JP2020076612A - Component extraction method, fluorescence fingerprint measuring device, and program executable by computer - Google Patents

Abstract

To provide a component extraction method capable of rapidly and accurately detecting a marker signal of a fluorescence fingerprint, a fluorescence fingerprint measuring device, and a program executable by a computer.SOLUTION: A component extraction method includes: a sample extraction process S11 of extracting a plurality of samples having different components, by continuously changing the extraction condition for one sample; and a fluorescence fingerprint continuous body information acquiring process S12 that measures the fluorescence intensity while gradually changing the radiated excitation wavelength and observed fluorescence wavelength for each of the plurality of extracted samples, and acquires a plurality of pieces of fluorescence fingerprint information as fluorescence fingerprint continuous body information.SELECTED DRAWING: Figure 5

Description

  The present invention relates to a component extraction method, a fluorescence fingerprint measuring device, and a computer-executable program.

  In order to promote Japan's agriculture and raise farmers' income, it is required to further increase the added value of agricultural, forestry and marine products, and to search for the quality of agricultural, forestry and marine products, which is typified by functionality and can involve many ingredients, quickly and at low cost. , The need for evaluation methods is increasing.

  In the past, in order to evaluate the quality of many components, including functionality, it has been common to use target analysis in which the major contributing components are identified by purification, analysis, bioassay, etc., and then quantified. It was However, in recent years, efforts have been made to accurately and quantitatively evaluate the quality involving multiple components by using a non-target analysis method that comprehensively analyzes the contained components without limiting the target components.

  Near-infrared spectroscopy, which has been put into practical use as a quick and simple method for evaluating the sugar content of fruits, is also a type of non-target analysis that utilizes the spectroscopic properties of the components contained in the sample. Among the spectroscopic evaluation methods, the fluorescence fingerprint method (see, for example, Patent Document 1, Patent Document 2, and Non-Patent Document 1) analyzes various fluorescent signals emitted from each component contained in a sample, and Since it is possible to obtain more component information than the optical method, it is expected as a method that can accurately evaluate the quality in which multiple components are involved, and even for samples with properties where direct acquisition of fluorescent fingerprints is difficult, crushing and tableting (for example, , Patent Document 3) and solvent extraction (see, for example, Non-Patent Document 2) have been developed, and the range of applications is expanding.

  When assessing the quality of samples containing various components such as agriculture, forestry and marine products, the conventional target analysis method requires a large cost to identify the major contributors, and the evaluation target is limited to the major components. It is difficult to accurately and quantitatively evaluate the quality associated with the components of.

In the non-target analysis method, generally, a large cost is required such as complicated operation and operation of an expensive analyzer. Among the non-target analysis methods, there is a relatively low cost fluorescent fingerprint method. However, in the conventional fluorescence fingerprinting method, it is difficult to absorb fluorescence by coexisting components, weaken the signal due to concentration quenching phenomenon in which the emission efficiency decreases when the optimum concentration peculiar to the component is exceeded, or separate signals with similar wavelengths. Therefore, there is a problem that the number of signals contributing to analysis decreases. Pretreatment methods for samples that enhance the detection power for specific signals have also been developed, but it is necessary to optimize the pretreatment conditions for each sample, and there is no effect of separating signals of similar wavelengths.

  That is, in the conventional fluorescent fingerprint method, since the fluorescent fingerprint is obtained as it is from the sample or from the extract with a single solvent, the above-mentioned problem is likely to occur.

Japanese Patent No. 3706914 Japanese Patent No. 5856741 JP, 2017-36991, A

Dheni Mita Mala, Masatoshi Yoshimura, Susumu Kawasaki, Mizuki Tsuta, Mito Kokawa, Vipavee Trivittayasil, Junichi Sugiyama, Yutaka Ktamura: Fiber optics fluorescence fingerprint measurement for aerobic plate count prediction on sliced beef surface, Food Science and Technology, DOI10.1016 / j ． lwt. 2015.11.065 (2015) Mizuki Tsuta, Reiko Aiyama, Hiroyuki Onda, Kumiko Suzuki, Taketo Sagawa, Determination of black pepper extract origin by fluorescent fingerprint and multivariate analysis, 2017 Japan Society for Food Science and Technology

  The present invention has been made in view of the above, and provides a component extraction method, a fluorescent fingerprint measuring device, and a computer-executable program capable of detecting a marker signal of a fluorescent fingerprint quickly and with high accuracy. The purpose is to

  In order to solve the above-mentioned problems and achieve the object, the present invention provides a sample extraction step of continuously transitioning extraction conditions for one sample to extract a plurality of samples having different components, and the extraction. Fluorescence fingerprint sequence that obtains multiple fluorescence fingerprint information as fluorescence fingerprint continuum information by measuring the fluorescence intensity while changing the excitation wavelength to be irradiated and the fluorescence wavelength to be observed stepwise for each of the multiple samples. And a body information acquisition step.

  According to a preferred aspect of the present invention, further, an actual measurement value acquisition step of acquiring actual measurement values by measurement for the plurality of extracted samples, an explanatory variable of the fluorescent fingerprint continuum information, and the actual measurement value Performing multivariate analysis as a variable, based on the result of the multivariate analysis, to estimate the index value based on the fluorescent fingerprint, and to include a marker signal detection step of detecting a marker signal that is a wavelength region important for estimation, May be.

  Moreover, according to a preferable aspect of the present invention, the one sample may include medicinal plants, functional foods, functional fruits and vegetables, ordinary foods, wood, and water to be evaluated for water quality.

  Moreover, according to a preferable aspect of the present invention, in the sample extraction step, the plurality of samples may be extracted by an extraction method based on polarity, temperature, or molecular weight.

  Further, according to a preferred aspect of the present invention, the measurement may include various physiological activity evaluations and chemical analysis.

  Further, according to a preferred aspect of the present invention, the multivariate analysis includes principal component regression, cluster analysis, discriminant analysis, SIMCA, multiple regression analysis, PLS regression analysis, PLS discrimination, SVM regression, SVM discrimination, RF regression, and One or a plurality of RF discriminations may be set.

  Further, according to a preferred aspect of the present invention, in the marker signal detection step, regression coefficient, factor loading, loading, selectivity ratio, variable imbalance in projection, variable importance, and out of bag error obtained by multivariate analysis. The marker signal may be detected based on one or more indexes indicating the contribution rate to regression / discrimination.

  Further, according to a preferred aspect of the present invention, a data pre-processing step of performing centering, standardization, normalization, differentiation, baseline correction, and / or smoothing processing on the fluorescent fingerprint continuum information is performed. Further, it may be included.

  Further, in order to solve the above-mentioned problems and achieve the object, the present invention, for one sample, for a plurality of samples with different components obtained by continuously transitioning the extraction conditions, respectively, It is equipped with a fluorescent fingerprint continuum information acquisition means for measuring the fluorescence intensity while changing the excitation wavelength to be irradiated and the observed fluorescent wavelength stepwise, and acquiring a plurality of fluorescent fingerprint information as fluorescent fingerprint continuum information. And

  Further, according to a preferred embodiment of the present invention, further, the fluorescent fingerprint continuum information is an explanatory variable, multivariate analysis is performed with an actual measurement value of the plurality of samples as an objective variable, and based on the result of the multivariate analysis. Alternatively, a marker signal detecting means for estimating an index value based on the fluorescent fingerprint and detecting a marker signal in a wavelength region important for the estimation may be provided.

  Moreover, according to a preferable aspect of the present invention, the one sample may include medicinal plants, functional foods, functional fruits and vegetables, ordinary foods, wood, and water to be evaluated for water quality.

  Further, according to a preferred aspect of the present invention, the measurement may include various physiological activity evaluations and chemical analysis.

  Further, according to a preferred aspect of the present invention, the multivariate analysis includes principal component regression, cluster analysis, discriminant analysis, SIMCA, multiple regression analysis, PLS regression analysis, PLS discrimination, SVM regression, SVM discrimination, RF regression, and One or a plurality of RF discriminations may be set.

  Further, according to a preferred aspect of the present invention, the marker signal detecting means is a regression coefficient obtained by multivariate analysis, a factor loading, a selectivity ratio, a variable importance in projection, a variable importance, an out of bag error. The marker signal may be detected based on one or more indexes indicating the contribution rate to regression / discrimination.

  Further, according to a preferred aspect of the present invention, a data pre-processing step of performing centering, standardization, normalization, differentiation, baseline correction, and / or smoothing processing on the fluorescent fingerprint continuum information is performed. Further, it may be included.

  Further, in order to solve the above-mentioned problems and achieve the object, the present invention, for one sample, for a plurality of samples with different components obtained by continuously transitioning the extraction conditions, respectively, To cause the computer to perform the fluorescent fingerprint continuum information acquisition process of measuring the fluorescence intensity while gradually changing the excitation wavelength to be irradiated and the observed fluorescent wavelength and acquiring a plurality of fluorescent fingerprint continuum information as the fluorescent fingerprint continuum information. Is a computer executable program.

  According to a preferred aspect of the present invention, further, an actual measurement value acquisition step of acquiring actual measurement values by measurement for the plurality of extracted samples, an explanatory variable of the fluorescent fingerprint continuum information, and the actual measurement value Perform a multivariate analysis as a variable, and based on the result of the multivariate analysis, estimate a marker value based on the fluorescent fingerprint, and perform a marker signal detection step of detecting a marker signal that is a wavelength region important for estimation, on a computer. You may decide to let it.

  Moreover, according to a preferable aspect of the present invention, the one sample may include medicinal plants, functional foods, functional fruits and vegetables, ordinary foods, wood, and water to be evaluated for water quality.

  Further, according to a preferred aspect of the present invention, the measurement may include various physiological activity evaluations and chemical analysis.

  Further, according to a preferred aspect of the present invention, the multivariate analysis includes principal component regression, cluster analysis, discriminant analysis, SIMCA, multiple regression analysis, PLS regression analysis, PLS discrimination, SVM regression, SVM discrimination, RF regression, and One or a plurality of RF discriminations may be set.

  Further, according to a preferred aspect of the present invention, in the marker signal detection step, regression coefficient, factor loading, loading, selectivity ratio, variable imbalance in projection, variable importance, and out of bag error obtained by multivariate analysis. The marker signal may be detected based on one or more indexes indicating the contribution rate to regression / discrimination.

  Further, according to a preferred aspect of the present invention, a data pre-processing step of performing centering, standardization, normalization, differentiation, baseline correction, and / or smoothing processing on the fluorescent fingerprint continuum information is performed. Further, it may be executed by a computer.

  According to the present invention, it is possible to detect a marker signal of a fluorescent fingerprint quickly and with high accuracy.

FIG. 1 is an explanatory diagram for explaining the outline of the present invention. FIG. 2 is a diagram for explaining fluorescence emitted from the measurement target when the measurement target is irradiated with excitation light. FIG. 3 is a diagram showing an example of a fluorescent fingerprint in a contour line graph of three-dimensional data. FIG. 4 is a plan view showing an example of the fluorescent fingerprint of FIG. 3 in plan view. FIG. 5 is a flowchart for explaining the fluorescent fingerprint measuring method according to the present embodiment. FIG. 6 is a diagram for explaining the first step of the data preprocessing performed on the fluorescent fingerprint information. FIG. 7 is a diagram for explaining the second stage (centralization) of the data preprocessing performed on the fluorescent fingerprint information. FIG. 8 is a diagram for explaining the second stage (normalization) of the data preprocessing performed on the fluorescent fingerprint information. FIG. 9 is a diagram for explaining the second stage (standardization) of the data preprocessing performed on the fluorescent fingerprint information. FIG. 10 is a block diagram showing an example of the configuration of the fluorescent fingerprint measuring apparatus according to this embodiment. FIG. 11 is a block diagram showing an example of a fluorescent fingerprint acquisition device. FIG. 12 is a diagram for explaining one embodiment of the fluorescent fingerprint measuring method of the present invention. FIG. 13 is a diagram for explaining one embodiment of the fluorescent fingerprint measuring method of the present invention. FIG. 14 is a diagram for explaining one embodiment of the fluorescent fingerprint measuring method of the present invention. FIG. 15 is a diagram for explaining an embodiment of the fluorescent fingerprint measuring method of the present invention. FIG. 16 is a diagram for explaining an embodiment of the fluorescent fingerprint measuring method of the present invention. FIG. 17 is a diagram for explaining one embodiment of the fluorescent fingerprint measuring method of the present invention. FIG. 18 is a diagram for explaining one embodiment of the fluorescent fingerprint measuring method of the present invention. FIG. 19 is a diagram for explaining one embodiment of the fluorescent fingerprint measuring method of the present invention. FIG. 20 is a diagram for explaining one embodiment of the fluorescent fingerprint measuring method of the present invention.

  Embodiments of a component extracting method, a fluorescent fingerprint measuring apparatus, and a computer-executable program according to the present invention will be described in detail below with reference to FIGS. 1 to 20. The present invention is not limited to this embodiment.

[1. Outline of the present invention]
The present invention does not separate the contained components as independent single peaks at the time of sample extraction, but acquires them as a series of multiple fractions under the condition that the sample components are continuously distributed (“exhaustive extraction method”). Also, the fluorescent fingerprint information of each fraction is integrated in the order of acquisition to reconstruct a “fluorescent fingerprint continuum”, and the fluorescent fingerprint continuum is subjected to multivariate analysis to obtain a fluorescent fingerprint marker. It detects a signal.

  That is, the present invention is a new method of acquiring and analyzing a fluorescent fingerprint, which is conventionally composed of three-dimensional data of excitation wavelength, fluorescence wavelength, and fluorescence intensity, as a "fluorescent fingerprint continuum" developed along the dimension of the fraction acquisition order. Is proposed.

  In addition, the present invention significantly mitigates the above problems by using extraction conditions such that each of the contained components is divided into a plurality of fractions at different concentrations, and a larger amount of fluorescent signal than the conventional fluorescent fingerprint method is used. Can be obtained, facilitating the search for marker signals that characterize quality.

  Further, in the conventional fluorescence fingerprint method, dozens of samples were required to establish a marker signal search / estimation model, but in the present invention, by comparing quality and fluorescence fingerprint between the obtained fractions, It is possible to obtain the key signal from one specimen.

  Further, since the present invention is premised on the continuous transition of the component extraction conditions, it is not necessary to consider the optimization of the pretreatment conditions in the conventional fluorescence fingerprint method. As the extraction conditions, solid-liquid continuous extraction or the like mainly using a combination of solvents having greatly different polarities is used, but it is also applicable to a reverse phase / normal phase, a size exclusion column and the like.

  INDUSTRIAL APPLICABILITY The present invention is suitable, for example, for rapid and simple primary quality control of functional foods, medicinal plants, and functional fruits and vegetables for which quality evaluation involving many components is required. By combining the present invention used for primary screening with the conventional method / official method used for definite control inspection, it becomes possible to guarantee quality at low cost.

  As described below, as one example, multiple fractions were obtained from the leaves of the Plectranthus ambonicus Lour (PA), which is a traditional medicine used as a lactation promoter in Indonesia, and each fraction was collected. Regarding, the fluorescent fingerprint and the antigen-specific IFN-γ production inhibitory activity from mouse primary immune cells were measured and OPLS analysis was performed to detect a marker signal on the fluorescent fingerprint that has a strong correlation with the IFN-γ production inhibitory activity. , Proved the usefulness of the present invention.

  The outline of the present invention will be described with reference to FIG. FIG. 1 is an explanatory diagram for explaining the outline of the present invention.

  According to the present invention, first, a plurality of samples having different components are extracted by continuously shifting the extraction condition for one sample (S1). Fluorescent fingerprint measurement is performed on the extracted multiple samples, and the fluorescent intensity is measured while gradually changing the excitation wavelength to be irradiated and the fluorescent wavelength to be observed. It is acquired as information (S2). On the other hand, measurement (physiological activity evaluation, chemical analysis, etc.) is performed on a plurality of samples to obtain actual measurement values (S3). Multivariate analysis is performed using the fluorescent fingerprint continuum information as the explanatory variable and the measured value as the objective variable (S4), and based on the result of the multivariate analysis, the index value based on the fluorescent fingerprint is estimated, and in the wavelength region important for the estimation. A certain marker signal is detected (S5). The components of the detected marker signal are estimated (identified) using a database (literature information) or the like (S6).

[Fluorescent fingerprint]
The fluorescent fingerprint will be described with reference to FIGS. 2 to 4. FIG. 2 is a diagram for explaining fluorescence emitted from the measurement target when the measurement target is irradiated with excitation light. FIG. 3 is a diagram showing an example of a fluorescent fingerprint in a contour line graph of three-dimensional data. FIG. 4 is a plan view showing an example of the fluorescent fingerprint of FIG. 3 in plan view.

  As shown in FIG. 2, excitation light (light of various wavelengths) is applied to the measurement target, and the fluorescence emitted from the measurement target is detected. As shown in FIG. 3, the “fluorescence fingerprint” is a contour line graph of three-dimensional data consisting of three axes of excitation wavelength irradiating the measurement target, fluorescence wavelength emitted from the measurement target, and fluorescence intensity of the measurement target. Is.

  In addition, as shown in FIG. 4, the “fluorescent fingerprint” can be expressed two-dimensionally by plotting the fluorescence intensity at each point with a fluorescence wavelength on the horizontal axis and an excitation wavelength on the vertical axis.

  Fluorescent fingerprints can be characterized without pretreatment such as fluorescent staining on the object to be measured, are easy to operate and can be measured in a short time, have higher sensitivity than the absorption method, and are nondestructive. This method is used for analysis of the internal structure of foods, measurement of suspended solids in the atmosphere, and identification of raw materials for dyes, because it has the advantage that measurement is possible.

  As described above, since the “fluorescent fingerprint” is fluorescent information unique to a component that has enormous three-dimensional information, the measurer can identify the component by using the fluorescent fingerprint, and It is possible to measure by destruction.

  In the present embodiment, for a fluorescent fingerprint consisting of three-dimensional data of excitation wavelength, fluorescence wavelength, and fluorescence intensity, “fluorescent fingerprint continuum information which is developed along a dimension called a fraction acquisition order for a plurality of extracted samples. , And obtain and analyze.

[Multivariate analysis]
The multivariate analysis includes (1) unsupervised (pattern recognition) and (2) supervised. (1) Principal component analysis, cluster analysis, etc. are available without supervision. (2) With a teacher includes (2-1) discriminant analysis and (2-2) regression analysis.

(2-1) Discriminant analysis divides data into several groups, and PLS (Partial least squares) discriminant analysis, SIMCA (Soft Independent Modeling of Class Analog), and SVM (Support vector Analyzer) discriminant analysis. Etc.

(2-2) Regression analysis creates a regression equation (calibration curve) for estimating a specific numerical value. For regression analysis, multiple regression analysis, PLS regression analysis, principal component regression analysis, RF regression, and , SVM regression.

  RF regression and PLS regression are more desirable, and PLS regression is even more desirable. By adopting a desirable analysis method, it becomes possible to detect the marker signal more accurately.

  In addition, based on the regression coefficient obtained by multivariate analysis, factor loading, loading, selectivity ratio, variable impact in projection, variable importance, and index of one or more contributions to regression / discrimination of out of bag error. Alternatively, the marker signal may be detected.

[Fluorescent fingerprint measurement method (component extraction method)]
The fluorescent fingerprint measuring method (component extracting method) according to the present embodiment will be described with reference to FIGS. FIG. 5 is a flowchart for explaining the fluorescent fingerprint measuring method according to the present embodiment.

  In FIG. 5, first, a sample extraction process is executed (step S11). In the sample extraction step, one sample to be measured is prepared, and the extraction conditions are continuously changed for this one sample to extract a plurality of samples having different components (exhaustive extraction method).

  The sample may be, for example, not only medicinal plants, functional foods, and fruits and vegetables having functionality, but also various foods such as ordinary foods, wood, and water to be evaluated for water quality.

  For example, a sample extractor composed of a column and an extractor can be used to extract a plurality of samples having different components from one sample by an extraction method based on polarity, temperature, or molecular weight. Specifically, for example, a sample of a dry powder was packed in a stainless steel column together with sea sand (Wako Pure Chemical Industry Ltd., Osaka, Japan), and solvents having different polarities were mixed stepwise by an extractor. A plurality of fractions are obtained with a solvent at a predetermined flow rate and at a predetermined time interval. The extract solvent is volatilized with a centrifugal evaporator (eg, Eyela Centrifugal Vaporizer CVE-200D, Tokyo Rikakikai Co., Ltd., Japan) and the like to obtain a dry solid fraction. As described below, the dried solid is redissolved in a predetermined solvent (for example, DMSO) and subjected to fluorescence fingerprint measurement and actual measurement.

  Examples of the extractor include (1) gradient pump (for example, Jasco PU-980 Gulliver HPLC pump), (2) high-speed solvent extractor (for example, Thermo Scientific ASE-350 / ASE-50), (3). Batch processing using a combination of a centrifuge tube, a centrifuge, and an ultrasonic cleaner can be used.

  Instead of using solvents with different polarities, the temperature of the extraction solvent may be raised (decreased) stepwise to obtain a plurality of fractions. Further, it is also possible to obtain a plurality of fractions such as a continuously different molecular weight component using a size exclusion column and a component eluted when the salt concentration of the solvent is continuously changed.

  Next, the fluorescent fingerprint continuum information acquisition step is executed (S12). In the fluorescent fingerprint continuum information acquisition step, for example, with respect to a plurality of samples redissolved in a predetermined solvent, the fluorescence intensity is measured while changing the excitation wavelength to be irradiated and the fluorescence wavelength to be observed stepwise. , Obtaining a plurality of pieces of fluorescent fingerprint information as a continuous series of fluorescent fingerprints.

  Specifically, each of the plurality of re-dissolved samples is placed in a cell (for example, 1 mL cell (FM20-SQ-3, GL Sciences Inc., Tokyo, Japan)) and set in a measuring device.

  A spectrofluorometer can be used as the measuring device, and the excitation wavelength (for example, slit width = 10 nm, m wavelengths at each data acquisition interval of 10 nm within the measurement wavelength range of 200 to 900 nm), and fluorescence While changing the combination of the wavelength (for example, slit width = 10 nm, n wavelengths at each data acquisition interval of 10 nm in the measurement wavelength range of 200 to 900 nm), the measurement target is measured under m × n total wavelength conditions. The fluorescence intensity of may be acquired. The wavelength scan speed and the photomultiplier voltage may be set to 500 nm / s and 400 V, respectively.

  Here, the measurer may adjust the number of times of measurement (for example, three times for each sample). FIG. 6A is a diagram showing an example of the fluorescent fingerprint of the measurement object.

  As the fluorescence spectrophotometer, for example, (1) Hitachi High-Technologies F-7000 type fluorescence spectrophotometer, (2) JASCO FP-8500 fluorescence spectrophotometer, (3) independently developed device (for example, light source (xenon or LED (Lamp) + spectroscopic mechanism (diffraction grating / optical filter) + detector), (4) As a detector for HPLC, etc., use a light source and spectroscopic mechanism that can be used online, a device with a detector, etc. can do.

  As shown in FIG. 6A, the fluorescence intensity of the measurement target is acquired under m × n wavelength conditions in which the combination of the excitation wavelength to be irradiated and the fluorescence wavelength to be observed is different, and the fluorescence fingerprint information of the measurement target is acquired. ..

  Returning to FIG. 5, a data pre-processing step is executed on the acquired fluorescent fingerprint information, if necessary (step S14).

  Hereinafter, the data preprocessing performed on the fluorescent fingerprint information before the multivariate analysis will be described with reference to FIGS. 6 to 9.

  FIG. 6 is a diagram for explaining the first step of the data preprocessing performed on the fluorescent fingerprint information. 7 to 9 are diagrams for explaining the second stage of the data preprocessing performed on the fluorescent fingerprint information.

  The data preprocessing includes, for example, 1. First step of removing scattered light of fluorescent fingerprint and developing to two-dimensional data, signal processing operation for 2.2-dimensional fluorescent fingerprint information (eg, centering, standardization, standardization, second derivative, baseline correction) , And smoothing).

<1. Removal of scattered light from fluorescent fingerprints and development into two-dimensional data (first stage processing)>
As shown in FIG. 6A, the acquired fluorescence fingerprint information includes high-dimensional fluorescence intensity data consisting of parameters of a total of m × n wavelength conditions (= combination of excitation wavelength and fluorescence wavelength), Further, it includes optical data that deviates from the definition of fluorescence (for example, scattered light of excitation light, its secondary light, tertiary light, etc .: in the present invention, referred to as noise information for convenience).

  Here, the scattered light appears under a wavelength condition in which the fluorescence wavelength and the excitation wavelength match, or the secondary light and the tertiary light of the scattered light appear under a wavelength condition in which the fluorescence wavelength is twice or three times the excitation wavelength, respectively.

  Therefore, it is desirable to remove the noise information and obtain low-dimensional fluorescence intensity data from the acquired high-dimensional fluorescence intensity data by removing information other than the target fluorescence fingerprint.

  Therefore, as shown in FIG. 6B, the scattered light of the excitation light that becomes the noise information, and the data of the secondary light and the tertiary light thereof (for example, the data indicated by (i) in FIG. 6B). To delete.

  Here, since the scattered light, the secondary light, and the tertiary light appear with a certain width, for example, the data in the range of 30 to 40 nm before and after may be excluded.

  Further, the data in the range where the fluorescence wavelength is shorter than the excitation wavelength (for example, the data indicated by (ii) in FIG. 6B) is removed.

  This is a process for analyzing only the fluorescence intensity data of the fluorescence wavelength longer than the excitation wavelength, because the fluorescence wavelength emitted by the measurement object is longer than the excitation wavelength.

  In this way, the data in the range where the scattered light and the fluorescence wavelength are shorter than the excitation wavelength is removed, and as shown in FIG. 6C, the fluorescence fingerprint information representing the characteristics of the measurement target is extracted.

  As described above, the fluorescent fingerprint information is three-dimensional data including the excitation wavelength, the fluorescent wavelength, and the fluorescent intensity. Since many analysis methods are developed for two-dimensional data, it is necessary to develop three-dimensional data into two dimensions in order to use them.

  For example, as shown in FIG. 6C, in the case of a fluorescence fingerprint acquired at excitation wavelengths 200 to 700 nm, fluorescence wavelengths 200 to 700 nm, excitation wavelengths, and fluorescence wavelengths at 5 nm intervals, after the fluorescence spectrum at excitation wavelength 200 nm, The fluorescence spectra are connected in a line, such as a fluorescence spectrum with an excitation wavelength of 205 nm, and then a fluorescence spectrum with an excitation wavelength of 210 nm.

  An example of the two-dimensional data developed in this manner is shown in FIG. FIG. 6D shows three fluorescence spectra, where the horizontal axis represents wavelength conditions (combination of excitation wavelength and fluorescence wavelength) and the vertical axis represents fluorescence intensity.

  In addition, in the present embodiment, the multivariate analysis may be performed on the three-dimensional data as it is without performing the first-stage processing.

<2.2 Signal Processing Calculation for Two-Dimensional Fluorescent Fingerprint Information (Second Stage Processing)>
For fluorescence fingerprint information developed in two dimensions, mean centering, normalization, autoscale, second derivative, baseline correction, and smoothing. One or a combination of (smoothing) is performed to perform the signal processing operation.

  By performing the second-step data preprocessing, there are effects such as enhancement of the information contained in the fluorescence spectrum and matching of the scales of the fluorescence spectra of different samples.

  FIG. 7 is a diagram showing an example in which the fluorescent fingerprint information of FIG. 6 (D) is centralized. In the "centering", an average of the fluorescence intensities of all the samples is obtained for each wavelength condition, and a process of subtracting from the fluorescence intensities so that the average becomes "0" is performed.

  FIG. 8 is a diagram showing an example in which the fluorescent fingerprint information of FIG. 6 (D) is standardized. In the “normalization”, the integral of the fluorescence intensity (the area under the spectrum) is obtained for each sample, and the coefficient is multiplied for each sample so that the integral is “1” in all the samples.

  FIG. 9 is a diagram showing an example in which the fluorescent fingerprint information of FIG. 6 (D) is standardized. In “standardization”, a process of subtracting the average and then dividing by the standard deviation is performed so that the average becomes “0” and the standard deviation becomes “1” for each wavelength condition.

  In the present embodiment, the multivariate analysis may be performed without performing the second stage data preprocessing.

  Next, returning to FIG. 5, the actual measurement value measurement step is executed (step S13), and the actual measurement value is acquired by measurement of a plurality of samples. The measured values of the samples are acquired by measuring the plurality of samples created in S11. The measurement may include various physiological activity evaluations and chemical analysis. Examples of the measurement system of the actual measurement value include, for example, (1) responsiveness to samples of animals and plants including humans, microorganisms and the tissues thereof, (2) responsiveness to samples of primary cultured cells and established cultured cells, ( 3) Sample binding properties with antibodies, receptor proteins (either on the cell surface or immobilized on a substrate), (4) Quality evaluation of food odor, taste, freshness, antioxidant, etc. (5 ) It is applicable to various things such as the quality of wood, (6) the amount of residual organic matter related to water quality evaluation.

  The marker signal detection step is executed (step S15), the above-mentioned multivariate analysis is performed using the fluorescent fingerprint continuum information as an explanatory variable and the measured value as an objective variable, and based on the result of the multivariate analysis, an index based on the fluorescent fingerprint. Estimate the value and detect the marker signal, which is the wavelength region important for the estimation.

  Here, when performing a multivariate analysis, MATLAB (Mathworks Inc, USA), Unscrambler (CAMO Software AS, Norway), JMP (SAS Institute Inc, USA, etc.), or Excel (Microsoft, USA, etc.) is available. You can use other software or original software.

  Then, the component estimation step is executed (step S16), and the component of the detected marker signal is estimated (identified) using a database or the like.

[Fluorescent fingerprint measuring device]
Next, the configuration of the fluorescent fingerprint measuring apparatus of the present invention will be described with reference to FIGS. 10 and 11 by taking an embodiment as an example.

  The fluorescent fingerprint measuring device according to the present embodiment can be suitably used for the above-mentioned fluorescent fingerprint measuring method, but the device used for the fluorescent fingerprint measuring method according to the present embodiment is not limited to this. is not.

  Here, FIG. 10 is a block diagram showing an example of the configuration of the fluorescent fingerprint measuring apparatus according to the present embodiment, and conceptually shows only the portion related to the present invention in the configuration.

  As shown in FIG. 10, the fluorescent fingerprint measuring device 20 includes at least the fluorescent fingerprint acquiring device 10. The fluorescent fingerprint acquisition device 10 is a device that acquires fluorescent fingerprint information, and includes a spectral illumination device 11 and a spectral detection device 12.

  The fluorescent fingerprint measuring device 20 is a device that detects a marker signal of the measurement target 13 from the fluorescent fingerprint continuum information acquired by the fluorescent fingerprint acquiring device 10, and includes a memory 21, a control unit 23, and a calculation processing unit. 24, the measurer inputs the measurement conditions and the like to the fluorescent fingerprint acquisition device 10 with the keyboard / mouse 22.

  Here, FIG. 11 is a block diagram showing an example of the fluorescent fingerprint acquisition apparatus 10, and conceptually shows only a portion related to the present invention in the configuration.

  As shown in FIG. 11, the fluorescent fingerprint acquisition device 10 includes a spectral illumination device 11 including a light source 110 and a spectroscopic device 112, and a spectral detection device 12 including a spectroscopic device 122 and a fluorescent fingerprint detection device 124. ing.

  The spectral illumination device 11 is a device that irradiates the measurement target 13 with excitation light having a predetermined wavelength to generate fluorescence from the components of the measurement target 13. The measurement target 13 is, for example, a plurality of samples having different components extracted from the above-described one sample. The spectral illumination device 11 has an excitation wavelength variable means for arbitrarily changing the excitation wavelength to be applied.

  The spectroscopic detection device 12 acquires the fluorescence intensity of the measurement target 13 at a predetermined fluorescence wavelength. The spectroscopic detection device 12 selectively captures a specific fluorescence wavelength of the fluorescence emitted by the measurement target 13 and measures the fluorescence intensity. The spectroscopic detection device 12 has a fluorescent wavelength variable means for arbitrarily changing the observed fluorescent wavelength.

  Now, returning to FIG. 10, the fluorescent fingerprint measuring device 20 will be described.

  The fluorescent fingerprint measuring device 20 is a device for executing the above-described fluorescent fingerprint measuring method. As shown in FIG. 10, the fluorescence fingerprint measurement device 20 includes a memory 21, a control unit 23, and a calculation processing unit 24, and includes a keyboard / mouse 22, an I / O port (for example, a USB port) 26, Also, the display 30 and the like are connected.

  The memory 21 is transferred from the fluorescence fingerprint detection device 124 of the fluorescence fingerprint acquisition device 10 to the fluorescence fingerprint measurement device 20, and the fluorescence fingerprint continuum information acquired by the fluorescence fingerprint continuum information acquisition unit 24-1 of the calculation processing unit 24, In addition, the actual measurement value by measurement is stored.

  The actual measurement value can be input from the keyboard / mouse 22 or the I / O port 26.

  The control unit 23 receives the excitation wavelength range input by the measurer, the fluorescence wavelength range, the excitation wavelength irradiated by the spectral illumination device 11 so as to acquire the fluorescent fingerprint of the measurement target 13 at the wavelength pitch, and the spectral detection device 12. Gives an instruction to adjust the observed fluorescence wavelength, and also instructs the calculation processing unit 24 to perform processing.

  Here, the fluorescent fingerprint continuum information transferred from the fluorescent fingerprint acquisition device 10 is stored in the memory 21 of the fluorescent fingerprint measurement device 20.

  The calculation processing unit 24 includes a fluorescent fingerprint continuum information acquisition unit 24-1, a data preprocessing unit 24-2, a marker signal detection unit 24-3, and a component estimation unit 24-4.

  Here, the fluorescent fingerprint continuum information acquiring unit 24-1 has a predetermined excitation wavelength range and a predetermined fluorescence wavelength range for a plurality of samples (measurement target 13) having different components extracted from one sample, respectively. Then, the fluorescence intensities of the plurality of samples are measured while gradually changing the excitation wavelength to be irradiated and the fluorescence wavelength to be observed, and the fluorescence fingerprint information of the plurality of samples is acquired as the fluorescence fingerprint continuum information.

  The data pre-processing unit 24-2 performs centering, standardization, standardization, differentiation, baseline correction, and / or smoothing processing on the fluorescent fingerprint continuum information, respectively.

  The marker signal detection unit 24-3 performs multivariate analysis using the fluorescent fingerprint continuum information as an explanatory variable and the measured value as an objective variable, and estimates and estimates an index value based on the fluorescent fingerprint based on the result of the multivariate analysis. Detects marker signals, which are important wavelength regions for Here, the marker signal detection unit 24-3 performs principal component regression, cluster analysis, discriminant analysis, SIMCA, multiple regression analysis, PLS regression analysis, PLS discrimination, SVM regression, SVM discrimination, RF regression, and / or multivariate analysis. Alternatively, the mark signal of the sample may be detected by performing RF discrimination. In addition, based on the regression coefficient obtained by multivariate analysis, factor loading, loading, selectivity ratio, variable impact in projection, variable importance, and index of one or more contributions to regression / discrimination of out of bag error. Alternatively, the marker signal may be detected.

  The component estimating unit 24-4 estimates the component of the mark signal detected by the marker signal detecting unit 24-3 by referring to the database and the document information according to the operation of the keyboard / mouse 22 by the measurer.

  For example, when the measurer instructs the calculation processing unit 24 to perform a process through the keyboard / mouse 22, first, the fluorescent fingerprint continuum information acquisition unit 24-1 of the calculation processing unit 24 is stored in the memory 21. Extract fluorescent fingerprint continuum information.

  The marker signal detection unit 24-3 of the calculation processing unit 24 performs multivariate analysis on the extracted fluorescent fingerprint information and the actual measurement value (after the preprocessing by the data preprocessing unit 24-2) to obtain the marker signal. To detect. In the present embodiment, the marker signal detection unit 24-3 of the calculation processing unit 24 may detect the marker signal by using OPLS regression analysis as the multivariate analysis, for example.

  The marker signal detection unit 24-3 of the calculation processing unit 24 may store the result of the multivariate analysis in the memory 21, output it on the display 30, or print it via a printer (not shown). You may.

  The component estimation unit 24-4 of the calculation processing unit 24 estimates the component of the marker signal of the fluorescent fingerprint.

[Example]
Embodiments will be described below with reference to FIGS. 12 to 20. The present invention is not limited to this embodiment.

  In this example, a case where the active ingredient of leaves of Plectranthus ambonicus Lour (PA), which is a traditional medicine used as a lactation promoter in Indonesia, is estimated by using the fluorescent fingerprint measuring method of the present invention will be described. To do.

  FIG. 12 is a diagram showing an example of a sample extraction device for explaining an example of the sample extraction step (S11), and FIG. 13 is a diagram showing a time schedule of stepwise mixing of solvents to be used.

  As shown in FIG. 12, in the sample extraction step, 1 g of the above-mentioned dried powder of Indonesian traditional medicine was used together with 5 g of methanol-washed 20-35 mesh sea sand (Wako Pure Chemical Industry Ltd., Osaka, Japan) in stainless steel. It was packed in a steel column (5 cm long, 2 cm diameter), and by a gradient pump (eg: Jasco PU-980 Gulliver HPLC pump (Jasco, Tokyo, Japan)), 3 ml of a solvent in which hexane, acetone, and water were mixed stepwise. Fractions were obtained every 5 minutes at a flow rate of / min and extracted for a total of 75 minutes to obtain 15 types of fractions. More specifically, as shown in FIG. 13, hexane 100% is used for 0-15 minutes, a mixture of hexane 100% and acetone 100% is used for 15-30 minutes, and acetone 100% is used for 30-45 minutes. %, A mixed solution of 100% acetone and 100% water was used for 45 to 60 minutes, and 100% water was used for 60 to 75 minutes.

  This was repeated 3 times to obtain 45 kinds of fractions. The extraction solvent was volatilized with a centrifugal evaporator (eg, Eyela Centrifugal Vaporizer CVE-200D, Tokyo Rikakikai Co., Ltd., Japan) to the 45 types of fractions to obtain a dry solid of 45 types of fractions. It was These dried solids were redissolved in 500 mL of dimethyl sulfoxide (DMSO, Wako Pure Chemical Industry Ltd., Osaka, Japan) to obtain 45 kinds of samples. The samples are "A" for the first time, "B" for the second time, "C" for the third time, and "1" to "15" in the order of acquisition. For example, the third sample acquired the second time becomes “B3”.

  FIG. 14: is a figure which shows the Example of a fluorescent fingerprint continuous body acquisition process (S12). As shown in FIG. 14, in the fluorescent fingerprint continuum acquiring step, 1 mL of each of 45 types of samples was placed in a 1 mL cell (FM20-SQ-3, GL Sciences Inc., Tokyo, Japan), and F With the -7000 type fluorescence spectrophotometer, the slit width of both excitation light and fluorescence is 10 nm, and the fluorescence fingerprint information is acquired in the range of 200-900 nm and the wavelength interval of 10 nm, thereby acquiring the fluorescence fingerprint continuum information. did. The wavelength scan speed and the photomultiplier voltage were set to 500 nm / s and 400 V, respectively.

15 and 16 are diagrams for explaining an example of the actual measurement value measuring step (S13).
As shown in FIG. 15, spleen immune cells collected from DO11.10 mice were stimulated with ovalbumin as an antigen, samples A1 to C15 (45 cells) were added, respectively, and cultured for 72 hours. IFN-γ, which is an inflammatory cytokine, was measured by the ELISA method.

  FIG. 16 shows the amount of IFN-γ produced in each sample-added group as a relative value, with the amount of IFN-γ in the cell culture supernatant added with DMSO alone as 100%. As shown in FIG. 16, the hexane-extracted fractions (1A, 1B, 1C) showed the strongest inhibitory activity, but 5A, 6A, 6B, 7B, 11A also showed the inhibitory activity, so that the active ingredient has a high polarity. It was shown to range from low to low.

  17 to 18 are diagrams for explaining an example of the marker signal detection step (S15). FIG. 17 is a diagram showing a graph of OPLS S-plot of fluorescent fingerprint continuum information and IFN-γ production suppression data. FIG. 18 is a diagram showing the marker signal of the fluorescent fingerprint of this example. As shown in FIG. 17, as a result of OPLS analysis, a sample having IFN-γ suppressing activity and a sample having no IFN-γ suppressing activity could be separated on the score plot. This analysis is based on the means of cross validation and permutation test. We confirmed the validity. From the graph of FIG. 17, it was shown that the signal at λex = 265-275 nm and λem = 295-310 nm is the marker of the strongest group. In FIG. 18, the horizontal axis represents the excitation wavelength λex and the vertical axis represents the fluorescence wavelength λem, and the circled portion (λex = 265-275 nm and λem = 295-310 nm) represents the marker signal.

  FIG. 19 is a diagram showing a conventional fluorescent fingerprint when the same sample was extracted with hexane. The hexane extract showed strong IFN-γ, but a marker signal could be detected in its fluorescent fingerprint although it was weak. That is, by once detecting the marker signal with the continuous fluorescent fingerprint, it is possible to confirm it even with a normal fluorescent fingerprint although it is weak. Conversely, it is difficult to detect this marker signal only with a normal fluorescent fingerprint.

  FIG. 20 is a diagram showing an example of the component estimation step (S16). As shown in FIG. 20, azurinic acid, ursolic acid, oleanolic acid, and tolmentic acid were estimated as active ingredient candidates from literature information and NMR analysis information. When these fluorescent fingerprints were measured, these signals were within the area of the component.

  As described above, according to the present embodiment, for one sample, the extraction condition is continuously changed to extract a plurality of samples having different components, and a plurality of extracted samples are extracted. On the other hand, respectively, the fluorescence intensity is measured while changing the excitation wavelength to be irradiated and the fluorescence wavelength to be observed in a stepwise manner, and a fluorescent fingerprint continuum information acquisition step of acquiring a plurality of fluorescent fingerprint information as fluorescent fingerprint continuum information. Since, the marker signal of the fluorescent fingerprint is included, it is possible to detect the marker signal of the fluorescent fingerprint quickly, inexpensively and highly accurately.

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

  Further, of the processes described in the embodiments, all or part of the processes described as being automatically performed may be manually performed, or the processes described as manually performed may be performed. All or part of the process can be automatically performed by a known method.

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

  Further, regarding the fluorescent fingerprint measuring apparatus 20, the constituent elements shown in the drawings are functionally conceptual, and do not necessarily have to be physically configured as illustrated.

  For example, with respect to the processing functions provided in the respective devices of the fluorescent fingerprint measuring apparatus 20, particularly the respective processing functions performed by the calculation processing unit 24, all or an arbitrary part thereof is processed by a CPU (Central Processing Unit) and the CPU. It may be realized by a program to be interpreted and executed, or may be realized as hardware by a wired logic. The program is recorded in a recording medium described later, and can be mechanically read by the fluorescent fingerprint measuring device 20 as needed. That is, the memory 21 such as the ROM or the HD stores a computer program for cooperating as an OS (Operating System) to give instructions to the CPU and perform various processes. This computer program is executed by being loaded in the RAM, and constitutes a control unit in cooperation with the CPU.

  Further, this computer program may be stored in an application program server connected to the fluorescent fingerprint measuring apparatus 20 via an arbitrary network, and it is possible to download all or a part of it as necessary. Is.

  Further, the program according to the present invention may be stored in a computer-readable recording medium, or may be configured as a program product. Here, the "recording medium" means a memory card, a USB memory, an SD card, a flexible disk, a magneto-optical disk, a ROM, an EPROM, an EEPROM, a CD-ROM, an MO, a DVD, and a Blu-ray (registered trademark). It includes any "portable physical medium" such as Disc.

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

  Various databases and the like stored in the memory 21 are memory devices such as RAM and ROM, fixed disk devices such as hard disks, storage means such as flexible disks and optical disks, and various programs used for various processes and website provision. Stores tables, databases, web page files, etc.

  Further, the fluorescent fingerprint measuring apparatus 20 may be configured as an information processing apparatus such as a known personal computer or workstation, or may be configured by connecting an arbitrary peripheral device to the information processing apparatus. Further, the fluorescent fingerprint measuring apparatus 20 may be realized by installing software (including programs, data, etc.) for realizing the method of the present invention in the information processing apparatus.

  Furthermore, the specific form of the distribution / integration of the devices is not limited to that shown in the drawings, and all or a part of the devices may be functionally or physically united in arbitrary units according to various additions or functional loads. Can be distributed and integrated. That is, the above-described embodiments may be implemented in any combination, or the embodiments may be selectively implemented.

10 Fluorescent Fingerprint Acquisition Device 11 Spectral Illumination Device 110 Light Source 112 Spectroscopic Device 12 Spectral Detection Device 122 Spectroscopic Device 124 Fluorescent Fingerprint Detection Device 13 Measurement Target 20 Fluorescent Fingerprint Measurement Device 21 Memory 22 Keyboard / Mouse 23 Control Unit 24 Calculation Processing Unit 24- 1 Fluorescent fingerprint continuum information acquisition unit 24-2 Data pre-processing unit 24-3 Marker signal detection unit 24-4 Component estimation unit 26 I / O port 30 display

Claims (22)

A sample extraction step of extracting a plurality of samples having different components by continuously transitioning the extraction conditions for one sample,
For each of the extracted plurality of samples, the fluorescence intensity is measured while gradually changing the excitation wavelength to be irradiated and the fluorescence wavelength to be observed, and a plurality of fluorescence fingerprint information is acquired as fluorescence fingerprint continuum information. Fingerprint continuum information acquisition process,
A method for extracting a component, comprising: further,
An actual measurement value acquisition step of acquiring actual measurement values by measurement for the plurality of extracted samples,
The fluorescent fingerprint continuum information is an explanatory variable, multivariate analysis is performed with the measured value as an objective variable, and based on the result of the multivariate analysis, an index value based on the fluorescent fingerprint is estimated, which is an important wavelength region for estimation. A marker signal detection step of detecting a marker signal,
The component extraction method according to claim 1, comprising:   3. The component extraction method according to claim 1, wherein the one sample contains medicinal plants, functional foods, functional fruits and vegetables, ordinary foods, wood, and water to be evaluated for water quality.   The component extraction method according to any one of claims 1 to 3, wherein in the sample extraction step, the plurality of samples are extracted by an extraction method based on polarity, temperature, or molecular weight.   The component extraction method according to claim 2, wherein the measurement includes various physiological activity evaluations and chemical analysis.   The multivariate analysis is one or more of principal component regression, cluster analysis, discriminant analysis, SIMCA, multiple regression analysis, PLS regression analysis, PLS discrimination, SVM regression, SVM discrimination, RF regression, and RF discrimination. The component extraction method according to claim 2.   In the marker signal detection step, a regression coefficient obtained by multivariate analysis, factor loading, loading, selectivity ratio, variable impact in projection, variable importance, contribution rate to one or more regressions / discriminations of out of bag error. The method for extracting a component according to claim 6, wherein the marker signal is detected based on the index indicating.   3. The method according to claim 2, further comprising a data pre-processing step of performing centering, standardization, normalization, differentiation, baseline correction, and / or smoothing processing on the fluorescent fingerprint continuum information. The described component extraction method.   Fluorescence intensity is measured while stepwise varying the excitation wavelength and the observed fluorescence wavelength for multiple samples with different components obtained by continuously transitioning the extraction conditions for one sample. Then, the fluorescent fingerprint measuring device is provided with a fluorescent fingerprint continuum information acquiring unit that acquires a plurality of fluorescent fingerprint information as fluorescent fingerprint continuum information. further,
The fluorescent fingerprint continuum information is an explanatory variable, multivariate analysis is performed with the actual measurement value obtained by measuring the plurality of samples as an objective variable, and based on the result of the multivariate analysis, an index value based on the fluorescent fingerprint is estimated and estimated. The fluorescent fingerprint measuring device according to claim 9, further comprising a marker signal detecting means for detecting a marker signal in an important wavelength region.   The fluorescence fingerprint measuring apparatus according to claim 9 or 10, wherein the one sample contains medicinal plants, functional foods, functional fruits and vegetables, ordinary foods, wood, and water for water quality evaluation. ..   The fluorescent fingerprint measuring device according to claim 10, wherein the measurement includes various physiological activity evaluations and chemical analysis.   The multivariate analysis is one or more of principal component regression, cluster analysis, discriminant analysis, SIMCA, multiple regression analysis, PLS regression analysis, PLS discrimination, SVM regression, SVM discrimination, RF regression, and RF discrimination. The fluorescent fingerprint measuring device according to claim 10.   The marker signal detection means is a coefficient of regression obtained by multivariate analysis, factor loading, loading, selectivity ratio, variable impact in projection, variable importance, contribution rate to one or more regressions / discriminations of out of bag error. The fluorescence fingerprint measuring device according to claim 13, wherein the marker signal is detected based on the index indicating.   11. The method according to claim 10, further comprising a data pre-processing step of performing centering, standardization, normalization, differentiation, baseline correction, and / or smoothing processing on the fluorescent fingerprint continuum information. The fluorescent fingerprint measuring device described.   Fluorescence intensity is measured while stepwise varying the excitation wavelength and the observed fluorescence wavelength for multiple samples with different components extracted by continuously transitioning the extraction conditions for one sample. Then, a computer-executable program for causing a computer to execute a fluorescent fingerprint continuum information acquisition step of acquiring a plurality of fluorescent fingerprint continuum information as fluorescent fingerprint continuum information. further,
An actual measurement value acquisition step of acquiring actual measurement values by measurement for the plurality of samples,
The fluorescent fingerprint continuum information is an explanatory variable, multivariate analysis is performed with the measured value as an objective variable, and based on the result of the multivariate analysis, an index value based on the fluorescent fingerprint is estimated, which is an important wavelength region for estimation. A marker signal detection step of detecting a marker signal,
The computer-executable program according to claim 16, wherein the program is executed by a computer.   The computer according to claim 16 or 17, wherein the one sample contains medicinal plants, functional foods, functional fruits and vegetables, ordinary foods, wood, and water to be evaluated for water quality. Programs.   The computer-executable program according to claim 17, wherein the measurement includes various types of physiological activity evaluation and chemical analysis.   The multivariate analysis is one or more of principal component regression, cluster analysis, discriminant analysis, SIMCA, multiple regression analysis, PLS regression analysis, PLS discrimination, SVM regression, SVM discrimination, RF regression, and RF discrimination. The computer-executable program according to claim 17.   In the marker signal detection step, a regression coefficient obtained by multivariate analysis, factor loading, loading, selectivity ratio, variable impact in projection, variable importance, contribution rate to one or more regressions / discriminations of out of bag error. The computer-executable program according to claim 20, wherein the marker signal is detected based on the index indicating.   18. The method according to claim 17, further comprising a data pre-processing step of performing centering, standardization, normalization, differentiation, baseline correction, and / or smoothing processing on the fluorescent fingerprint continuum information. The computer-executable program described.