JP2006087913A - Method of preparing calibration curve for quantitatively analyzing in-vivo component and quantitative analyzer using calibration curve - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method of non-invasively determining a concentration of an in-vivo component such as blood sugar level (glucose) of a subject. <P>SOLUTION: The concentration of the in-vivo component of a subject is determined by use of the absorbance spectrum and a calibration curve of the subject measured by use of near-infrared light. The calibration curver is prepared by determining a plurality of difference absorbance spectra that are differences between a plurality of near-infrared absorbance spectra of a living body and a reference absorbance spectrum selected from the near-infrared absorbance spectra, synthesizing each of the difference absorbance spectra with a previously measured reference absorbance spectrum of the subject, thereby obtaining a plurality of synthetic absorbance spectra, and performing a multivariate analysis with use of the obtained synthetic absorbance spectra. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a technique for noninvasively estimating in-vivo component concentrations such as blood glucose levels (glucose).

  For health management and medical treatment, a method of noninvasively analyzing in vivo components such as glucose, protein, lipid, moisture, urea and the like without drawing blood is attracting attention. When near-infrared light is used in this analysis method, since the absorption spectrum of water in the near-infrared region is small, it is possible to analyze an aqueous solution and to have an advantage that near-infrared light easily propagates in the living body. On the other hand, the near-infrared signal level is very small compared to the mid-infrared signal level, and the absorption signal of the target body component such as glucose is less than that of other body components such as water, lipids and proteins. Since it is sensitive to changes in concentration, it has been difficult to accurately analyze the target body component using the peak position and peak height.

  In recent years, it has been proposed to use multivariate analysis such as PLS regression analysis in order to improve these deficiencies in near infrared spectroscopy. In this case, practical quantitative analysis using near-infrared light is possible even if the absorption signal in the near-infrared region has a low S / N ratio or changes in the concentration of other body components. become.

  For example, a method for obtaining a glucose concentration in a target using near infrared spectroscopy has been proposed (Patent Document 1). In this method, near-infrared radiation is projected onto the subject's skin, and the emitted light from the skin is received by an optical fiber bundle. Performing spectral analysis of the received synchrotron radiation, a first wavelength region having an absorption peak of OH groups derived from glucose molecules (for example, 1550 to 1650 nm), a second wavelength region having an absorption peak of NH groups (for example, 1480 to 1550 nm), an absorption signal is detected from a third wavelength region (for example, 1650 to 1880 nm) having a CH group absorption peak. The glucose concentration is determined by multivariate analysis using these absorption signals as explanatory variables.

Further, a method for obtaining the concentration of the target component in the medium based on a stochastic statistical simulation has been proposed (Patent Document 2). In this method, the optical path group in the medium is analyzed by a stochastic statistical simulation such as the Monte Carlo method. In addition, a data table showing changes in diffuse reflectance when the absorption coefficient and equivalent scattering coefficient that are optical characteristics of the medium are changed within a predetermined range is created, and then smoothing processing of diffuse reflectance is performed by a regression analysis method. The correction data table is created by executing. Next, the measured spectrum obtained by irradiating the medium with light such as near-infrared light in the wavelength range of 1000 to 2500 nm and detecting the emitted light therefrom is used as a reference spectrum provided from the correction data table. To obtain the concentration of the target component in the medium. In addition, if the spectrum change caused by the concentration change of components other than the target component in the medium is calculated from the correction data table, it can be calculated from the measured spectrum by multivariate analysis such as principal component regression analysis (PCR) and multiple regression analysis (MLR). It is said that the concentration of the target component can be obtained.
Japanese Patent Laid-Open No. 10-325794 JP 2003-50200 A

  However, it is known that the skin of a living body irradiated with near-infrared light generally has a non-uniform structure, and there are individual differences in skin thickness and skin structure. In addition, the concentration of the in-vivo component of the subject measured in the morning of one day is often different from the concentration of the in-vivo component of the subject measured in the evening of the same day. Thus, fluctuations in the daily concentration of the in-vivo component of the subject and other in-vivo components that affect the in-vivo component concentration cause a decrease in the estimation accuracy of the in-vivo component.

  FIG. 5A shows the measurement results showing the relationship between the concentration of the target in-vivo component (blood glucose level) and the concentration of other in-vivo components in each of the three subjects (A, B, C). In this figure, the ellipses (A1, A2, A3, A4) indicate the measurement results of subject A on different days. Similarly, the ellipse (B1, B2, B3) indicates the measurement result of the subject B on different days, and the ellipse (C1, C2, C3) indicates the measurement result of the subject C on different days. The plot m in each ellipse shows the fluctuation of the measurement result within one day for each subject. By the way, when there is a data deficient region G as shown by the dotted line in FIG. 5A, it is possible to obtain high estimation accuracy in the data deficient region G using the calibration curve created from the measurement result of FIG. Have difficulty.

  Therefore, in order to obtain a stable reliability of the estimation accuracy of the target body component, it is desirable to create a calibration curve using more data. However, it means a significant increase in the time required for data collection. Furthermore, when glucose, that is, a blood glucose level is selected as the target body component, the glucose absorption signal is very weak. Therefore, even if the data amount is increased, there is a possibility that the estimation accuracy cannot be sufficiently improved due to the influence of noise.

  Therefore, a main object of the present invention is to provide a method for preparing a calibration curve for quantitative analysis, which can estimate the concentration of a target in-vivo component such as glucose that changes every moment with stable accuracy. That is, the method of the present invention obtains a plurality of differential absorbance spectra that are differences between a plurality of near-infrared absorbance spectra of a living body and a reference absorbance spectrum, and each of the differential absorbance spectra has a second reference different from the reference absorbance spectrum. A plurality of synthesized absorbance spectra are obtained by synthesizing the absorbance spectra, and a calibration curve is created by multivariate analysis using the plurality of synthesized absorbance spectra.

  In the above method, the plurality of near-infrared absorbance spectra are preferably measured by irradiating a living body with near-infrared light and performing spectrum analysis of radiation emitted from the living body.

  In the above method, it is preferable to obtain a plurality of differential absorbance spectra based on a light propagation simulation defined as a method for analyzing light propagation in a modeled living body. In this case, a calibration curve can be created without using a device that measures a near-infrared absorbance spectrum from a living body. In addition, since there is no influence of the noise component in the simulation, it is possible to create a calibration curve with high reliability in estimation accuracy even if the internal component having a weak absorption signal such as glucose is the target internal component. it can.

  In the above method, the reference absorbance spectrum is preferably selected from a plurality of near infrared absorbance spectra. When a plurality of near-infrared absorbance spectra are measured over a plurality of days, the reference absorbance spectrum is preferably selected from a plurality of near-infrared absorbance spectra measured on each of the plurality of days.

  In the method for creating a calibration curve for quantitative analysis according to a preferred embodiment of the present invention, the concentrations of the components in the target body at the time of measuring each of the near-infrared absorbance spectra are measured from the living body by, for example, a blood sampling method, A plurality of differential concentrations, which are the differences between the target in-vivo component concentration and the reference concentration, are determined. For example, the reference concentration is selected from the measured concentrations of a plurality of target body components. In this case, the calibration curve is created by multivariate analysis using a difference data table composed of the difference absorbance spectrum and the difference concentration.

  It is a further object of the present invention to provide a method for non-invasively determining a body component concentration of a subject. That is, this method is characterized by measuring the near-infrared absorbance spectrum of the subject and determining the concentration of the in-vivo component of the subject using the calibration curve created by the above method and the measured near-infrared absorbance spectrum of the subject. And

  Another object of the present invention is to provide a quantitative analysis device that noninvasively determines the concentration of a body component of a subject. That is, this apparatus includes a light irradiating means for irradiating a subject's skin with near-infrared light, a light receiving means for receiving radiated light from the skin, a memory for storing a calibration curve created by the above-described method, It comprises an operation means for calculating an in-vivo component concentration of the subject using an output of the light receiving means and a calibration curve read from the memory.

  According to the present invention, by using a differential absorbance spectrum for multivariate analysis, the influence of individual differences in living tissue, the influence of variation in measurement locations, the influence of scattering on biological tissue changes, and other in vivo components The influence of the fluctuation of the absorbance spectrum of the target body component caused by the concentration change can be removed, and as a result, improved estimation accuracy of the target body component concentration can be realized. For example, when used for quantitative analysis for a subject with a calibration curve, the second reference absorbance spectrum is the subject's pre-measured near-infrared absorbance spectrum. Alternatively, when a plurality of near-infrared absorbance spectra of the subject are measured in advance, the average may be used as the second reference absorbance spectrum. Since the synthetic absorbance spectrum takes into account changes in the daily concentration of the target body component of the subject, quantitative analysis can be performed more accurately using a calibration curve unique to the subject.

A method for creating a calibration curve of the present invention, a quantitative analysis of a body component of a subject using the calibration curve, and an apparatus used therefor will be described in detail based on the following preferred embodiments.
<First Embodiment>
As shown in FIG. 1, the apparatus of this embodiment for performing non-invasive quantitative analysis of a subject's body components using near-infrared light is applied to an object such as the skin of the subject. A light irradiating means for irradiating light, a light receiving means for receiving radiated light from the skin, a memory for storing a calibration curve created by a method described later, and a calibration curve read from the memory and the output of the light receiving means And calculating means for calculating the concentration of the body component of the subject.

  The light irradiation means is a light source 10 such as a halogen lamp that emits near-infrared light, one end is connected to the light source 10 via an optical system, and the other end is used to project near-infrared light onto the skin S of the subject. A first optical fiber 12 to which a measurement probe 13 is connected, and a reference probe 15 for projecting near-infrared light onto a reference plate T such as a ceramic plate, one end of which is connected to the light source 10 via an optical system. Are connected to the second optical fiber 14. The optical system is disposed on the light irradiation side between the light source 10 and the first and second optical fibers (12, 14). For example, the optical member 16 having a pinhole, the lens 17, and the near infrared emitted from the light source. A beam splitter 18 is included that divides the light into first and second optical fibers, respectively.

  By the way, the skin of a living body like a human is usually composed of an epidermis layer including a stratum corneum, a dermis layer, and a subcutaneous tissue layer, and an approximate thickness of the epidermis layer is 0.2 to 0.4 mm. The approximate thickness is 0.5-2 mm, and the approximate thickness of the subcutaneous tissue layer is 1-3 mm. In the dermis layer, internal components easily move through capillaries. In particular, changes in blood glucose concentration (that is, blood glucose level) are considered to appear rapidly as changes in glucose concentration in the dermis layer. On the other hand, since the subcutaneous tissue layer contains fat as a main component, water-soluble components such as glucose are unlikely to exist uniformly in the subcutaneous tissue layer. From these considerations, it is desirable to selectively measure the near-infrared absorbance spectrum from the dermis layer in order to accurately determine the blood glucose concentration.

  In the present embodiment, the first optical fiber 12 is disposed on the end face of the measurement probe 13 as shown in FIG. That is, the end surfaces of the first optical fibers 12 are arranged at regular intervals on a circumference having a predetermined radius. Further, as will be described later, the third optical fiber 20 for receiving the emitted light from the skin is disposed at the center of the circle. The radius of this circle is preferably 2 mm or less. In this embodiment, the radius is 0.65 mm. The reference probe 15 has substantially the same structure as the measurement probe 13.

  The light receiving means receives the third optical fiber 20 having one end connected to the measurement probe 13, the fourth optical fiber 22 having one end connected to the reference probe 15, and the emitted light from the skin "S" or the reference plate "T". For this purpose, a light receiving element 29 optically connected to the other ends of the third and fourth optical fibers (20, 22) through an optional optical system is included. The optical system is disposed on the light receiving side between the light receiving element 29 and the third and fourth optical fibers (20, 22), and includes, for example, a shutter 25, a lens 26, a reflecting mirror 27, and a diffraction grating 28. In this way, near-infrared light enters the subject's skin “S” from the measurement probe 13, is reflected or diffused in the skin, and radiated light from the skin is received by the light receiving element 29 via the third optical fiber 20. .

  In the present embodiment, the emitted light from the reference plate “T” is used as the reference light in order to correct near-infrared light fluctuations caused by changes in the ambient temperature and the positional relationship of the optical components. That is, near-infrared light radiated from the light source 10 enters the surface of the reference plate from the measurement probe 13, and radiated light therefrom is received by the light receiving element 29 via the fourth optical fiber 22. In this case, by adjusting the optical system, either one of the radiated light from the third and fourth optical fibers (20, 22) is selectively received by the light receiving element 29.

  The light received by the light receiving element 29 is converted into an electrical signal and A / D converted by the A / D converter 40. The output of the A / D converter 40 is sent to a calculation unit 50 such as a personal computer. The computing unit 50 performs spectrum analysis of the received light, and calculates the concentration of the in-vivo component based on a method described later. Specifically, the calculation unit 50 uses a signal “Ref” obtained from the reference plate “T” and a signal “Sig” obtained from the skin “S” of the subject using multivariate analysis such as PLS analysis. Then, the concentration of the in-vivo component is calculated by analyzing the minute change in the absorbance spectrum derived from the change in the concentration of the in-vivo component. Here, the absorbance “Abs” is expressed as log 10 (Ref / Sig).

  FIG. 3 shows an example of the calculation unit 50. The calculation unit 50 includes a CPU unit 51, a RAM used for securing a work area, temporary storage of data, and the like, a first storage unit 53 including a ROM equipped with a basic program, and the CPU unit 50 and the second unit via an internal bus 52. The second storage unit 54 is basically composed of a second storage unit 54 connected to the first storage unit 53, and the second storage unit 54 is formed by, for example, a fixed disk device loaded with a predetermined program or data. The output of the light receiving element 29 is sent to the internal bus 52 via the A / D converter 40. As will be described later, in order to obtain a calibration curve, a device (for example, a blood collection type blood glucose meter) 60 for measuring data of internal components by blood collection is connected to the internal bus 52 via the interface unit 55.

  In the present embodiment, data necessary for creating a calibration curve is measured from a living body (preliminary measurement (I)). That is, as shown in FIG. 4, a set of the absorbance spectrum of the living body and the concentration (for example, blood glucose level) of the target body component of the living body is measured a plurality of times (S1). In each set, the measurement of the concentration in the target body component is performed at almost the same timing as the measurement of the absorbance spectrum. When creating a calibration curve for a subject, the living body in which data necessary for creating the calibration curve is measured is not limited to that subject. For example, any person other than the subject may be used. When measuring a blood glucose level, it is desirable to measure an absorbance spectrum in the wavelength range of 1200 nm to 1880 nm because the absorption due to glucose molecules is large and the influence of absorption due to water molecules is relatively small.

  Next, one of the measured absorbance spectra is selected as a reference absorbance spectrum. One of the measured plurality of in-vivo component concentrations is selected as the reference concentration. Next, a plurality of differential absorbance spectra are obtained by subtracting the reference absorbance spectrum from each of the measured absorbance spectra. Similarly, a plurality of differential concentrations can be obtained by subtracting the reference concentration from each of the measured in-vivo component concentrations. As a result, a difference data table composed of the difference absorbance spectrum and the difference concentration is created and stored in the second storage unit 54 (S2).

The measured concentration value used as the reference concentration and the measured absorbance spectrum used as the reference absorbance spectrum are data obtained at substantially the same time. Furthermore, they must satisfy the following conditions:
(1) When a set consisting of an absorbance spectrum and an in-vivo component concentration is measured from a plurality of persons as living bodies, the reference absorbance spectrum and the reference concentration are obtained for each person. By satisfying this condition, a differential absorbance spectrum that does not include the influence of individual differences can be obtained.
(2) When a set consisting of an absorbance spectrum and an in-vivo component concentration is measured on a plurality of days, the reference absorbance spectrum and the reference concentration are obtained every day. By satisfying this condition, a differential absorbance spectrum that does not include the influence of the daily difference can be obtained.
In order to obtain higher reliability of the calibration curve, it is desirable to obtain a pair of a reference absorbance spectrum and a reference concentration from a plurality of persons on a plurality of days.

  The above-described data collection will be described from the viewpoint of the calculation unit 50. In the calculation unit 50, the CPU unit 51 executes a spectrum measurement program stored in the second storage unit 54, and obtains an absorbance spectrum from the optical signal received by the light receiving element 29. On the other hand, the CPU unit 51 executes a blood sugar level measurement program. Data obtained by the blood-collecting blood glucose meter 60 is sent to the internal bus 52 via the interface unit 55 in order to obtain the in-vivo component concentration (blood glucose level). Further, the CPU unit 51 executes a data table creation program to obtain a difference absorbance spectrum and a difference component concentration, and a difference data table is created. The difference data table obtained in this way is stored in the second storage unit 54.

  The importance of using difference values in the present invention will be briefly described below. In the conventional method, as described above, it is difficult to obtain high estimation accuracy in the data deficient region G using the calibration curve created from the measurement result of FIG. In order to secure it, it is necessary to measure a very large amount of data for preparing a calibration curve. On the other hand, in each of the ellipses (A1 to A4, B1 to B3, C1 to C3) in FIG. 5A, a reference value R is obtained from the measured values m, and the difference value D is obtained by subtracting the reference value from each measured value. As shown in FIG. 5B, the difference value D gathers in a narrow area around the origin ORG. Even if the density value to be estimated is in the data insufficient region G of FIG. 5A, the data in such a region is rearranged around the origin ORG by obtaining the difference value. Therefore, by using the difference data, it is possible to obtain a calibration curve having a stable estimation accuracy that does not include the influence of individual differences and daily differences without measuring a very large amount of data.

  Next, as shown in “S3” of the provisional measurement (II) in FIG. 4, one set of the absorbance spectrum of the subject requiring the quantitative analysis of the target body component (blood glucose level) and the concentration value of the target body component is obtained. It is measured in the same manner as described above. The absorbance spectrum measured from the subject is defined as the second reference absorbance spectrum, and similarly, the concentration value measured from the subject is defined as the second reference concentration. When a plurality of absorbance spectra are measured from the subject, the average of the absorbance spectra can be used as the second reference absorbance spectrum. Similarly, when a plurality of concentration values are measured from the subject, the average of the concentration values can be used as the second reference concentration. In this case, it is assumed that the measured plurality of concentrations have substantially the same value. Further, the concentration value used as the second reference concentration and the absorbance spectrum used as the second reference absorbance spectrum do not have to be data obtained at substantially the same time.

  Next, the second reference absorbance spectrum is synthesized with each of the plurality of differential absorbance spectra to obtain a plurality of synthesized absorbance spectra (S4). The synthetic absorbance spectrum thus obtained provides a subject-specific absorbance spectrum corresponding to a change in the daily concentration of the target body component.

  Next, as shown in (S5) of FIG. 4, a calibration curve (often called a calibration function) is created by performing multivariate analysis such as PLS using the synthetic absorbance spectrum and the difference data table. By using this calibration curve, the concentration value of the target in-vivo component of the subject can be accurately estimated from the near-infrared absorbance spectrum measured noninvasively from the subject. In the present invention, since a difference data table composed of a difference absorbance spectrum and a difference concentration is prepared in advance, if only one set of the absorbance spectrum and the concentration value of the target body component is measured from the subject, the subject is specific to the subject. A calibration curve can be obtained quickly. The calibration curve obtained in this way includes information unique to the subject and information on diurnal changes of other components. For example, the number of principal components is approximately 7 to 10.

  The creation of the calibration curve described above will be described with reference to the flowchart of FIG. When a calibration curve creation command is input to the calculation unit 50 via the input unit 56 (S10), the CPU unit 51 executes the spectrum measurement program and measures the second reference absorbance spectrum of the subject (S11). Further, the CPU 51 executes a blood sugar level measurement program, and obtains the second reference concentration (blood sugar level) of the subject from the data measured by the blood collection type blood glucose meter 60 (S12). Next, a plurality of synthetic absorbance spectra are obtained by synthesizing the second reference absorbance spectrum with each of the differential absorbance spectra (S13). It is not necessary to combine the second reference density with each of the difference densities. The second reference concentration is used as a bias value when determining the concentration value of the target in-vivo component of the subject.

  Next, the CPU unit 51 executes a PLS analysis program stored in the second storage unit 54, and creates a calibration curve using a plurality of synthetic absorbance spectra and the difference data table stored in the second storage unit 54. (S14). The obtained calibration curve is stored in the second storage unit 54 (S15). As an example, a calibration curve obtained by the above-described procedure is shown in FIG. From this figure, it can be seen that the calibration curve has an absorption peak of glucose as a target body component in the vicinity of 1600 nm. As will be described later, a similar calibration curve can be obtained by the method of the second embodiment using light propagation simulation.

  By using the obtained calibration curve, the concentration of the in-vivo component of the subject is measured noninvasively (main measurement (III)). That is, the near-infrared absorbance spectrum is measured by irradiating the subject's skin with near-infrared light (S6). By comparing the measured absorbance spectrum with a calibration curve, the concentration value of the target body component of the subject at the time of measuring the absorbance spectrum is estimated (S7).

  Quantitative analysis of the subject's in-vivo components will be described with reference to the flowchart of FIG. When a command for quantitative analysis is input to the calculation unit 50 through the input unit 56 (S16), the CPU unit 51 executes a spectrum measurement program and measures the absorbance spectrum of the subject (S17). When the measurement is completed, the CPU unit 51 executes a concentration (blood sugar level) calculation program stored in the second storage unit 54. That is, the CPU unit 51 reads out a calibration curve unique to the subject stored in the second storage unit 54, compares the measured absorbance spectrum with the calibration curve, and calculates the concentration value of the target body component of the subject (S18). . The calculated density value is displayed on the monitor device 57 through the interface unit (S19). As described above, since a calibration curve having a stable and accurate estimation accuracy is obtained by the method of the present invention, it is possible to continuously and non-invasively measure the concentration value of the in-vivo component of the subject.

In the above method, the target in vivo component is not limited to glucose. For example, other components such as proteins, lipids, moisture, and urea may be target body components. In this case, it is necessary to store a program suitable for the desired in-vivo component in the second storage unit 54.
Second Embodiment
The calibration curve generation method of the present embodiment is substantially the first except that the differential absorbance spectrum is obtained based on a light propagation simulation defined as a method of analyzing light propagation in a modeled living body. This is the same as the method of the embodiment. Therefore, the overlapping description is omitted. In addition, an apparatus having the same configuration as that of the first embodiment can be used to perform quantitative analysis in this embodiment.

  In the present embodiment, the Monte Carlo method is adopted as the light propagation simulation. The Monte Carlo method is a statistical method that can accurately reproduce a target event using a function based on the probability distribution of the target event with respect to uniform random numbers in the range of 0 to 1 generated by a computer.

  When reproducing light propagation, the light incident on the medium is regarded as a collection of photons. The behavior of each photon in the medium is traced based on the optical characteristic values (μα: absorption coefficient, μS: scattering coefficient, p (θ): scattering phase function, n: refractive index) of the medium. As a result, light propagation can be statistically reproduced from the behavior of all photons. The scattering phase function θ is an angular change in the traveling direction of light caused by a single scattering, assuming symmetry with respect to the azimuth angle. An effective description of the scattering phase function is expressed by the Henyey-Greenstein function, as shown in the following equation (1), and is often used to express scattering of biological tissue including red blood cells.

In the equation (1), “g” is an anisotropic scattering parameter, and the scattering anisotropy expressed by p (θ) can be more easily characterized. g takes a value from 1 to −1, and when g = 1, 0, −1, the scattering characteristic is represented by perfect forward scattering, isotropic scattering, and backscattering corresponding to each. Scattering characteristics in a living body are expressed by very strong forward scattering, and scattering characteristics of a medium having weak absorption can be expressed almost by isotropic scattering.

  Also, based on the above approximation, the scattering coefficient μS is provided by the equivalent scattering coefficient μS = (1 g) μS. The optical path length L between two consecutive interactions (absorption, scattering) in the propagation of light is expressed by the following equation (2). Changes in zenith angle and azimuth angle (θ, φ) of refraction during interaction are expressed by the following equations (3) and (4), respectively.

In the above equation, each of R1, R2, and R3 is a uniform random number between 0 and 1, and ∫ (θ) is the cumulative probability of the scattering phase function. In the first interaction, the energy of the photon is absorbed based on the light absorption characteristic of the medium, and is expressed by the following equation (5).

  By the way, the skin of a living body includes a stratum corneum, a granular layer, a spiny layer, and a basal layer. The basal layer consists of a single layer of basal cells, and new cells are created at a certain level by cell division. New cells move upward toward the skin surface and change in the order of spinous cells → granule cells → keratinocytes, resulting in the formation of a stratum corneum. In the case of normal keratinocytes, one layer of keratinocytes peels off every day. The epidermis layer is reborn about every two weeks after the basal cells are newly born to become horny cells, about two weeks until the horny cells reach the skin surface and peel off. This means that the epidermal layer is composed of more than 20 cell layers. In the dermis layer, there are sweat glands, sebaceous glands and hair roots in addition to blood vessels, lymph vessels and nerves. Also, due to active physiological activity in the dermis layer, nutrients supplemented from blood are used to create cells in the basal layer. Furthermore, the dermis layer has a fibrous tissue that provides skin elasticity while retaining the skin. Subcutaneous tissue is thought to be formed mainly by subcutaneous fat. Therefore, it can be considered that glucose is mainly present in the dermis layer and almost no glucose is present in the epidermis layer or subcutaneous tissue.

  FIG. 8 shows a simulation model of the skin. In the figure, numbers 30, 31, 32 and 33 are the epidermis layer, dermis layer, subcutaneous tissue, muscle and bone, respectively. For example, near-infrared light irradiated on the skin from the first optical fiber 12 is propagated along the optical path indicated by “X” in FIG. 8 and received by the third optical fiber 20.

  When conducting light propagation simulation based on the Monte Carlo method, optical characteristic values such as absorption coefficient μα, scattering coefficient μS, refractive index n, and anisotropic scattering parameter g of the epidermis layer, dermis layer and subcutaneous tissue layer are required. It becomes. FIG. 9 shows the absorption coefficient μα of these layers. FIG. 10 shows the scattering coefficient μS of these layers. In general, the “g” of living tissue is a very strong forward scatter of 0.8-0.95. Therefore, “g” used in the present embodiment is set to be constant 0.9. Further, the refractive index n of these layers is also kept constant at 1.34. FIG. 11 shows the actually measured absorbance spectrum (A) and the absorbance spectrum (B) simulated by the Monte Carlo method under the conditions described above. In vivo parameters such as glucose, protein, lipid, moisture, and temperature are changed within a preset range so as to cover the daily fluctuation of the in vivo parameters, and the creation of the absorbance spectrum by simulation is repeated. Thus, since a plurality of simulated absorbance spectra are obtained, a plurality of differential absorbance spectra can be obtained as in the first embodiment. As a result, a difference data table can be obtained by simulation instead of measuring data from a living body.

  The advantages of using simulation in the present invention are briefly described below. In the simulation, the daily fluctuation range of the concentration in the target body component such as glucose is determined in advance. As shown in FIG. 5C, the difference value “D” is equally spaced around the origin “ORG” in the fluctuation range. Is simulated. In the case where data necessary for creating a calibration curve is measured from a living body, as shown in FIG. 5B, it is difficult to obtain difference values “D” at equal intervals within a short time. However, according to the simulation, it is possible to quickly obtain an optimum difference value for creating a calibration curve having a stable and reliable estimation accuracy without collecting a lot of data. Moreover, it is very difficult to determine whether or not the actually measured data includes a weak signal of glucose. However, according to the simulation, such a weak signal of glucose can be reliably reproduced. Furthermore, it is possible to prevent the influence of an error occurring between devices for measuring an absorbance spectrum from a living body and a noise component generated during measurement on a weak glucose signal. Therefore, there is no accidental correlation in the simulation. As an example, FIG. 12 shows changes in blood glucose level estimated using a measured blood glucose level of a subject and a calibration curve created by the method of this embodiment. In this case, the correlation coefficient r of both is 0.94.

Next, similarly to the first embodiment, a plurality of synthetic absorbance spectra are obtained from the differential absorbance spectrum obtained by the simulation and the second reference absorbance spectrum measured from the subject. The synthetic absorbance spectrum thus obtained contains information specific to the subject. The calibration curve of the second embodiment is obtained by performing multivariate analysis using the synthetic absorbance spectrum and the difference data table.
<Third Embodiment>
The method for creating a calibration curve of the present embodiment is substantially the same as the method of the first embodiment, except that the near-infrared absorbance spectrum used for creating the calibration curve is measured from simulated data instead of a living body. The same. Therefore, the overlapping description is omitted. In addition, an apparatus having the same configuration as that of the first embodiment can be used to perform quantitative analysis in this embodiment.

  In this embodiment, a suspended scatterer solution such as a fat emulsion (for example, “Intralipid” <Fresenius Mold Co., Ltd.) is used as a simulated sample. In particular, a solution containing 2 to 5% of the fat emulsion “Intralipid” is close to a living body. Various test solutions are prepared by changing the mixing ratio of body components such as glucose, protein, lipid, and water to the fat emulsion, and the solution temperature within a preset range so that their daily fluctuations are covered. . For each of the obtained test solutions, an absorbance spectrum is measured and one of the absorbance spectra is selected as a reference absorbance spectrum. As in the first embodiment, a plurality of differential absorbance spectra can be obtained by subtracting the reference absorbance spectrum from each of the measured absorbance spectra, and the concentration values of the in-vivo components in each test solution can be accurately determined. A difference data table can be created based on data measured from simulated data instead of a living body.

Next, in the same manner as in the first embodiment, a plurality of synthetic absorbance spectra are obtained from the differential absorbance spectrum obtained using the simulated sample and the second reference absorbance spectrum measured from the subject. The synthetic absorbance spectrum thus obtained contains information specific to the subject. The calibration curve of the third embodiment is obtained by performing multivariate analysis using the synthetic absorbance spectrum and the difference data table.
<Fourth embodiment>
The method for creating a calibration curve according to the present embodiment obtains an absorbance spectrum of a target body component (for example, glucose) by using the simulation described in the second embodiment, and other body parameters (for example, moisture, protein, and lipid). Concentration, temperature, and scattering) are characterized in that an absorbance spectrum is actually measured from a living body based on the same procedure as in the first embodiment under the condition that the concentration of a target body component is constant. Therefore, the overlapping description is omitted. In addition, an apparatus having the same configuration as that of the first embodiment can be used to perform quantitative analysis in this embodiment.

  For example, when preparing a calibration curve for glucose by multivariate analysis, it is preferable to take into account the absorbance spectrum of other body components other than glucose. In the living body, when the glucose concentration changes, the concentrations of other body components change simultaneously. Therefore, the absorbance spectra of other body components are useful for removing the influence of other body components from the glucose calibration curve. Further, as described in the second embodiment, the weak signal of glucose is reliably reproduced by simulation. Therefore, according to the present embodiment, it is possible to provide a calibration curve having further improvement in estimation accuracy.

  Moreover, when measuring an absorbance spectrum from another living body with respect to other in-vivo components, the living body is not limited to a subject that requires quantitative analysis. For example, at least one person other than the subject may be used. One of the measured absorbance spectra is selected as the reference absorbance spectrum, and a plurality of differential absorbance spectra are obtained in the same manner as in the first embodiment. At this time, since the concentration of the target body component is kept constant, there is no need to obtain the difference concentration.

  In the present embodiment, the difference data table is created from the difference absorbance spectrum obtained from the measurement data for other in-vivo components, the difference absorbance spectrum obtained by simulation for the target in-vivo components, and the difference concentration of the in-vivo components. Next, similarly to the second embodiment, each of the differential absorbance spectra obtained by the simulation is synthesized with the second reference absorbance spectrum measured from the subject to obtain a plurality of synthesized absorbance spectra. The synthetic absorbance spectrum thus obtained contains information specific to the subject. The calibration curve of the fourth embodiment is obtained by performing multivariate analysis using the synthetic absorbance spectrum and the difference data table.

  According to the present embodiment, since the calibration curve can be created taking into consideration the influence of other in-vivo components that cannot be taken into account in the simulation, the second effect brought about by the use of simulation such as reduction of noise components and improvement of stability of estimation accuracy is brought about. While ensuring the effect of the embodiment, it is possible to achieve further improvement in the estimation accuracy of the target body component concentration.

  In the first to fourth embodiments described above, the absorbance spectrum is obtained under at least two different conditions within the assumed range of the daily fluctuation of the absorbance spectrum with respect to in-vivo parameters other than the target in-vivo component (for example, glucose). Is preferred. For example, if the other in-vivo parameter is temperature, two temperatures of the reference value ± 1 ° C. can be selected. If the other in-vivo parameter is protein concentration, two types of protein concentrations of the reference value ± 10% can be selected. In this case, the differential absorbance spectrum changes according to the reference concentration value, but if the objective variable is the glucose concentration, no other in-vivo component concentration value is required in the multivariate analysis. As described above, a calibration curve may be created in consideration of changes in other in-vivo parameters such as temperature and protein concentration as necessary.

1 is a schematic view of an apparatus for non-invasively performing quantitative analysis of in-vivo components according to a first embodiment of the present invention. It is an end view of the measurement probe of the same apparatus. It is a block diagram which shows an example of the calculating part of the apparatus. It is a flowchart which shows the preparation method of the calibration curve of this invention. (A)-(c) is the schematic which shows the data for the calibration curve preparation of glucose (blood glucose level). It is a detailed flowchart of the preparation method of a calibration curve. It is a figure which shows an example of the calibration curve created by the method of this invention. It is sectional drawing of the human skin tissue by simulation. Figure 2 is a graph showing the relationship between wavelength and absorption coefficient in different layers of human skin. Figure 2 is a graph showing the relationship between wavelength and scattering coefficient in different layers of human skin. It is a figure which shows the coincidence degree of the measured absorbance spectrum and the absorbance spectrum by simulation. It is a graph which shows the time change of a test subject's measured blood glucose level and an estimated blood glucose level.

Claims (9)

  A plurality of differential absorbance spectra that are differences between a plurality of near-infrared absorbance spectra and a reference absorbance spectrum of a living body are obtained, and a second reference absorbance spectrum different from the reference absorbance spectrum is synthesized with each of the differential absorbance spectra. A method for preparing a calibration curve for quantitative analysis of in-vivo components, wherein a plurality of synthetic absorbance spectra are obtained and a calibration curve is created by multivariate analysis using the plurality of synthetic absorbance spectra.   The method according to claim 1, wherein the in-vivo component is glucose.   2. The method according to claim 1, wherein each of the near-infrared absorbance spectra is measured by irradiating a living body with near-infrared light and performing spectral analysis of radiation emitted from the living body.   The method according to claim 1, wherein the plurality of differential absorbance spectra are obtained based on a light propagation simulation defined as a method of analyzing light propagation in a modeled living body.   The method of claim 1, wherein the reference absorbance spectrum is selected from the plurality of near infrared absorbance spectra.   Measuring a plurality of near-infrared absorbance spectra at the time of measurement of the in-vivo component concentration of the living body, and determining a plurality of difference concentrations, each of which is a difference between the in-vivo component concentration and a reference concentration, The method according to claim 3, wherein the calibration curve is created by multivariate analysis using a difference data table including the difference absorbance spectrum and the difference concentration.   The plurality of near-infrared absorbance spectra are measured over a plurality of days, and the reference absorbance spectrum is selected from a plurality of near-infrared absorbance spectra measured on each of the plurality of days. The method of claim 3.   The near-infrared absorbance spectrum of the subject is measured, and the concentration of the in-vivo component of the subject is determined using the calibration curve created by the method according to claim 1 and the near-infrared absorbance spectrum of the subject. A method for noninvasively determining a characteristic concentration of a body component of a subject.   A light irradiating means for irradiating a subject's skin with near-infrared light, a light receiving means for receiving radiated light from the skin, and a memory for storing a calibration curve created by the method according to claim 1. And a non-invasive means for calculating the in-vivo component concentration of the subject using the output of the light-receiving means and a calculation means for calculating the in-vivo component concentration of the subject using the calibration curve read from the memory Quantitative analyzer to determine

Images