JP4533603B2 - How to measure skin condition - Google Patents
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Description
本発明は、皮膚状態の鑑別法及び該鑑別法による鑑別結果を用いた皮膚状態のモニタリング方法に関する。 The present invention relates to a skin condition discrimination method and a skin condition monitoring method using a discrimination result by the discrimination method.
美しい皮膚でありたいと願うのは、女性のみならず万人が思うところであり、この為、化粧料などを使用して皮膚の状態を好ましく保つ努力を多くの人がしていると言える。この皮膚の状態は、個人個人により大きく異なるものであり、又、年を取るなどにつれ、弾力を消失し、しわなどが増えてくるなど経時変化もする事項である。この様な変化は、皮膚の生理変化を反映したものであり、皮膚を美しく保つためには、皮膚の状態を客観的に、且つ、適切に知る必要があると言える。主観的には、皮膚の状態は、その人が見た目で大凡判断されるが、例えば、今使用している化粧料が効果を奏しているか否か、或いは、肌にあったものであるか否かなどの判断を、皮膚状態から適切に行うためには、皮膚状態を定量化出来る程度に細かく判別する必要が存する。この様な必要性から、皮膚状態を客観的に、且つ、適切に鑑別する手だての開発が試みられてきた。この様な試みとしては、例えば、デジタルカメラなどで皮膚の様子を画像として取り込み、これの輝度分布などを操作して数値の集合体に変換し、指標とする方法（例えば、特許文献１、特許文献２、特許文献３を参照）などや角層細胞の形状から皮膚状態を推測する方法（例えば、特許文献４、特許文献５を参照）などが、又、皮膚の粘弾特性については、皮膚を機械的につまみ、これによる変形と戻りを数値化する方法（例えば、特許文献６を参照）などが存するが、これらにおいては画像の取り込み、選択、解析などが全て人の手でバッチ処理する必要があったり、顕微鏡標本の作成やその観察に経験や習熟を要するなどの障壁が存するため、誰もが容易に行えるものとは言い難かった。或いは、被験者に負担を強いるものもあり、これまで、シワや皮膚粘弾性については、判定基準を元に目視判定し、スコア化する方法が最も一般的であることは否定出来ない。
The desire for beautiful skin is not only for women but also for everyone, so it can be said that many people are making efforts to keep skin condition favorable by using cosmetics. This skin condition varies greatly from individual to individual, and is a matter that changes with time, such as loss of elasticity and wrinkles, etc., increase with age. Such a change reflects physiological changes of the skin, and it can be said that it is necessary to objectively and appropriately know the state of the skin in order to keep the skin beautiful. Subjectively, the condition of the skin is roughly judged by the person's appearance. For example, whether or not the cosmetics currently used are effective, or whether or not they are on the skin. In order to appropriately determine whether or not the skin condition is appropriate, it is necessary to determine the skin condition so finely that the skin condition can be quantified. From such a need, development of a technique for objectively and appropriately distinguishing the skin condition has been attempted. As such an attempt, for example, a method of capturing the state of the skin as an image with a digital camera or the like, and manipulating the luminance distribution or the like to convert it into a set of numerical values and using it as an index (for example,
一方、近赤外スペクトルを用いた分析において、皮膚の近赤外スペクトルを測定して、血中のグルコース量を定量する技術（例えば、特許文献７、特許文献８を参照）や皮膚の近赤外スペクトルを測定して、水に由来するピークを抽出し、皮膚内の水分量を定量する技術（例えば、特許文献９を参照）、皮膚の近赤外スペクトルより皮下脂肪量を定量する方法（例えば、特許文献１０を参照）或いは創傷の複数のスペクトルチャンネル像から創傷の重篤度を推定する方法に於いて、該複数のスペクトルチャンネル像の一つに近赤外スペクトルのイメージ像を用いる方法（例えば特許文献１１を参照）などが存するが、皮膚性状の鑑別法であって、予め状態の異なる２種以上の皮膚の近赤外吸収スペクトルを測定し、前記近赤外吸収スペクトルと、皮膚性状の示性値とを多変量解析し、該分析結果を指標として、これと試験試料である皮膚の近赤外吸収スペクトルとを比較し、該試験試料の皮膚性状を鑑別する技術は全く知られていない。更に、主成分分析やＰＬＳ分析などの統計解析法をスペクトル解析に応用することは既に知られていることであるが、（例えば、特許文献１２、特許文献１３、特許文献１４を参照）極めて近似した化学物質構成の皮膚という場所に於ける、シワ、或いは真皮コラーゲン線維束構造の秩序の程度など、形態的変化を近赤外吸収スペクトルの統計学的分析から定量するような技術は全く知られていない。
On the other hand, in analysis using a near-infrared spectrum, a technique for measuring the near-infrared spectrum of the skin and quantifying the amount of glucose in the blood (see, for example, Patent Document 7 and Patent Document 8) or near-red of the skin A technique for measuring the outer spectrum, extracting a peak derived from water, and quantifying the amount of water in the skin (see, for example, Patent Document 9), and a method for quantifying the amount of subcutaneous fat from the near-infrared spectrum of the skin ( For example, see Patent Document 10) or a method for estimating the severity of a wound from a plurality of spectrum channel images of a wound, wherein a near infrared spectrum image is used as one of the plurality of spectrum channel images. (For example, refer to Patent Document 11), etc., which is a method for distinguishing skin properties, wherein near-infrared absorption spectra of two or more skins having different states are measured in advance, and the near-infrared absorption spectrum A technique for multivariate analysis of the skin property indication value, comparing the result of analysis with the near-infrared absorption spectrum of the skin as the test sample, and distinguishing the skin property of the test sample is as follows: Not known at all. Furthermore, it is already known that statistical analysis methods such as principal component analysis and PLS analysis are applied to spectrum analysis (see, for example, Patent Document 12,
本発明は、この様な状況下為されたものであり、シワや皮膚弾性特性、真皮コラーゲン線維束構造の秩序などの皮膚状態の鑑別において、誰もが容易にかかる鑑別を為しうる技術を提供することを課題とする。 The present invention has been made under such circumstances, and in the differentiation of skin conditions such as wrinkles, skin elastic characteristics, and the order of the dermal collagen fiber bundle structure, anyone can easily make such a differentiation. The issue is to provide.
本発明者らは、かかる状況に鑑みて、シワや皮膚弾性特性、真皮コラーゲン線維束構造の秩序などの皮膚状態の鑑別において、誰もが容易にかかる鑑別を為しうる技術を求めて、鋭意研究努力を重ねた結果、皮膚性状の鑑別法であって、予め状態の異なる２種以上の皮膚の近赤外吸収スペクトルを測定し、前記近赤外吸収スペクトルと、皮膚性状の示性値とを多変量解析し、該分析結果を指標として、これと試験試料である皮膚の近赤外吸収スペクトルとを比較することにより、前記試験試料の皮膚状態の鑑別が、再現良く、且つ、鑑別者の属性によらず行えることを見出し、発明を完成させるに至った。即ち、本発明は以下に示す技術に関するものである。
（１）予め得ておいた、状態の異なる２種以上の皮膚の４２００〜８０００ｃｍ-1の波長領域の近赤外吸収スペクトルと、同皮膚の弾性との多変量解析の解析結果を指標として、
被験試料である皮膚の４２００〜８０００ｃｍ-1の波長領域の近赤外吸収スペクトルから、該被験試料である皮膚の弾性を測定する、方法。
（２）前記近赤外吸収スペクトルが、フーリエ変換近赤外吸収スペクトルであることを特徴とする、（１）に記載の方法。
（３）前記波長領域が、４３００〜５０００ｃｍ-1、５０００〜５５００ｃｍ-1、５５００〜６１００ｃｍ-1、及び６７００〜７５００ｃｍ-1のいずれかである、（１）又は（２）に記載の方法。
（４）予め得ておいた、状態の異なる２種以上の皮膚の４２００〜８０００ｃｍ -1 の波長領域の近赤外吸収スペクトルと、真皮コラーゲン線維束構造の秩序のスコアとの多変量解析の解析結果を指標として、
被験試料である皮膚の４２００〜８０００ｃｍ -1 の波長領域の近赤外吸収スペクトルから、該被験試料である真皮コラーゲン線維束構造の秩序のスコアを算出する、方法。
（５）前記近赤外吸収スペクトルが、フーリエ変換近赤外吸収スペクトルであることを特徴とする、（４）に記載の方法。
（６）前記波長領域が、４３００〜５０００ｃｍ -1 、５０００〜５５００ｃｍ -1 、５５００〜６１００ｃｍ -1 、及び６７００〜７５００ｃｍ -1 のいずれかである、（４）又は（５）に記載の方法。
In view of such circumstances, the present inventors have eagerly sought for a technique by which anyone can easily perform such differentiation in the differentiation of skin conditions such as wrinkles, skin elastic characteristics, and the order of the dermal collagen fiber bundle structure. As a result of repeated research efforts, it is a method for distinguishing skin properties, measuring near-infrared absorption spectra of two or more skins in different states in advance, Multivariate analysis, and using the analysis result as an index, and comparing this with the near-infrared absorption spectrum of the skin that is the test sample, the discrimination of the skin state of the test sample is reproducible and the discriminator We have found that this can be done regardless of the attribute of the present invention, and have completed the invention. That is, the present invention relates to the following technique.
(1) had previously been obtained, and near-infrared absorption spectrum in the wavelength region of 4200～8000Cm -1 of two or more skin of different states, the analysis results of the multivariate analysis of the elastic of the skin as an indicator ,
A method of measuring the elasticity of the skin as a test sample from a near-infrared absorption spectrum in the wavelength region of 4200 to 8000 cm −1 of the skin as the test sample.
(2) The method according to (1), wherein the near infrared absorption spectrum is a Fourier transform near infrared absorption spectrum.
(3) the wavelength region, 4300~5000cm -1, 5000~5500cm -1, is either 5500~6100cm -1, and 6700~7500cm -1, the method described in (1) or (2).
(4) Analysis of multivariate analysis of near-infrared absorption spectra in the wavelength region of 4200 to 8000 cm −1 of two or more types of skin obtained in advance and the score of the order of the dermal collagen fiber bundle structure Using the results as an indicator
A method of calculating an order score of a dermal collagen fiber bundle structure as a test sample from a near-infrared absorption spectrum in a wavelength region of 4200 to 8000 cm −1 of the skin as a test sample .
(5) The method according to (4), wherein the near infrared absorption spectrum is a Fourier transform near infrared absorption spectrum.
(6) the wavelength region, 4300~5000cm -1, 5000~5500cm -1, is either 5500~6100cm -1, and 6700~7500cm -1, the method described in (4) or (5).
本発明によれば、シワや皮膚弾性特性、真皮コラーゲン線維束構造の秩序などの皮膚状態の鑑別において、誰もが容易にかかる鑑別を為しうる技術を提供することができる。 ADVANTAGE OF THE INVENTION According to this invention, in the discrimination of skin conditions, such as a wrinkle, a skin elastic characteristic, and the order of a dermis collagen fiber bundle structure, the technique which anyone can make such a discrimination easily can be provided.
本発明の鑑別法は、皮膚の状態の鑑別法であって、予め状態の異なる２種以上の状態の皮膚の近赤外吸収スペクトルを測定し、前記近赤外吸収スペクトルと状態の示性値とを多変量解析により分析し、該分析結果と試験試料の近赤外吸収スペクトルとを比較し、試験試料の状態を推定し、これを指標とすることを特徴とする。かかる指標、或いは鑑別に用いる近赤外吸収スペクトルは通常の回折格子を用いた分散型のものによるスペクトル、ダイオードアレーを用いた装置によるスペクトル、更にこれらをフーリエ変換したスペクトル、検出されたインターフェログラムをフーリエ変換したスペクトルの何れもが使用可能である。更に好ましいものは、分散型の装置によるスペクトルを更にフーリエ変換したもの等が例示できる。特に好ましいものは、フーリエ変換をしたスペクトルを用いることである。ここで、多変量解析（統計化学的処理）であるが、多変量解析とは、分光データなどの化学的な特性と物性などの特性値との関係を計量学的な処理によって関係づけ、解析する手法であり、重回帰分析或いは主成分分析などが知られている。この内、重回帰分析としてはＰＬＳ分析が好適に例示できる。このＰＬＳ分析であるが、この分析法は特定の試料に於ける波長などの連続的な因子の変化に対して、吸光度などの変数の出現する分光スペクトルパターンと当該試料のある示性値の間の関係を分析する場合において、各示性値と因子ごとの変数の変化を分析する手技として確立されているものである。又、主成分分析は、同様な分析において、変動に寄与する第一主成分を分析し、しかる後この第一主成分軸に対して直交する第二主成分軸を分析し、この２つの主成分軸がつくる座標におけるパターン変化で物性を比較、推定する方法である。この様なＰＬＳ分析或いは主成分分析と言った、多変量解析は、市販されているソフトウェアを使用して行うことができる。このような多変量解析用のソフトウェアとしては、例えば、ＧＬサイエンス社より販売されている、ピロエット（ＰＩＲＯＵＥＴＴ）、サイバネットシステム社より販売されている、マットラボ（ＭＡＴＬＡＢ）横川電気株式会社より販売されている、アンスクランブラーＩＩ（ＵｎｓｃｒａｎｂｌｅｒＩＩ）、セパノヴァ（ＳＥＰＡＮＯＶＡ）社より販売されているシムカ（ＳＩＭＣＡ）等のソフトウェアが例示できる。又、これらに加えてシムカ（ＳＩＭＣＡ）と言われるアルゴリズムを加えることができる。かかるアルゴリズムは前記ソフトウェア中に組み込まれている場合が多く、主成分分析の表示に有用である。これらのソフトウェアを利用して、近赤外吸収スペクトルを解析し、その結果を本発明の鑑別法で用いる場合、大凡の処理ステップは次に示す手順による。この時、使用するフーリエ変換近赤外吸収スペクトルは測定して得られた原スペクトルでも良いし、前記原スペクトルをデータ加工したものでも良い。データ加工の方法としては、例えば、一次微分値、二次微分値、三次微分値などの多次微分値や平滑化（Smoothing）、ノーマライズ（Nor
malize）、MSC（Multiplicative Scatter Correction）、SNV（Standard Normal Variate）、平均化（Mean-Center）、オートスケール（Autoscale）などが好ましく例示できる。この内、好ましいものは原スペクトル或いはその二次微分値である。かくして、分析すると皮膚の状態を表す示性値と皮膚のフーリエ変換近赤外吸収スペクトルの間には良好な相関関係がある。
The discrimination method of the present invention is a skin state discrimination method in which near-infrared absorption spectra of skin in two or more different states are measured in advance, and the near-infrared absorption spectrum and the state value of the state are measured. Are analyzed by multivariate analysis, the analysis result is compared with the near-infrared absorption spectrum of the test sample, the state of the test sample is estimated, and this is used as an index. The near-infrared absorption spectrum used for this index or discrimination is a spectrum obtained by a dispersion type using a normal diffraction grating, a spectrum obtained by a device using a diode array, a spectrum obtained by Fourier transforming these, and a detected interferogram. Any spectrum obtained by Fourier transform of can be used. More preferable examples include those obtained by further Fourier transforming the spectrum obtained by the dispersion type apparatus. It is particularly preferable to use a spectrum obtained by performing Fourier transform. Here, multivariate analysis (statistical chemical processing) is used, but multivariate analysis is a relationship between chemical properties such as spectroscopic data and property values such as physical properties. For example, multiple regression analysis or principal component analysis is known. Of these, PLS analysis can be suitably exemplified as the multiple regression analysis. This PLS analysis is based on the difference between a spectral spectrum pattern in which a variable such as absorbance appears and a certain characteristic value of the sample with respect to a continuous change in a factor such as a wavelength in a specific sample. This is an established technique for analyzing the change of each characteristic value and the variable for each factor. In the principal component analysis, the first principal component contributing to the fluctuation is analyzed in the same analysis, and then the second principal component axis orthogonal to the first principal component axis is analyzed. This is a method for comparing and estimating physical properties by pattern changes in the coordinates created by component axes. Multivariate analysis such as PLS analysis or principal component analysis can be performed using commercially available software. As such multivariate analysis software, for example, sold by GL Science, Pirouett, sold by Cybernet Systems, sold by Matlab Yokogawa Electric Co., Ltd. Software such as Simsca (SIMCA) sold by Unscrambler II and Sepanova (SEPANOVA) can be exemplified. In addition to these, an algorithm called SIMCA can be added. Such an algorithm is often incorporated in the software and is useful for displaying principal component analysis. When these softwares are used to analyze near-infrared absorption spectra and the results are used in the discrimination method of the present invention, the general processing steps are as follows. At this time, the Fourier transform near-infrared absorption spectrum to be used may be an original spectrum obtained by measurement, or may be obtained by processing the original spectrum. Data processing methods include, for example, first-order differential values, second-order differential values, third-order differential values, etc., smoothing (Smoothing), normalization (Nor
Preferred examples include malize), MSC (Multiplicative Scatter Correction), SNV (Standard Normal Variate), averaging (Mean-Center), and autoscale. Of these, the original spectrum or its second derivative is preferred. Thus, when analyzed, there is a good correlation between the readings representing the skin condition and the Fourier transform near infrared absorption spectrum of the skin.
一方、皮膚の状態としては、通常化粧料や皮膚科学の分野で使用されている因子を用いることが出来る。この様な因子としては、例えば、シワ、肌荒れ、かさつき、のっぺり感等の表面形態的な因子、皮膚の弾性、はり等の皮膚粘弾特性、真皮コラーゲン線維束構造の秩序、角層構造等の皮膚構造特性、経皮的水分散逸量（ＴＥＷＬ）、脂質代謝量等の生理学的特性等が好適に例示出来る。これらの内、好ましいものは、「定量化が困難な評価技術、或いは、手技習熟に手間がかかる技術が、客観的、且つ、容易に定量化できる」と言う、効果の高い、シワの程度、皮膚粘弾性、真皮コラーゲン線維束構造の秩序度合いが特に好ましく例示出来る。シワの程度は、レプリカを撮像ユニットを用いてコンピューターにより画像処理し、数値化して用いることが出来る。値としては，シワ面積比率やシワ体積比率が求められる。真皮コラーゲン線維束構造の秩序は、専門家の目視による判定、例えばスコア値などを用いることが出来るし、皮膚粘弾性は、「キュートメータ」と称される測定機器を用いて、皮膚を吸引し、該吸引によって皮膚の変形する程度、該変形が元に戻る程度を数値化して用いることが出来る。又、更に正確に測定するには前記特許文献６に記載されている「レジリオメータ」と称される測定機器で、皮膚変形特性の代表値を使用することも出来る。真皮コラーゲン線維束構造に関しては、人より採取した皮膚の電子顕微鏡像をスコア化した値や、動物に長期間紫外線を照射し、人為的に真皮コラーゲン線維束構造を崩し、この電子顕微鏡観察像からの目視判定を用いることも出来る。
On the other hand, factors commonly used in the cosmetics and dermatological fields can be used as the skin condition. Such factors include, for example, surface morphological factors such as wrinkles, rough skin, roughness, and a feeling of firmness, skin elasticity, skin viscoelastic properties such as beams, order of dermal collagen fiber bundle structure, stratum corneum structure, etc. Physiological characteristics such as skin structure characteristics, transdermal water dispersion loss (TEWL), lipid metabolism and the like can be suitably exemplified. Among these, preferred is a highly effective degree of wrinkle, saying that “an evaluation technique that is difficult to quantify or a technique that requires time and skill to learn is objective and easy to quantify”. The degree of order of skin viscoelasticity and dermal collagen fiber bundle structure can be particularly preferably exemplified. The degree of wrinkles can be used by processing a replica image by a computer using an imaging unit and digitizing the replica. As a value, a wrinkle area ratio and a wrinkle volume ratio are obtained. The order of the dermal collagen fiber bundle structure can be determined by an expert's visual judgment, for example, a score value, and the skin viscoelasticity is obtained by sucking the skin using a measuring device called a “cutmeter”. The degree of deformation of the skin by the suction and the degree of return of the deformation can be converted into numerical values. For more accurate measurement, a representative value of skin deformation characteristics can be used with a measuring instrument called “resiliometer” described in
本発明の鑑別法で使用されるフーリエ変換近赤外吸収スペクトルとしては、４０００〜８０００ｃｍ−１の内の少なくとも４００ｃｍ−１が好ましい波長領域であり、特に好ましい波長領域では４３００〜５０００ｃｍ−１、５０００〜５５００ｃｍ−１、５５００〜６１００ｃｍ−１、６７００〜７５００ｃｍ−１である。これは、この波長領域に於けるスペクトルが皮膚の状態の示性値を良く反映しているからである。この範囲の近赤外吸収スペクトルは皮膚内の蛋白質の存在状態とその挙動を的確に捉えていることもその一因と考えられる。 As a Fourier transform near infrared absorption spectrum used in the discrimination method of the present invention, at least 400 cm-1 of 4000 to 8000 cm-1 is a preferable wavelength region, and in a particularly preferable wavelength region, 4300 to 5000 cm-1, 5000. -5500 cm-1, 5500-6100 cm-1, and 6700-7500 cm-1. This is because the spectrum in this wavelength region well reflects the characteristic value of the skin condition. The near-infrared absorption spectrum in this range is considered to be due to the fact that the existence state and behavior of proteins in the skin are accurately captured.
かくして、測定された近赤外吸収スペクトルは、好ましくはフーリエ変換された後、前記皮膚状態の示性値とともに統計化学的分析にかけられ、その因果関係を数量化される。この数量関係と試験試料のスペクトルの対比より、試験試料の皮膚状態が鑑別される。これらの具体的手順を下記に示す。 Thus, the measured near-infrared absorption spectrum is preferably subjected to a Fourier transform and then subjected to statistical chemical analysis together with the skin condition indication value, and the causal relationship thereof is quantified. The skin condition of the test sample is discriminated from the comparison between the quantity relationship and the spectrum of the test sample. These specific procedures are shown below.
ＰＬＳ分析の場合
（１）皮膚の分散型或いはダイオードアレイタイプの近赤外吸収スペクトル或いはそれらのフーリエ変換スペクトルやフーリエ変換スペクトルを所望により、二次微分等データ加工を行い、波長と近赤外吸収スペクトル乃至はその加工データとの行列を作成する。
（２）前記行列と示性値との行列を作成し、示性値の動きに対して、動きの大きい近赤外吸収スペクトル乃至はその加工データを抽出し、その波長を特定する。（３）抽出した近赤外吸収スペクトル乃至はその加工データと示性値より検量線を作成する。同時に、示性値ごとに検量線上へのプロットを作成しておく。
（４）試験試料のフーリエ変換近赤外吸収スペクトルを測定し、所望により二次微分等のデータ加工する。
（５）（４）のデータより（２）で特定された波長のデータを抽出する。
（６）（５）で抽出されたデータを検量線上への写像を作成する。或いは、データを検量線上へプロットする。
（７）（３）の示性値ごとのプロットと（５）の写像乃至はプロットとを比較し、試料の示性値を推測する。
尚、（２）以下の作業はコンピューターソフトウェアを利用することにより行うことができる。
In the case of PLS analysis (1) The near-infrared absorption spectrum of the skin dispersion type or diode array type, or their Fourier transform spectrum or Fourier transform spectrum, if desired, data processing such as second derivative, wavelength and near infrared absorption A matrix with the spectrum or the processed data is created.
(2) A matrix of the matrix and the characteristic value is created, a near-infrared absorption spectrum or its processed data having a large movement is extracted with respect to the movement of the characteristic value, and the wavelength is specified. (3) A calibration curve is created from the extracted near-infrared absorption spectrum or the processed data and the characteristic value. At the same time, a plot on the calibration curve is created for each characteristic value.
(4) The Fourier transform near-infrared absorption spectrum of the test sample is measured, and data such as second derivative is processed as desired.
(5) Extract the data of the wavelength specified in (2) from the data of (4).
(6) Create a map of the data extracted in (5) onto the calibration curve. Alternatively, the data is plotted on a calibration curve.
(7) The plot for each characteristic value in (3) is compared with the mapping or plot in (5) to estimate the characteristic value of the sample.
(2) The following operations can be performed using computer software.
主成分分析の場合
（１）皮膚の分散型或いはダイオードアレイタイプの近赤外吸収スペクトル或いはそれらのフーリエ変換スペクトルやフーリエ変換スペクトルを所望により、二次微分等データ加工を行い、波長と近赤外吸収スペクトル乃至はその加工データとの行列を作成する。
（２）前記行列について主成分分析を行い、第一主成分軸を作成する。
（３）第一主成分と直交する第二主成分軸を作成する。
（４）第一主成分軸と第二主成分軸が作る平面上に（１）のスペクトルの第一主成分と第二主成分が作る点をプロットする。
（５）所望によりシムカなどのアルゴリズムを用いてグルーピングを行う。
（６）（１）と同様に試験試料の近赤外スペクトルを測定し、（４）と同様のプロットを行う。
（７）（４）のプロット乃至は（５）のグルーピングを指標に試験試料の鑑別を行う。
In the case of principal component analysis (1) The near-infrared absorption spectrum of the skin dispersion type or diode array type, or the Fourier transform spectrum or Fourier transform spectrum thereof, if desired, is subjected to data processing such as second derivative, and the wavelength and near infrared An absorption spectrum or a matrix with the processed data is created.
(2) A principal component analysis is performed on the matrix to create a first principal component axis.
(3) Create a second principal component axis orthogonal to the first principal component.
(4) The points formed by the first principal component and the second principal component of the spectrum of (1) are plotted on the plane formed by the first principal component axis and the second principal component axis.
(5) Grouping is performed using an algorithm such as shimuka as desired.
(6) The near-infrared spectrum of the test sample is measured in the same manner as (1), and the same plot as in (4) is performed.
(7) The test sample is identified using the plot in (4) or the grouping in (5) as an index.
本発明の鑑別法は、化粧料の選択或いはエステティックなどのコースの選択のために、皮膚状態をグループ分けし、皮膚状態に適した化粧料或いはエステティックコースを選別するのに使用することも出来るし、化粧料による処置、或いは、エステティックによる処置の効果を、経時的に皮膚状態を鑑別し、その変化をトレースして、皮膚状態のモニタリングに使用することも出来る。或いは、皮膚内部状態を鑑別し，表面形態への将来的な影響を予知し、
皮膚内部状態に適した化粧料或いはエステティックコースを選別するのに使用することも出来る。
The discrimination method of the present invention can also be used to group skin conditions and select cosmetics or aesthetic courses suitable for the skin condition for selection of cosmetics or courses such as esthetics. In addition, the effect of treatment with cosmetics or treatment with esthetics can be used for skin condition monitoring by distinguishing the skin condition over time and tracing the change. Or, identify the internal state of the skin and predict future effects on the surface morphology,
It can also be used to select cosmetics or aesthetic courses suitable for the internal skin condition.
以下に、実施例を挙げて、本発明について更に詳細に説明を加えるが、本発明がかかる実施例にのみ限定されないことは言うまでもない。 Hereinafter, the present invention will be described in more detail with reference to examples, but it is needless to say that the present invention is not limited to such examples.
＜参考例＞
モデル動物の作成
図４２に示すスケジュールに従って、ヘアレスマウス（雌性、５週齢）の背部に、１日１回、１週３回５４〜１０８ｍＪ／ｃｍ２の紫外線を、徐々に照射エネルギー量を増やしながら、２〜１０週間連続照射し、群毎にその程度の異なる、光老化を皮膚に起こさせ、これをモデル動物とした。
<Reference example>
Preparation of model animal According to the schedule shown in FIG. 42, the back of a hairless mouse (female, 5 weeks old) was irradiated with ultraviolet rays of 54 to 108 mJ /
＜実施例１＞
参考例の動物の背部のフーリエ変換近赤外吸収スペクトルを測定した。その後、キュートメータで皮膚の弾性の測定を、及び、目視で真皮コラーゲン線維束構造の秩序の判定を行った。キュートメタでは、これにより描かれる曲線のＵｒ＊で表されるパラメータ（図１を参照）を用いた。真皮コラーゲン線維束構造の秩序は、この測定が済んだ後、動物より、皮膚を採取し、標本を作製し、電子顕微鏡下、次に示す基準に従ってスコアリングし評価、判定した。スコア３：視野全体に堅牢な真皮コラーゲン繊維を認める、スコア２：堅牢な真皮コラーゲン繊維を視野の半分以上に認める。僅かに繊維束構造が崩れているのを認める、スコア１：僅かに堅牢な真皮コラーゲン繊維を認める。顕微鏡下繊維束構造は認められない、スコア０：堅牢な真皮コラーゲン繊維は全く観察されないの基準である。また、表皮の厚さを計測した。表皮に於けるシワの体積率は、動物背部からレプリカをとり、そのレプリカを撮像ユニットを用いてコンピューターにより画像処理し、数値化して用いた。これらの数値と近赤外吸収スペクトルとを用いて、ＰＬＳ分析をアンスクランブラーＩＩを用いて行いＰＬＳ分析により検量線を作成した。検量線は図２、４、６、８、１０、１２、１４、１６、１８、２０、２２、２４、２６、２８、３０、３２、３４、３６、３８、４０に示す。これより、近赤外吸収スペクトルと皮膚状態の示性値の間には、極めて良好な相関関係が存することが判る。更に、アンスクランブラーＩＩを用いて、これらの主成分分析を行った。結果を図３、５、７、９、１１、１３、１５、１７、１９、２１、２３、２５、２７、２９、３１、３３、３５、３７、３９、４１に示す。これより、示性値毎にプロットがまとまりグループを形成していることが判る。このグループの属性が何であるかが判っていれば、近赤外吸収スペクトルより、その試験試料の属性が明らかになることが判る。又、本発明の鑑別法では、複数の示性値に鑑別が瞬時に行えるメリットが存することも明白に判る。
近赤外分光分析装置：ＶＥＣＴＥＲ ２２／Ｎ（ブルカー・オプティクス）
レプリカ撮像ユニット：ＡＳＡ−０３Ｒ−Ｕ
<Example 1>
The Fourier transform near infrared absorption spectrum of the back of the animal of the reference example was measured. Thereafter, the skin elasticity was measured with a cutometer, and the order of the dermal collagen fiber bundle structure was visually determined. In the cute meta, a parameter (see FIG. 1) represented by Ur * of the curve drawn by this was used. After this measurement, the order of the dermal collagen fiber bundle structure was evaluated by evaluating the skin by collecting skin from an animal, preparing a specimen, and scoring under an electron microscope according to the following criteria. Score 3: Strong dermal collagen fibers are observed in the entire visual field. Score 2: Strong dermal collagen fibers are observed in more than half of the visual field. Slightly broken fiber bundle structure is observed. Score 1: Slightly robust dermal collagen fibers are observed. No microscopic fiber bundle structure is observed, score 0: a criterion that no robust dermal collagen fibers are observed. In addition, the thickness of the epidermis was measured. For the volume ratio of wrinkles in the epidermis, a replica was taken from the back of the animal, the replica was subjected to image processing by a computer using an imaging unit, and used as a numerical value. Using these values and the near infrared absorption spectrum, PLS analysis was performed using Unscrambler II, and a calibration curve was prepared by PLS analysis. The calibration curves are shown in FIGS. 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40. From this, it can be seen that there is a very good correlation between the near-infrared absorption spectrum and the skin condition indication value. Furthermore, these principal component analyzes were performed using Unscrambler II. The results are shown in FIGS. 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41. From this, it can be seen that the plots are grouped for each characteristic value to form a group. If it is known what the attribute of this group is, it can be understood from the near-infrared absorption spectrum that the attribute of the test sample becomes clear. In addition, it can be clearly seen that the discrimination method of the present invention has an advantage that discrimination can be instantly performed for a plurality of characteristic values.
Near-infrared spectrometer: VECTER 22 / N (Bruker Optics)
Replica imaging unit: ASA-03R-U
本発明は、被験者に負担を殆どかけずに、非侵襲的に皮膚の内部構造まで鑑別出来るので、苦痛無く適切な化粧料を選ぶための皮膚データを提供することが出来、化粧料販売支援カウンセリングツールに利用出来る。 Since the present invention can distinguish the internal structure of the skin non-invasively with almost no burden on the subject, it can provide skin data for selecting an appropriate cosmetic without pain and can provide cosmetic sales support counseling. Available for tools.
Claims (6)
被験試料である皮膚の４２００〜８０００ｃｍ-1の波長領域の近赤外吸収スペクトルから、該被験試料である皮膚の弾性を測定する、方法。 Had previously been obtained, and near-infrared absorption spectrum in the wavelength region of 4200～8000Cm -1 of two or more skin of different states, the analysis results of the multivariate analysis of the elastic of the skin as an indicator,
A method of measuring the elasticity of the skin as a test sample from a near-infrared absorption spectrum in the wavelength region of 4200 to 8000 cm −1 of the skin as the test sample.
被験試料である皮膚の４２００〜８０００ｃｍ4200-8000 cm of skin as test sample -1-1 の波長領域の近赤外吸収スペクトルから、該被験試料である真皮コラーゲン線維束構造の秩序のスコアを算出する、方法。The score of the order of the dermal collagen fiber bundle structure which is the said test sample is calculated from the near-infrared absorption spectrum of the wavelength region.
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| KR20090060418A (en) | 2006-10-06 | 2009-06-12 | 가부시키가이샤 시세이도 | Ultraviolet radiation protective effect evaluation method and device |
| JP2008111834A (en) * | 2006-10-06 | 2008-05-15 | Shiseido Co Ltd | Method and device for evaluating ultraviolet radiation protective effect |
| JP5275898B2 (en) * | 2009-05-21 | 2013-08-28 | ポーラ化成工業株式会社 | Evaluation method of skin condition |
| JP5635762B2 (en) * | 2009-11-11 | 2014-12-03 | ポーラ化成工業株式会社 | Method for calculating nipple shape or collagen-like structure |
| CN103654724B (en) * | 2012-09-04 | 2016-06-22 | 宝丽化成工业有限公司 | Differential method based on the skin condition that filamentary structure is analyzed |
| JP7280676B2 (en) * | 2018-09-26 | 2023-05-24 | ポーラ化成工業株式会社 | Screening method for ingredients that suppress deterioration of collagen structure due to hypoxic conditions and/or aging, using the degree of cohesion of collagen fibers as an index |
| WO2019225728A1 (en) * | 2018-05-25 | 2019-11-28 | ポーラ化成工業株式会社 | Method for screening for components that improve condition of aged or hypoxic skin, and method for estimating oxygen level of subdermal tissue or fibrosis level of subdermal adipocytes as index of subdermal tissue viscoelasticity |
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| JP2000186998A (en) * | 1998-12-22 | 2000-07-04 | Matsushita Electric Works Ltd | Living body spectrum measuring device |
| JP2000201899A (en) * | 1999-01-11 | 2000-07-25 | Kao Corp | Display method for skin state |
| JP2003144421A (en) * | 2001-11-15 | 2003-05-20 | Matsushita Electric Works Ltd | Method and device for determining biocomponent |
| JP2004089617A (en) * | 2002-09-04 | 2004-03-25 | Pola Chem Ind Inc | Distinguishing method for turbulence of corium collagen fiber bundle |
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2003
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1998040045A1 (en) * | 1997-03-11 | 1998-09-17 | Pola Chemical Industries Inc. | Method for evaluating skin conditioning agents and process for producing external skin preparations |
| JPH11137525A (en) * | 1997-11-07 | 1999-05-25 | Kanebo Ltd | Skin condition measuring apparatus |
| JPH11244266A (en) * | 1998-02-27 | 1999-09-14 | Matsushita Electric Works Ltd | Superficial organism tissue analytical method and superficial organism tissue analyzer |
| JP2000186998A (en) * | 1998-12-22 | 2000-07-04 | Matsushita Electric Works Ltd | Living body spectrum measuring device |
| JP2000201899A (en) * | 1999-01-11 | 2000-07-25 | Kao Corp | Display method for skin state |
| JP2003144421A (en) * | 2001-11-15 | 2003-05-20 | Matsushita Electric Works Ltd | Method and device for determining biocomponent |
| JP2004089617A (en) * | 2002-09-04 | 2004-03-25 | Pola Chem Ind Inc | Distinguishing method for turbulence of corium collagen fiber bundle |
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