten and PS (water) obtained by the excitation / fluorescence matrix measurement unit 24-1 are used. Compared with the result of principal component analysis obtained by the data analysis unit 24-2 from the EEM data, the pixels in the bread dough of the measurement object are spread over a wide range. The point where the sample PC (powder) is distributed away from other samples and the point where the gluten and PC (water) distribution of the sample do not overlap are acquired by the excitation / fluorescence matrix measurement unit 24-1. This agrees with the result of the principal component analysis obtained by the data analysis unit 24-2 from the gluten and PS (water) EEM data of the sample.

  Here, FIG. 51 shows a principal component score plot of the first principal component and the third principal component created by the data analysis unit 24-2, as shown in FIG. 44, the data analysis unit 24-2. This is because the distribution of gluten and PS (water) in the sample overlap in the principal component score plot of the first principal component and the second principal component created by the above. In the principal component analysis in the data analysis unit 24-2, it is necessary to combine the principal components so that the target component can be detected, instead of simply selecting the principal component having a large contribution rate.

As shown in FIG. 51, from the eigenvectors of gluten and PS (water) of the sample acquired by the excitation / fluorescence matrix measurement unit 24-1, the water content which is the solvent content by the principal component analysis by the data analysis unit 24-2. The influence of was removed. The data analysis unit 24-2 determines the center of gravity of PS (powder) and PS (hydrous) of the sample, and the average principal component score of the first main component of the sample PS (powder) is 2.05. The main component score average of the two main components was -0.0216. Similarly, the first principal component score average of PS (hydro) of the sample was 1.03, and the principal component score average of the second principal component was −0.0076. From the average of the main component scores of PS (powdered) and PS (hydrous) of the sample, the inclination of the solvent axis is as shown in Equation 4 below. In Equation 7, the x-axis is the first principal component axis and the y-axis is the second principal component axis.
0.014x + 1.02y (Equation 4)

Accordingly, the coordinates obtained on the “solvent axis” of the pixel whose principal component score of the first principal component is X and whose principal component score of the second principal component is Y obtained by the data analysis unit 24-2 are expressed by the following formula. It becomes like 5. As in the above 3-3-2, the intersection of the “solvent axis” and the “component axis” is 0.
0.014X + 1.02Y (Formula 5)

  FIG. 52 shows images of four samples obtained by the component distribution visualization unit 24-3 from the principal component eigenvectors of the bread dough of the measurement object obtained by the data analysis unit 24-2. An image visualized by the component distribution visualization unit 24-3 using the principal component eigenvector calculated by the data analysis unit 24-2 from the EEM of the sample gluten and primary starch obtained by the excitation / fluorescence matrix measurement unit 24-1. In contrast, the contrast for each pixel in the bread dough of the measurement object is clear.

  As shown in FIG. 52, as in FIG. 45, the gluten image of the sample is generally blue, the image of the primary starch of the sample is generally red, and the blue portion in the bread dough is rich in gluten and red. The portion is thought to be rich in starch. The resolution of the image is still low, and it seems that it is necessary to devise in order to obtain an image in which the outline of the starch grain can be seen.

  As shown in FIG. 52, the eigenvector calculated by the data analysis unit 24-2 is visualized by the component distribution visualization unit 24-3 from the EEM of the sample gluten and the primary starch obtained by the excitation / fluorescence matrix measurement unit 24-1. The image obtained by visualizing the eigenvector calculated by the data analysis unit 24-2 from the EEM of the bread dough of the measurement object acquired by the excitation / fluorescence matrix measurement unit 24-1 and visualized by the component distribution visualization unit 24-3 Although there are differences in contrast, the general distribution of gluten and starch is similar. This is thought to be because the eigenvectors of the first principal component obtained by the data analysis unit 24-2 are similar. By using the property that the eigenvectors of the first principal component having a large contribution rate are similar, when the gluten in the bread dough of the measurement object is visualized by the component distribution visualization unit 24-3, it is extracted from the measurement object. Even if sample gluten and primary starch are not prepared, the same result may be obtained only by EEM data of the bread dough to be measured. This is important when considering simplification of measurement and analysis.

(3-4. Summary of Step 3)
As described above, in step 3, the data analyzer 24-2 uses the excitation / fluorescence matrix having both the optical information acquired by the excitation / fluorescence matrix measurement unit 24-1 and the positional information on the sample cross section. The gluten distribution in the bread dough cross section of the measurement object was examined one by one in the flat region of the bread dough cross section of the measurement object, and visualized by the component distribution visualization unit 24-3.

  In step 3, the EEM was measured by the excitation / fluorescence matrix measurement unit 24-1 using the EEM imaging system. The excitation / fluorescence matrix measurement unit for the excitation / fluorescence matrix under the excitation light is obtained by photographing the cross section of the sample while changing the wavelength of the fluorescence while keeping the wavelength of the excitation light passing through the spectrometer constant. It can be measured at 24-1. Next, the wavelength of the excitation light passing through the spectroscope is changed, and the excitation / fluorescence matrix is similarly measured by the excitation / fluorescence matrix measurement unit 24-1. Thus, the excitation / fluorescence matrix measurement unit 24-1 obtained the excitation / fluorescence matrix by photographing the measurement object and the sample with various combinations of excitation light / fluorescence.

  Further, in step 3, the eigenvector calculated by the data analysis unit 24-2 from the EEM data of the sample gluten and primary starch (water) obtained by the excitation / fluorescence matrix measurement unit 24-1, and the excitation / fluorescence matrix measurement The principal component score was determined from the eigenvector calculated by the data analysis unit 24-2 from the EEM data of the bread dough of the measurement object acquired by the unit 24-1.

  Further, in step 3, for the samples of two primary starches having different solvent contents, the data analysis unit 24-2 defines a straight line passing through the center of gravity of the distribution of each principal component score as a “solvent axis”. The “component axis” orthogonal to “” is used for image creation by the component distribution visualization unit 24-3, thereby removing the influence of the solvent. The component distribution visualization unit 24-3 determines the color of each pixel by associating the color axis with the “component axis” orthogonal to the “solvent axis” set by the data analysis unit 24-2, and the measurement object We succeeded in visualizing the distribution of gluten and starch in bread dough.

  In Step 3, the image obtained by the component distribution visualization unit 24-3 from two different eigenvectors obtained by the data analysis unit 24-2 has a similar distribution of gluten and starch, and the bread dough of the measurement object Only the data suggests the possibility of visualizing the component distribution in the bread dough of the measurement object.

  Further, in step 3, in order to increase the resolution of an image acquired by the excitation / fluorescence matrix measurement unit 24-1, it is necessary to increase the resolution of the camera and adjust the excitation light intensity.

  This is the end of the description of step 3.

[Other embodiments]
Although the embodiments of the present invention have been described so far, the present invention is not limited to the above-described embodiments, but can be applied to various different embodiments within the scope of the technical idea described in the claims. It may be implemented.

  In addition, among the processes described in the embodiment, all or part of the processes described as being automatically performed can be performed manually, or the processes described as being performed manually can be performed. All or a part can be automatically performed by a known method.

  In addition, unless otherwise specified, the processing procedures, control procedures, specific names, information including registration data for each processing, parameters such as search conditions, screen examples, and database configurations shown in the above documents and drawings Can be changed arbitrarily.

  In addition, regarding the component distribution analysis system, each illustrated component is functionally conceptual and does not necessarily need to be physically configured as illustrated.

  For example, the processing functions of each component of the component distribution analyzer 20, particularly the processing functions performed by the calculation processing unit 24, are all or any part of them by a CPU (Central Processing Unit) and the CPU It may be realized by a program to be interpreted and executed as hardware by wired logic. The program is recorded on a recording medium to be described later, and is mechanically read by the component distribution analyzer 20 as necessary. That is, a memory 21 such as a ROM or an HD stores a computer program for giving instructions to the CPU in cooperation with an OS (Operating System) and performing various processes. This computer program is executed by being loaded into the RAM, and constitutes a control unit in cooperation with the CPU.

  The computer program may be stored in an application program server connected to the component distribution analyzer 20 via an arbitrary network, and may be downloaded in whole or in part as necessary. It is.

  The program according to the present invention can also be stored in a computer-readable recording medium. Here, the “recording medium” refers to any “portable physical medium” such as a flexible disk, a magneto-optical disk, a ROM, an EPROM, an EEPROM, a CD-ROM, an MO, and a DVD, or a LAN, WAN, or Internet. It includes a “communication medium” that holds the program in a short period of time, such as a communication line or a carrier wave when the program is transmitted via a network represented by

  The “program” is a data processing method described in an arbitrary language or description method, and may be in any format such as source code or binary code. The “program” is not necessarily limited to a single configuration, but is distributed in the form of a plurality of modules and libraries, or in cooperation with a separate program represented by an OS (Operating System). Including those that achieve the function. Note that a well-known configuration and procedure can be used for a specific configuration for reading a recording medium, a reading procedure, an installation procedure after reading, and the like in each device described in the embodiment.

  Various databases and the like stored in the memory 21 are storage means such as a memory device such as a RAM and a ROM, a fixed disk device such as a hard disk, a flexible disk and an optical disk, and various programs and programs used for providing websites. Stores tables, databases, files for web pages, etc.

  The component distribution analyzer 20 is connected to an information processing apparatus such as a known personal computer or workstation, and software (including programs, data, etc.) for realizing the method of the present invention is installed in the information processing apparatus. May be realized.

  Furthermore, the specific form of distribution / integration of the devices is not limited to that shown in the figure, and all or a part of them may be functional or physical in arbitrary units according to various additions or according to functional loads. Can be distributed and integrated.

  As described above in detail, according to the present invention, a difference in component distribution between a measurement object and a sample extracted from the measurement object is analyzed based on an excitation / fluorescence matrix (EEM). The present invention also provides a component distribution analysis method and a component distribution analysis apparatus that can remove the influence of the solvent content between the measurement object and the sample and can analyze a specific component of the measurement object more clearly. Therefore, it can be suitably used for various applications such as analysis of the internal structure of food or the like.

DESCRIPTION OF SYMBOLS 10 Fluorescence image imaging device 11 Spectral illumination device
110 Light source
112 Spectrometer
114 Excitation wavelength adjustment unit
116 optical fiber
118 Excitation light irradiation unit 12 Spectroscopic imaging device
120 Optics
122 Spectrometer
124 Image Capture Device 13 Measurement Object / Sample 20 Component Distribution Analyzer 21 Memory 22 Keyboard / Mouse 23 Control Unit 24 Calculation Processing Unit
24-1 Excitation / Fluorescence Matrix Measurement Unit
24-2 Data Analysis Department
24-3 Component Distribution Visualization Unit 30 Display

Claims (8)

A component distribution analysis method for analyzing a component distribution of a measurement object and a sample extracted from the measurement object,
Measuring fluorescence intensity at a plurality of measurement locations on the measurement object and the sample while changing the excitation wavelength to be irradiated and the fluorescence wavelength to be observed in a stepwise manner within a predetermined excitation wavelength range and a predetermined fluorescence wavelength range. Excitation / fluorescence matrix information acquisition step of acquiring excitation / fluorescence matrix information of the measurement object and each measurement location on the sample, and
Performs principal component analysis on the excitation / fluorescence matrix information acquired for each measurement location in the excitation / fluorescence matrix information acquisition step, and values of the measurement object and the components of the sample represented by principal components A principal component score plot is created by plotting the principal component score as a coordinate, and on the principal component score plot, a plot group showing components of the solvent axis determination samples having different solvent contents contained in the sample Among them, an axis passing through the centroid of the first plot group having a high solvent content and the centroid of the second plot group having a low solvent content, or an approximate straight line of the plot group of the solvent axis determination sample, Set as the solvent axis, set the axis orthogonal to the solvent axis as the component axis not affected by the solvent content, and indicate the components of the measurement object and the sample. By projecting plotted on the component axis, the data analysis step of parameterizing the difference of the components of the respective measuring points,
A component distribution analysis method comprising: A color determination step for determining a color to be colored at each measurement location based on the plot projected on the component axis in the data analysis step; and
A component distribution visualization step of visualizing the component distribution of the measurement object and the sample by color-mapping the color to be colored at each measurement location determined in the color determination step;
The component distribution analysis method according to claim 1, further comprising: In the excitation / fluorescence matrix information acquisition step,
For the measurement object and the sample, take a plurality of fluorescence images with different excitation wavelengths and fluorescence wavelengths to be observed,
3. The component distribution according to claim 1, wherein the excitation / fluorescence matrix information at each measurement location on the measurement object and the sample is acquired from the fluorescence intensity of each pixel in the fluorescence image. Analysis method.   The component distribution analysis method according to claim 1, wherein the measurement object includes at least a flour product.   5. The component distribution analysis method according to claim 1, wherein the component includes at least one selected from the group consisting of protein, starch, polysaccharides, minerals, and lipids.   The component distribution analysis method according to claim 1, wherein the solvent includes at least one selected from the group consisting of water and alcohol. A spectral illumination device that irradiates a measurement object and a sample extracted from the measurement object with a predetermined excitation wavelength; and a spectral imaging device that observes the measurement object and the sample with a predetermined fluorescence wavelength, A component distribution analyzer for analyzing a component distribution of a measurement object and the sample, wherein an excitation / fluorescence matrix measurement unit acquires excitation / fluorescence matrix information at least at a plurality of measurement locations on the measurement object and the sample ,and,
The principal component analysis is performed on the excitation / fluorescence matrix information acquired for each measurement point by the excitation / fluorescence matrix measurement unit, and the values of the components of the measurement object and the sample represented by the principal components Create a principal component score plot by plotting the principal component score as coordinates, and on the principal component score plot, among the plot group indicating the components of the solvent axis determination sample having a different solvent content contained in the sample, An axis passing through the centroid of the first plot group having a high solvent content and the centroid of the second plot group having a low solvent content, or an approximate straight line of the plot group of the solvent axis determination sample is represented by a solvent axis. The axis perpendicular to the solvent axis is set as a component axis that is not affected by the solvent content, and the measurement object and the sample components are shown. By projecting the Tsu bets on the component axis, the data analysis unit for parameterizing the difference of the components of the respective measuring points,
A component distribution analysis apparatus comprising: Based on the plot projected on the component axis by the data analysis unit, the color to be colored at each measurement location is determined, and the color to be colored at each measurement location is color-mapped, whereby the measurement object And a component distribution visualization unit that visualizes the component distribution of the sample,
The component distribution analyzer according to claim 7, further comprising:

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