JP2010051589A - Non-invasive human skin melanin measuring method, and instrument therefor - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a new non-invasive human skin melanin measuring method for correctly quantitating human skin melanin without performing biopsy, and observing and displaying the distribution, and to provide an instrument for the method. <P>SOLUTION: Data group, where diffuse reflection spectra in three-dimensional model skins different in produced melanin amount is made correspond to a chemically quantitated melanin amount, is obtained. The calibration curve of the melanin amount is obtained concerning a score obtained by performing main component analysis with respect to the data group. The score is calculated, based on the diffuse reflection spectra of an object to be measured, and applied to the calibration curve, thereby calculating the melanin amount of the object to be measured. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a non-invasive human skin melanin measurement method and apparatus.

  As for the light absorption characteristics of melanin in the visible-near red region, it is known that a monotonically decreasing spectrum is shown as the wavelength increases, and that the spectral intensity increases in the entire wavelength region as the amount of melanin increases. In the conventional technique, the absorbance spectrum intensity distribution at a certain wavelength (or wavelength band) is used as the melanin distribution by regarding the absorbance spectrum intensity as the amount of melanin. Alternatively, the melanin distribution has an intensity distribution of color coordinate parameters calculated from the diffuse reflection full spectrum in the entire visible-near red region or those represented by the light intensity measured in the wavelength band. However, these methods are intended to qualitatively observe the melanin distribution state, and not to measure the melanin distribution quantitatively.

  In addition, a method of estimating the amount of melanin by decomposing a diffuse reflection spectrum measured from the skin is also known (Patent Document 1). This method assumes that the absorption or absorbance spectrum of the pigment itself containing melanin is known. However, although the absorption or absorbance spectrum of synthetic melanin is known, it is not yet known what the true absorption or absorbance spectrum of the pigment itself containing melanin produced in the human body is. could not.

For this reason, when trying to accurately quantify human skin melanin using the light spectrum, biopsy (living a living body) was inevitable. However, biopsy is not preferable because it imposes a burden on the human body.
International Publication WO2005 / 079661 Pamphlet

  Accordingly, an object of the present invention is to provide a novel non-invasive human skin melanin measurement method and apparatus capable of accurately quantifying human skin melanin and observing and displaying the distribution without performing biopsy. And

The non-invasive human skin melanin measurement method of the present invention includes a first step of measuring a diffuse reflection spectrum in a three-dimensional model skin having different amounts of produced melanin to obtain a spatially averaged diffuse reflection spectrum;
A second step of chemically quantifying the amount of melanin in the three-dimensional model skin;
A third step of performing a principal component analysis on the diffuse reflection spectrum obtained in the first step to obtain a loading vector and a score;
Expression of the relationship between the score obtained in the third step and the amount of melanin determined in the second step

(Where S is a score calculated from the diffuse reflectance spectrum data group, M is the amount of melanin determined by a chemical method) and a fourth step for determining a calibration curve;
A fifth step of calculating a score by projecting the diffuse reflection spectrum of the measurement object onto the loading vector obtained in the third step;
And a sixth step of calculating the melanin amount of the measurement object by applying the score obtained in the fifth step to the calibration curve obtained in the fourth step.

  The wavelength range of the diffuse reflection spectrum is 620 nm to 720 nm.

A first step of measuring a diffuse reflection spectrum in a three-dimensional model skin having different amounts of produced melanin to obtain a spatially averaged diffuse reflection spectrum;
A second step of chemically quantifying the amount of melanin in the three-dimensional model skin;
Formula from the diffuse reflectance spectrum obtained in the first step

_{(Wherein, R 1, 2, ..., n} is the diffuse reflection of light, _{T i} is the diffusion transmittance of the i layer) third identifying _x i of each wavelength using the _{y i} spectral intensity When,
A fourth step for obtaining a calibration curve from the relationship between the x _i , y _i spectral intensity of each wavelength identified in the third step and the amount of melanin quantified in the second step;
A fifth step of calculating x _i and y _i spectral intensities from the diffuse reflection spectrum of the measurement object;
And a sixth step of calculating the melanin amount of the measurement object by applying the x _i and y _i spectral intensities obtained in the fifth step to the calibration curve obtained in the fourth step.

  A non-invasive human skin melanin measuring apparatus according to the present invention includes a white light source that irradiates a measurement target with white light, a spectroscope that splits light diffusely reflected by the measurement target, and a diffusion that is spectrally dispersed by the spectroscope. A detector for detecting the reflection spectrum, a data processing device to which data of the diffuse reflection spectrum detected by the detector is input, and white light from the white light source is linearly polarized and perpendicular to the polarization plane of the white light. And a polarizing plate for allowing only the linearly polarized light component to enter the spectroscope.

  In addition, the data processing device receives data on the amount of melanin in the three-dimensional model skin, and the data processing device can calculate the amount of melanin in the measurement object according to the non-invasive human skin melanin measurement method of the present invention. It is characterized by being configured.

  In addition, the detector has an automatic focusing function capable of always focusing on the measurement central portion.

  It also has a microscopic optical system function.

  Without performing biopsy, it is possible to accurately quantify human skin melanin and observe and display its distribution.

  Hereinafter, preferred embodiments of the present invention will be described.

  First, an embodiment of a non-invasive human skin melanin measuring apparatus according to the present invention will be described with reference to FIGS. In FIG. 1, 1 is a stage on which the measurement object S is placed, and 2 is a white light source. A spectroscope 4 having a slit 3 is provided above the stage. The spectrometer 4 is an imaging spectrometer equipped with a transmission type grating. The light reflected from one line of the measurement object passes through the slit 3 and is split by the spectroscope to form an image on the light receiving surface of the CCD camera 5 which is a detector. That is, the X axis of the light receiving surface of the CCD camera 5 corresponds to the position on one line of the measurement object, and the Y axis direction is the spectrum of the dispersed light.

  FIG. 2 shows the detailed structure of the spectrometer 4. The slit 3 includes a slit body 3a and a lens 3b for condensing light. Further, it is composed of a transmissive grating type prism 4b between two lenses 4a and 4c. The CCD camera 5 uses an EM (Electron Multiplying) CCD camera, a CCD camera equipped with a photomultiplier tube, or the like to increase the sensitivity so that even weak light can be felt.

  Since the configuration of the optical part of the apparatus is as described above, diffuse reflection spectrum data of one line of the measuring object S can be acquired with one frame of the CCD camera. This data is input to the data processing device 6. Subsequently, the stage is moved by a minute length to acquire the next one-line diffuse reflection spectrum data in the next frame of the CCD camera and send it to the data processor. By repeating this operation, diffuse reflection spectrum data of a two-dimensional surface can be acquired. Actually, a mechanism for sweeping in a direction perpendicular to one line on the surface of the measurement object corresponding to the X axis, for example, a CCD camera in synchronism with the operation while the stage is moved almost continuously by the adjusting means 7. In step 5, data is acquired.

  Although not shown, the apparatus of this embodiment includes a set of polarizing plates. By this polarizing plate, the light from the white light source 2 is linearly polarized, and only the linearly polarized light component perpendicular to the polarization plane of the light from the linearly polarized white light source 2 is incident on the spectrometer 4. It has become. Thereby, the influence of the irregular reflection which arises on the surface of the measuring object S is suppressed. The directions of the two linearly polarized lights can be arbitrarily set.

  Although not shown, the apparatus of the present embodiment has an automatic focus (AF) function that can always focus on the measurement center. Thereby, the influence of the shadow resulting from the unevenness | corrugation with comparatively big spatial frequency of the surface of the measurement target S can be suppressed now.

  Further, although not shown, the apparatus of the present embodiment has a microscopic optical system function that is excellent in observation of a boundary region between a pigment lesion and a normal part.

  This apparatus has a measurement screen vertical dimension determined by the optical slit length of the mounted slit 3 and the optical system magnification of the spectroscope 4, a measurement screen determined by the optical slit width of the slit 3 and the drive software setting of the adjusting means 7. The horizontal dimension, one pixel dimension of the CCD camera 5, one pixel dimension determined by the optical slit width of the slit 3 and the optical system magnification of the spectroscope 4 are used as the basic length scale of the two-dimensional image for each pixel in the two-dimensional image. A diffuse reflection spectrum within a wavelength range determined by the characteristics of the spectroscope 4 can be stored. This apparatus can acquire position information and diffuse reflection spectrum information in a short time by sweeping, ie, line scanning, in the direction perpendicular to the longitudinal direction of the optical slit. The analysis of the acquired diffuse reflection spectrum of the measurement object enables drawing of a two-dimensional spectrum image. Therefore, according to this apparatus, if there is a qualitative / quantitative difference in the diffuse reflection spectrum, such an optically different area can be highlighted. If each element value of the three primary colors of color is calculated from the diffuse reflection spectrum and rendered based on the calculated value, a pseudo color image comparable to a color photograph can be reconstructed.

  Next, the noninvasive human skin melanin measuring method according to the present invention will be described. The noninvasive human skin melanin measurement method according to the present invention is based on a new concept of nonclinical simulation. That is, a three-dimensional model skin for the epidermis composed of a stratum corneum, a granule layer, a spiny layer, and a basal layer containing human-derived melanocytes is used as a simulator for the epidermis in which melanin is produced and diffused. The diffuse reflectance spectrum of the three-dimensional model skin cultured for 2 weeks under UVB irradiation under a certain condition is measured, and at the same time, the amount of melanin actually produced is quantified by a chemical method. This makes it possible to have a 1: 1 correspondence between various parameter values extracted based on the measured average diffuse reflection spectrum or a mathematical model proposed from them (described later) and the actual amount of melanin. Furthermore, the mathematical model can relate the diffuse reflectance spectrum measured on the three-dimensional model skin and the actual skin or extracted parameters, so the melanin amount can be calculated from the diffuse reflectance spectrum measured on the actual human skin. It can be estimated. The estimated value is more reliable than the conventional method using a spectrum of a synthetic melanin polymer whose relationship with a melanin polymer synthesized in vivo is unknown. The image finally drawn based on the diffuse reflection spectrum measured from the human skin gives a quantitative two-dimensional melanin distribution map. Hereinafter, the mathematical models I and II will be described in detail.

[Mathematical model I]
Principal component analysis is essentially a statistical method, an unsupervised multivariate analysis that derives independent components from the obtained data group, and is currently often used as a quantitative method using spectra. It has been. The original formula is as follows.

  Here, A is an N × M matrix that is N data groups measured at M wavelengths. T is called the score of the N × M matrix, and P is called the loading vector of the M × M matrix.

  When the principal component analysis is performed, as many loading vectors and scores as the number M of wavelengths constituting the spectrum are obtained. This loading vector is regarded as an axis constituting an M-th order multidimensional space, and the score corresponds to a coordinate on the axis. When the data group A is constituted by an absorption spectrum, it is considered from Lambert-Beer rule that the loading vector corresponds to the absorption spectrum represented by one of the components constituting the measurement object, and the score corresponds to the amount of the component.

  When the data group A is constituted by a diffuse reflection spectrum, it is considered that the loading vector corresponds to the reflectance from each interface and the score corresponds to the component amount by grasping the measurement target as a multilayer. However, in the diffuse reflection measurement, the specular reflection light on the target surface is strong, so it is difficult to correctly obtain a loading vector corresponding to the diffuse reflection spectrum of melanin. In order to solve this, introduction of a polarizing plate is effective.

  In the present invention, using the above-described apparatus, first, a diffuse reflection spectrum is measured in a three-dimensional model skin having different amounts of produced melanin, and a spatially averaged diffuse reflection spectrum is obtained. On the other hand, melanin quantification is performed by a chemical method using a chemical degradation method. At this stage, an average diffuse reflectance spectrum corresponding 1: 1 with the chemically quantified amount of melanin is obtained. A principal component analysis is performed on the data group to obtain a loading vector and a score corresponding to the diffuse reflection spectrum of melanin.

  Specifically, the loading vector and the score are obtained as follows. An eigenvector obtained by obtaining a covariance matrix with an expected value of 0 for the data group A represented by an N × M matrix and solving the eigenvalue problem with respect to this is the loading vector P of all components (M).

Since the loading vector calculated here is orthogonalized, it is considered to represent an axis in the M-dimensional space. A score T _{j, i} is obtained by projecting the i-th A _i of N data onto the loading vector P _j of the j-th principal component.

  Next, a relationship between the score and the melanin amount, that is, a calibration curve is obtained. As a result of the examination, it is known that the following equation is best fit with the data.

  Here, S is a score calculated from the diffuse reflectance spectrum data group, and M is the amount of melanin obtained by a chemical method.

  By projecting the diffuse reflection spectrum of the human skin, which is the measurement object, onto the loading vector obtained for the melanin using the same apparatus, the score amount can be easily calculated. When this score amount is applied to the above-mentioned calibration curve, the melanin amount can be calculated. As a result, a melanin distribution image of human skin can be drawn.

  The structure of human skin is from the surface layer to the epidermis, dermis, and subcutaneous tissue, and a vascular network exists behind the dermis tissue. Therefore, in the diffuse reflection spectrum measured from human skin, the melanin spectrum and the hemoglobin spectrum interfere with each other. This method can suppress the influence of the hemoglobin spectrum by limiting the wavelength range. For example, a wavelength range of 620 nm to 720 nm is used. Even if it does in this way, it is thought that the influence of spectrum interference cannot be removed completely, but it is an extremely useful method because of its simplicity.

[Mathematical model II]
According to this method, the interference between the melanin spectrum and the hemoglobin spectrum in the diffuse reflection spectrum measured from human skin can be removed 100% in principle.

Kubelka and Munk have shown that the essence of color appearance, or diffuse reflection, is the absorption and scattering of light by matter (P. Kubelka and F. Munk, Z. tech. Phys. 12, p593 (1931)). The theory that considers multiple reflection of light considering a multilayer model is formulated by Kubelka. In the n-layer model, if the absorption coefficient of the i-th layer is K _i and the scattering coefficient is S _i , the total return light in all layers, that is, the diffuse reflectance R _{1, 2,.} (1) (P. Kubelka, Journal of the Optical Society of America 38, 5: pp.448-457, 1948, ibid. 44, 4: pp.330-335, 1954).

T _i represents the diffuse transmittance of the i-th layer. The thickness of the i-th layer is d _i ,

Then, equation (1) can be rewritten as equation (3).

If 2n unknown variables x _i and y _i included in the equation (3) are known, a theoretical diffuse reflectance in a multilayer model of n layers can be obtained. Conversely, theoretically, it is possible to back-calculate 2n unknown variables x _i and y _i from the diffuse reflectance under appropriate restraint conditions.

In actual human skin, it is almost impossible to individually measure variables in the epidermis and dermis. Therefore, a technique for finding a constraint condition necessary for identifying 2n unknown variables x _i and y _i from the diffuse reflection spectrum obtained by experiment using Equation (3) is a known synthetic melanin spectrum. And you have to rely on the hemoglobin spectrum. This method is based on an unprecedented idea of using a wavelength-dependent value obtained in the three-dimensional model skin as an initial value of the variables x _i and y _i in the human epidermis as a constraint condition.

Various cultured cells such as human three-dimensional model skin are generally very thin, and when the reflection spectrum is measured, the light irradiated to the cultured cells is transmitted through the cells, and the reflection and absorption of the material placed thereunder Strongly influenced by. Therefore, it is desirable to add the following devices in this method. A reference white plate is placed under a quartz glass petri dish containing the three-dimensional model skin. For this arrangement, ignoring a quartz glass petri dish that is transparent to visible near-red light, a multilayer model in which the three-dimensional model skin is the first layer and the reference white plate placed below is the second layer Considering the above, variables x _i and y _i (i = 1) specific to the three-dimensional model skin are obtained. In this model, T ₂ = 0 and R ₂ = C (constant value) can be set in the equation (3), and the observed reflectance R is determined by the equation (3) when the multiple scattering effect in the first layer is not considered. 4), and when considered, it can be expressed as in equation (5).

In equation (4), there are two unknown variables for each wavelength, x ₁ and y ₁ . x ₁ and y ₁ are variables related to the extinction coefficient / scattering coefficient as shown in Expression (2), and are variables inherent to the three-dimensional model skin. Such x ₁ and y ₁ are determined so as to satisfy Expression (5). Then, _{x 1, y} 1 according to Equation (6) is obtained.

Where R _exp is the diffuse reflectance of the three-dimensional model skin obtained by measurement, and λ means that the sum is taken over the wavelength range of the measured spectrum. For example, the Gauss-Newton method (nonlinear least square method) can be used for optimization of the unknown variable.

Since these calculations do not include position information, x ₁ and y ₁ spectra are calculated individually for diffuse reflection spectra at all points included in the measurement target (the pixel dimensions of the CCD camera used for measurement are determined). can do. Therefore, a two-dimensional distribution of x ₁ and y ₁ at each wavelength can be drawn. This means that it is possible to draw a two-dimensional distribution of melanin. The specific method is described below.

As described in the mathematical model I, a diffuse reflection spectrum is measured in a three-dimensional model skin with different amounts of produced melanin, and a spatially averaged diffuse reflection spectrum is obtained. On the other hand, melanin quantification is performed by a chemical method using a chemical decomposition method. Next, x ₁ and y ₁ spectra are identified from the average diffuse reflection spectrum. Thereafter, for example, if a relationship between the x ₁ and y ₁ spectral intensities of the respective wavelengths and the amount of melanin is obtained, a calibration curve can be obtained. If this is used, the amount of melanin can be estimated from the x ₁ and y ₁ spectra.

  Next, a method for estimating the amount of melanin in human skin will be described. As already described, the structure of human skin is from the surface layer to the epidermis, dermis, and subcutaneous tissue, and a vascular network exists behind the dermis tissue. Therefore, in the diffuse reflection spectrum, the melanin spectrum and the hemoglobin spectrum interfere with each other.

Since this method can be developed in multiple layers as shown in Equation (3), it is possible to derive a theoretical equation for a model that reflects the physical structure of the skin. The simplest model is a two-layer model. There, we consider a skin layer with a finite thickness and a dermis layer simplified with infinite depth. When the theoretical formula is derived based on the model, the necessary parameters are only the x ₁ and y ₁ spectra in the epidermis layer and the x ₂ spectrum in the dermis layer. Appropriate constraints are required to identify the x ₁ and y ₁ spectra of the epidermis and the x ₂ spectrum of the dermis so that the actually measured diffuse reflectance spectrum can be reproduced. As a constraint, the spectrum obtained from the three-dimensional model skin as the x ₁ and y ₁ spectra is set as an initial value. For the x ₂ spectrum, for example, a hemoglobin spectrum in a 100% oxidation state that can be used by anyone is initially set. An initial value setting condition of setting as a value can be used. By doing so, the convergence of equation (4) can be accelerated, and the x ₁ and y ₁ spectra of the epidermis are dominated by melanin and the x ₂ spectrum of the dermis is hemoglobin so as to prevent misconvergence. It is possible to derive physically meaningful parameters that are governed by. When the x ₁ and y ₁ spectra of the identified epidermis layer are converted into melanin amounts using the above-mentioned calibration curve, the melanin amount distribution of human skin can be drawn.

From x ₂ spectrum identified dermis layer, it is also, it is possible to calculate the oxygen saturation in a manner conventionally performed for calculating the relative value of the total amount of hemoglobin. It is also possible to consider a multilayer model such as a three-layer structure model. In this case, it is possible to identify the spectrum caused by hemoglobin separately for the dermis layer and subcutaneous tissue. Mathematical model II can estimate the amount of melanin more precisely than mathematical model I.

  According to the present invention, the distribution of melanin in human skin can be measured in a non-contact and non-invasive manner, which greatly contributes to the diagnosis of pigmented lesions such as melanoma.

  In addition, this invention is not limited to the said Example, A various deformation | transformation implementation is possible.

  The following specific examples further illustrate the present invention.

[Skin model culture]
As a skin model, MEL-300A manufactured by MatTek Corporation was used.

First, tissue transplantation was performed by removing the insert from the transport medium and transferring it to a 6-well plate containing 0.9 ml of EPI-100MM preheated to 37 ° C. The plate with the insert was placed in an incubator (37 ° C., 5% CO ₂ ) and equilibrated for 1 hour to perform pre-equilibration.

  Next, the plate containing the treated tissue was returned to the incubator and cultured for 14 days. Then, during the culture period of 14 days, the medium exchange operation was performed seven times.

  The medium exchange was performed according to the following procedure. The medium in the plate was removed by aspiration, sterilized stainless steel washers were stacked in each well in two stages, and the insert was placed on it. Thereafter, 3.5 ml of preheated fresh medium was added to each well. In order to remove the excess kojic acid added last time, the inside and outside of the insert were washed with a PBS washing solution.

  The above culture was carried out by adding kojic acid, which is known to have a melanin production inhibitory effect, at various concentrations, thereby obtaining skin models having various amounts of produced melanin.

[Melanin determination by chemical method]
In Example 1, the cultured skin tissue was removed from the cup, placed in a 1.5 ml tube, freeze-dried, and the dry skin tissue was weighed. Then, 1.0 ml of ultrapure water (miliQ-water) was added to the tissue and homogenized using a glass homogenizer. Thereafter, the homogenized tissue was subjected to ultrasonic treatment using an ultrasonic device (Handy sonic model UR-20P). Samples were dispensed 100 μl into screw tubes.

  Then, quantification was performed for eumelanin and pheomelanin by the following procedure.

[Quantification of Eumelanin]
PTCA (pyrrole-2,3,5-tricarboxylic acid) was quantified, and eumelanin was quantified using this as an index.

First, 800 μl of 1M-H ₂ SO ₄ was added to 100 μl of tissue lysate. Then, 100 μl of mouse liver homogenizing solution (500 mg / 10 ml) was added and shaken for 10 minutes. During shaking, 10 μl of 3% KMnO ₄ was added. Also, 10 μl of 3% KMnO ₄ was added again when the purple color of KMnO ₄ disappeared while shaking. Then 100 μl of 10% Na ₂ SO ₃ was added.

  Next, about 7 ml of ether (alumina-treated) was added to the reaction solution, shaken, and the ether fraction was transferred to a test tube with a rim. Then, about 7 ml of ether was added again to the aqueous fraction, shaken, and about 7 ml of ether was added again to the aqueous fraction, and the ether fraction was transferred to the rim-equipped test tube.

  Thereafter, the ether fraction was concentrated to dryness under reduced pressure using a test tube concentrator (TC-10D), and then completely concentrated using a vacuum pump.

  Then, 200 μl of ultrapure water (miliQ-water) was added to the residue and mixed by vortexing. The solution was transferred to a 1.5 ml tube and centrifuged (10000 rpm, 1 minute).

  The supernatant was transferred to an HPLC tube and HPLC (detector: JASCA 875-UV, pump: JASCA PU-980, sampler: JASCA AS-2057plus, integrator: JASCA 807-IT, column: JASCA 860-Co, buffer: 0 PTCA was quantified by 1M KPB (pH 2.10): MeOH = 99: 1). The value obtained by multiplying the PTCA amount by 160 was defined as the eumelanin amount (K. Wakamatsu and S. Ito, Pigment Cell Res 15: pp.174-183, 2002).

[Quantification of pheomelanin]
4-AHP (4-amino-3-hydroxyphenylalanine) was quantified, and pheomelanin was quantified using this as an index.

First, 30 μl of 30% H ₃ PO ₄ was added to 100 μl of tissue lysate. Then, 500 μl of 57% HI was added and reacted at 130 ° C. for 20 hours in an oil bath. After allowing to cool, 100 μl of the reaction solution was placed in another test tube.

  Next, the reaction solution was dried using a vacuum pump, 200 μl of 0.1 M HCl was added to the residue, and mixed by vortexing. Then, this was transferred to a 1.5 ml tube and centrifuged (10000 rpm, 1 minute).

  The supernatant was transferred to an HPLC tube, and HPLC (detector: JASCA 2020plus, pump: JASCA 880-PU, sampler: JASCA AS-950-10, integrator: JASCA 807-IT, column: JASCA catechol pack, buffer: 0. 4-AHP was quantified with 1M citrate buffer (pH 3.00): MeOH = 96: 4). A value obtained by multiplying the 4-AHP amount by 9 was defined as the pheomelanin amount (K. Wakamatsu and S. Ito, Pigment Cell Res 15: pp.174-183, 2002).

  Table 1 shows the quantitative results of eumelanin (EM) and pheomelanin (PM).

[Fit to mathematical model I]
About the skin tissue having various amounts of produced melanin obtained in Example 1, after washing the inside and outside of the insert with PBS washing solution, respectively, transferred to a quartz glass petri dish, and measured the diffuse reflection spectrum using the above-described apparatus, A spatially averaged diffuse reflectance spectrum was determined. The principal component analysis of the mathematical model I described above was performed on this data group, and a loading vector and a score corresponding to the diffuse reflection spectrum of melanin were obtained. The relationship between this score and the amount of melanin obtained in Example 2 was determined. The results are shown in FIGS. FIG. 3 shows the results for pheomelanin, and FIG. 4 shows the results for eumelanin.

3 and 4 are applied to the following formulas described above, and the coefficients a and b are obtained by the method of least squares. When a = 0.0899 and b = 0.0432 in FIG. The square R ^{2 of the} relationship number is 0.913, and in FIG. 4, when a = 0.0776 and b = 0.0271, the square R ^{2 of the} correlation coefficient is 0.816. From this, it was confirmed that the loading vector prepared this time sufficiently corresponds to the diffuse reflection spectrum of melanin.

  Here, S (y in FIGS. 3 and 4) is a score calculated from the diffuse reflectance spectrum data group measured in this example, and M (x in FIGS. 3 and 4) is obtained by the chemical method of Example 2. The amount of melanin.

  The distribution of femelanin and eumelanin determined from the first score using the above formula is shown in FIGS.

[Fit to mathematical model II]
The skin tissues having various amounts of produced melanin obtained in Example 1 were respectively washed with PBS washing solution inside and outside the insert, and then transferred to a quartz glass petri dish, and a reference white plate was placed under this. And the diffuse reflection spectrum was measured using the apparatus mentioned above, and the spatially averaged diffuse reflection spectrum was obtained. When the mathematical model II method described above is applied to the diffuse reflection spectrum of the three-dimensional model skin, as shown in FIGS. 7 and 8, both x ₁ and y ₁ monotonously decrease as the wavelength increases. I understood. Since the human three-dimensional model skin has a structure with a substantially constant thickness, it is presumed from the above equation (2) that both the absorption coefficient and the scattering coefficient are monotonously decreased. This is consistent with conventional knowledge. In addition, the thickness of the three-dimensional model skin was measured with an optical microscope, and the absorption coefficient / scattering coefficient was estimated from equation (2). As a result, the papillary dermis (not including blood) in actual human skin was in vitro. It almost coincided with the identified absorption coefficient and scattering coefficient (RR Anderson and JA Parrish, The Journal of Investigative Dermatology 77: pp.13-19, 1981).

7 and 8 show the relationship between the pheomelanin concentration and the x _i and y _i spectra, and it was demonstrated that the amount of melanin can be estimated by using this.

It is the schematic of the measuring device by this invention. It is a block diagram of the spectrometer mounted in the measuring apparatus by this invention. It is a graph which shows the relationship between the score corresponding to the diffuse reflection spectrum of pheomelanin, and the amount of pheomelanin. It is a graph which shows the relationship between the score corresponding to the diffuse reflection spectrum of eumelanin, and the amount of eumelanins. It is an image which shows distribution of pheomelanin. It is an image which shows distribution of eumelanin. Is a graph showing the relationship between the phaeomelanin concentration and x _i spectrum. It is a graph which shows the relationship between a pheomelanin density | concentration and _yi spectrum.

Explanation of symbols

2 White light source 4 Spectrometer 5 CCD camera (detector)
6 Data processing device

Claims (8)

A first step of measuring a diffuse reflection spectrum in a three-dimensional model skin having different amounts of produced melanin to obtain a spatially averaged diffuse reflection spectrum;
A second step of chemically quantifying the amount of melanin in the three-dimensional model skin;
A third step of performing a principal component analysis on the diffuse reflection spectrum obtained in the first step to obtain a loading vector and a score;
Expression of the relationship between the score obtained in the third step and the amount of melanin determined in the second step
(Where S is a score calculated from the diffuse reflectance spectrum data group, M is the amount of melanin determined by a chemical method) and a fourth step for determining a calibration curve;
A fifth step of calculating a score by projecting the diffuse reflection spectrum of the measurement object onto the loading vector obtained in the third step;
A non-invasive human skin melanin measurement method comprising: a sixth step of calculating the melanin amount of the measurement object by applying the score obtained in the fifth step to the calibration curve obtained in the fourth step . The noninvasive human skin melanin measuring method according to claim 1, wherein the wavelength range of the diffuse reflection spectrum is 620 nm to 720 nm. A first step of measuring a diffuse reflection spectrum in a three-dimensional model skin having different amounts of produced melanin to obtain a spatially averaged diffuse reflection spectrum;
A second step of chemically quantifying the amount of melanin in the three-dimensional model skin;
Formula from the diffuse reflectance spectrum obtained in the first step
_{(Wherein, R 1, 2, ..., n} is the diffuse reflection of light, _{T i} is the diffusion transmittance of the i layer) third identifying _x i of each wavelength using the _{y i} spectral intensity When,
A fourth step for obtaining a calibration curve from the relationship between the x _i , y _i spectral intensity of each wavelength identified in the third step and the amount of melanin quantified in the second step;
A fifth step of calculating x _i and y _i spectral intensities from the diffuse reflection spectrum of the measurement object;
And a sixth step of calculating the melanin amount of the measurement object by applying the x _i and y _i spectral intensities obtained in the fifth step to the calibration curve obtained in the fourth step. Invasive human skin melanin measurement method. A white light source that irradiates a measurement object with white light, a spectroscope that divides the light diffusely reflected by the measurement object, a detector that detects a diffuse reflection spectrum that is dispersed by the spectroscope, and the detector A data processing device to which data of the diffuse reflection spectrum detected by is input, and polarized light that linearly polarizes white light from the white light source and causes only the linearly polarized light component perpendicular to the polarization plane of the white light to enter the spectroscope. A non-invasive human skin melanin measuring device comprising a plate. The data processing device receives data on the melanin amount of the three-dimensional model skin, and the data processing device can calculate the melanin amount of the measurement object according to the non-invasive human skin melanin measurement method according to claim 1. The noninvasive human skin melanin measuring apparatus according to claim 4, wherein the noninvasive human skin melanin measuring apparatus is configured. The data processing device receives data on the amount of melanin in the three-dimensional model skin, and the data processing device can calculate the amount of melanin in the measurement object according to the noninvasive human skin melanin measurement method according to claim 3. The noninvasive human skin melanin measuring apparatus according to claim 4, wherein the noninvasive human skin melanin measuring apparatus is configured. The non-invasive human skin melanin measuring apparatus according to any one of claims 4 to 6, wherein the detector has an automatic focusing function capable of always focusing on a measurement central portion. The noninvasive human skin melanin measuring apparatus according to any one of claims 4 to 6, which has a microscopic optical system function.