JP2003270138A - Hair condition discrimination method - Google Patents
Hair condition discrimination methodInfo
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- JP2003270138A JP2003270138A JP2002074219A JP2002074219A JP2003270138A JP 2003270138 A JP2003270138 A JP 2003270138A JP 2002074219 A JP2002074219 A JP 2002074219A JP 2002074219 A JP2002074219 A JP 2002074219A JP 2003270138 A JP2003270138 A JP 2003270138A
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- Prior art keywords
- hair
- condition
- infrared absorption
- absorption spectrum
- differentiating
- Prior art date
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- Investigating Or Analysing Materials By Optical Means (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
Description
【０００１】[0001]
【発明の属する技術分野】本発明は、毛髪用の化粧料の
評価などに有用な毛髪の状態の鑑別法に関する。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for distinguishing the condition of hair, which is useful for evaluating cosmetics for hair.
【０００２】[0002]
【従来の技術】毛髪の状態を鑑別することは、毛髪用の
化粧料の評価などには必須の事項であり、従来この様な
毛髪の状態の鑑別は、非侵襲的な方法としては、専門パ
ネラーによる官能検査があるのみであり、その他として
は、侵襲的に毛髪を採取し、グロスメーターにより、つ
やの示性値としてのグロス値を測定する方法や摩擦感テ
スターにより、なめらかさの示性値である抵抗値を測定
する方法などが存在している。即ち、毛髪の状態の鑑別
において、非進取的に鑑別を行う方法の開発、取り分
け、定量性のある鑑別法の開発が望まれていた。又、毛
髪の状態の代理値として毛髪内の水分量があり、これを
通常のフーリエ変換を使用しない近赤外吸収スペクトル
であって、回折格子を用いた分散型の近赤外吸収スペク
トルより解析し、毛髪の示性値の代替として用いること
は知られているが、近赤外吸収スペクトルの解析より得
られた水の特性と毛髪の状態との間の因果関係について
は検討されていない。又、分光分析と特定の示性値とを
ＰＬＳ分析等の重回帰分析、主成分分析などの多変量解
析を行い、相関関係に明らかにする手技は知られている
が、毛髪の状態と近赤外吸収スペクトルとについて、多
変量解析を行い、近赤外吸収スペクトルより毛髪の状態
を鑑別するような試みも為されていない。加えて、つや
やかさやなめらかさ等の毛髪の状態を近赤外吸収スペク
トルの多変量解析より鑑別することも行われていなかっ
たし、行うような発想自体も存在していなかった。毛髪
のつややかさやなめらかさと水の存在状態やその存在量
との因果関係は全く知られていなかった。2. Description of the Related Art Differentiating the condition of hair is an essential item for evaluation of cosmetics for hair. Conventionally, such distinction of the condition of hair is a non-invasive method. There is only a sensory test by a panelist, and the other is that the smoothness index is measured by a method of invasively collecting hair and measuring the gloss level as a gloss index using a gloss meter, or by a friction tester. There are methods for measuring the resistance value. That is, in the discrimination of the state of hair, it has been desired to develop a method for performing discrimination in a non-progressive manner, a separate method, and a discrimination method having a quantitative property. In addition, there is a water content in the hair as a surrogate value for the state of the hair, which is a near-infrared absorption spectrum that does not use ordinary Fourier transform, and is analyzed from a dispersion-type near-infrared absorption spectrum using a diffraction grating. However, although it is known to be used as a substitute for the indication value of hair, the causal relationship between the properties of water and the condition of hair obtained by analysis of near infrared absorption spectra has not been examined. Further, a technique for performing a multiple regression analysis such as a PLS analysis or a multivariate analysis such as a principal component analysis on a spectroscopic analysis and a specific indication value to clarify the correlation is known, but it is close to the hair condition. No attempt has been made to perform multivariate analysis on the infrared absorption spectrum and distinguish the hair condition from the near infrared absorption spectrum. In addition, the hair condition such as gloss and smoothness has not been identified by multivariate analysis of near-infrared absorption spectrum, and the idea itself has not existed. The causal relationship between the gloss and smoothness of hair and the state of water present and the amount of water present was unknown at all.
【０００３】[0003]
【発明が解決しようとする課題】本発明は、この様な状
況下為されたものであり、毛髪用の化粧料の選択や評
価、毛髪の状態の変化のモニタリングなどに有用な、毛
髪の状態の鑑別において、非侵襲的に鑑別を行う方法、
取り分け、非侵襲的な毛髪の鑑別法であって、定量性の
ある鑑別法を提供することを課題とする。The present invention has been made under such circumstances, and is useful for selection and evaluation of cosmetics for hair, monitoring of changes in hair condition, and the like. Non-invasive method for differentiating
An object of the present invention is to provide a non-invasive method of distinguishing hair, which is quantitative and quantifiable.
【０００４】[0004]
【課題の解決手段】この様な状況に鑑みて、本発明者ら
は毛髪の状態の鑑別法であって、予め状態の異なる２種
以上の毛髪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し、該
分析結果を指標として、これと試験試料の近赤外吸収ス
ペクトルとを比較し、試験試料を鑑別することを特徴と
する、毛髪の状態の鑑別法により、試験試料の毛髪の状
態を非侵襲的に、且つ、定量的に鑑別できることを見出
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）毛髪の状態の鑑別法であって、予め状態の異なる
２種以上の毛髪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
し、該分析結果を指標として、これと試験試料の近赤外
吸収スペクトルとを比較し、試験試料を鑑別することを
特徴とする、毛髪の状態の鑑別法。
（２）前記近赤外吸収スペクトルが、フーリエ変換近赤
外吸収スペクトル及び／又はダイオードアレー検出器に
よるものであることを特徴とする、（１）に記載の毛髪
の状態の鑑別法。
（３）多変量解析が、重回帰分析乃至は主成分分析であ
ることを特徴とする、（１）又は（２）に記載の毛髪の
状態の鑑別法。
（４）毛髪の状態の表現項目が、なめらかさ及び／又は
つややかさであることを特徴とする、（１）〜（３）何
れか１項に記載の毛髪の状態の鑑別法。
（５）毛髪の状態の示性値がグロスメーターによる測定
値、摩擦感テスターによる測定値、つややかさの官能試
験結果及びなめらかさの官能試験結果から選ばれる１種
乃至は２種以上である、（１）〜（４）何れか１項に記
載の毛髪の状態の鑑別法。
（６）近赤外吸収スペクトルの測定波長の領域が、４７
００〜５０００ｃｍ−１であることを特徴とする、
（１）〜（５）何れか１項に記載の毛髪の状態の鑑別
法。
（７）毛髪用の化粧料の評価において、前記毛髪用の化
粧料による処理の前後に於ける毛髪の状態を、（１）〜
（６）何れか１項に記載の毛髪の状態の鑑別法によって
鑑別して得られる化粧料による毛髪の状態の変化を指標
とすることを特徴とする、毛髪用の化粧料の評価法。
（８）毛髪の状態のモニタリングにおいて、（１）〜
（６）何れか１項に記載の毛髪の状態の鑑別法で鑑別さ
れた毛髪の状態を指標とすることを特徴とする、毛髪の
状態のモニタリング方法。
以下、本発明について、更に詳細に説明を加える。In view of such a situation, the present inventors have proposed a method for differentiating the state of hair, in which the near-infrared absorption spectra of two or more types of hair having different states are measured in advance, Multivariate analysis of the infrared absorption spectrum and the indication value of the state, the analysis result as an index, to compare with the near infrared absorption spectrum of the test sample, characterized by distinguishing the test sample, The inventors have found that the hair condition of a test sample can be non-invasively and quantitatively distinguished by a method for distinguishing the condition of hair, and have completed the invention. That is, the present invention relates to the techniques described below. (1) A method for distinguishing the state of hair, in which the near-infrared absorption spectra of two or more types of hair having different states are measured in advance, and the near-infrared absorption spectrum and the exemplar value of the state are subjected to multivariate analysis. A method for differentiating a hair condition, which comprises using the analysis result as an index and comparing this with a near-infrared absorption spectrum of a test sample to distinguish the test sample. (2) The method for differentiating the state of hair according to (1), wherein the near infrared absorption spectrum is obtained by Fourier transform near infrared absorption spectrum and / or diode array detector. (3) The method for differentiating the hair condition according to (1) or (2), wherein the multivariate analysis is multiple regression analysis or principal component analysis. (4) The method for differentiating the hair condition according to any one of (1) to (3), wherein the expression item of the hair condition is smoothness and / or gloss. (5) One or two or more kinds of the indicator values of the state of the hair are selected from a gloss meter measurement value, a friction tester measurement value, a gloss sensory test result, and a smooth sensory test result. (1) to (4) The method for differentiating the state of hair according to any one of items. (6) The measurement wavelength range of the near infrared absorption spectrum is 47
It is characterized in that it is from 00 to 5000 cm-1,
(1) to (5) The method for distinguishing the condition of hair according to any one of items. (7) In the evaluation of the cosmetic for hair, the condition of the hair before and after the treatment with the cosmetic for hair is (1) to
(6) A method for evaluating a cosmetic composition for hair, which is characterized by using as an index a change in the condition of the hair due to the cosmetic composition obtained by differentiating by the method for differentiating the hair condition according to any one of items. (8) In monitoring the condition of hair, (1) to
(6) A method for monitoring the condition of hair, characterized in that the condition of the hair identified by the method for distinguishing the condition of hair according to any one of the items is used as an index. Hereinafter, the present invention will be described in more detail.
【０００５】[0005]
【発明の実施の形態】本発明の毛髪の状態の鑑別法は、
毛髪の状態の鑑別法であって、予め状態の異なる２種以
上の毛髪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、或いは鑑別の用いる近
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、ダイオードアレイ検出器によるスペクトル、
ダイオードアレイ検出器によるスペクトルを更にフーリ
エ変換したものが例示できる。ここで、多変量解析であ
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。この様な多変量解
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、ピロウェット（ＰＩＲＯＵＥＴ
Ｔ）、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、二次微分
値、三次微分値などの多次微分値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、分析すると毛髪の状態と
毛髪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。BEST MODE FOR CARRYING OUT THE INVENTION The method for discriminating the condition of hair according to the present invention comprises:
A method for distinguishing the state of hair, in which the near-infrared absorption spectra of two or more types of hair having different states are measured in advance, and the near-infrared absorption spectrum and the exemplar value of the state are analyzed by multivariate analysis, The analysis result and the near infrared absorption spectrum of the test sample are compared, the state of the test sample is estimated, and this is used as an index. As the near-infrared absorption spectrum used for such an index or discrimination, any of a spectrum by a dispersion type using a normal diffraction grating, a spectrum by a device using a diode array, and a spectrum obtained by Fourier transforming these can be used. is there. More preferable is a spectrum obtained by further Fourier transforming a spectrum obtained by a dispersion type apparatus, a spectrum obtained by a diode array detector,
An example is a spectrum obtained by further Fourier transforming the spectrum obtained by the diode array detector. Here, although it is a multivariate analysis, the multivariate analysis is a method of associating and analyzing the relationship between chemical properties such as spectral data and characteristic values such as physical properties by a metrological process. Regression analysis or principal component analysis is known. Of these, PLS analysis can be preferably exemplified as the multiple regression analysis. This PLS analysis is based on the fact that, with respect to a continuous change in a factor such as wavelength in a specific sample, the spectral spectrum pattern in which a variable such as absorbance appears and a certain rational value of the sample. It is established as a technique for analyzing the change in variables for each rational value and each factor when analyzing the relationship between. In the principal component analysis, in the same analysis, the first principal component contributing to the fluctuation is analyzed, and then the second principal component axis orthogonal to the first principal component axis is analyzed, and the two principal components are analyzed. It is a method of comparing and estimating physical properties by changing the pattern in the coordinates formed by the component axes. Multivariate analysis such as PLS analysis or principal component analysis can be performed using commercially available software. As software for such multivariate analysis, for example, Pirowet (PIROUTET) sold by GL Sciences, Inc.
T), software sold by Cybernet Systems Co., Ltd., matlab (MATLAB) Yokokawa Electric Co., Ltd., Unscrambler II (Unscranbler II), Sepanova (SIMCA) sold software such as SIMCA Can be illustrated.
In addition to these, an algorithm called SIMCA can be added. Such algorithms are often embedded in the software,
This is useful for displaying principal component analysis. When these software are used to analyze the near-infrared absorption spectrum and the result is 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 used may be the original spectrum obtained by measurement, or may be the one obtained by processing the original spectrum. As a data processing method, for example, a multi-order differential value such as a second-order differential value or a third-order differential value can be preferably exemplified. Of these, the preferable one is the original spectrum or its second derivative. Thus, there is a good correlation between the condition of the hair and the Fourier transform near infrared absorption spectrum of the hair when analyzed.
【０００６】ＰＬＳ分析の場合
（１）毛髪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。
主成分分析の場合
（１）毛髪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 PLS analysis (1) The wavelength and near-infrared absorption spectrum of the near-infrared absorption spectrum of hair dispersion type or diode array type or their Fourier transform spectrum is processed as desired by second-order differentiation. Or, a matrix with the processed data is created. (2) A matrix of the above-mentioned matrix and an indication value is created, and a near-infrared absorption spectrum or its processed data in which movement of the indication value is large is extracted, and its wavelength is specified. (3) A calibration curve is created from the extracted near-infrared absorption spectrum or its processed data and the characteristic value. At the same time, a plot on the calibration curve is prepared for each rational value. (4) Fourier transform near infrared absorption spectrum of the test sample is measured, and data such as second derivative is processed if desired. (5) Data of the wavelength specified in (2) is extracted from the data of (4). (6) Map the data extracted in (5) onto the calibration curve. Alternatively, the data is plotted on a calibration curve. (7) The plot of each indication value of (3) is compared with the mapping or plot of (5) to estimate the indication value of the sample. The operations (2) and below can be performed by using computer software. In the case of principal component analysis (1) If necessary, data such as the second derivative is processed for the near-infrared absorption spectrum of hair dispersion type or diode array type or Fourier transform spectrum thereof, and the wavelength and near-infrared absorption spectrum or Create a matrix with the processed data. (2) Principal component analysis is performed on the matrix to create a first principal component axis. (3) A second principal component axis orthogonal to the first principal component is created. (4) Plot the points formed by the first principal component and the second principal component of the spectrum of (1) on the plane formed by the first principal component axis and the second principal component axis. (5) If desired, grouping is performed using an algorithm such as Simuka. (6) The near infrared spectrum of the test sample is measured in the same manner as in (1), and the same plot as in (4) is performed. (7) The test sample is identified using the plots in (4) or the grouping in (5) as an index.
【０００７】本発明の毛髪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、４０００〜１２
０００ｃｍ−１の内の少なくとも１００ｃｍ−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４７０
０〜５０００ｃｍ−１ である。これは、この波長領域
に於けるスペクトルが毛髪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
毛髪内の蛋白質の存在状態とその挙動を的確に捉えられ
ていることもその一因と考えられる。The Fourier transform near infrared absorption spectrum used in the hair discrimination method of the present invention is 4000-12.
At least 100 cm −1 of 000 cm −1 is a preferable wavelength region, and 470 is particularly preferable.
It is 0-5000 cm-1. This is because the spectrum in this wavelength region well reflects the indicative value of the state of hair. It is considered that the near infrared absorption spectrum in this range is one of the reasons that the existence state and the behavior of the protein in the hair are accurately grasped.
【０００８】本発明の毛髪の状態の鑑別法で対象とする
毛髪の状態の表現項目としては、なめらかさとつややか
さが挙げられる。これらの項目は相互に関連しながら異
なる内容も含む表現項目であり、本発明の毛髪の状態の
鑑別法に於いては、これらのどちらかを対象とすること
もできるし、両方を対象とすることもできる。又、これ
らの表現項目の示性値としては、グロスメーターによる
測定値、摩擦感テスターによる測定値、つややかさの官
能試験結果及びなめらかさの官能試験結果から選ばれる
１種乃至は２種以上が好ましく例示できる。ここで、官
能評価は、通常化粧料の分野で使用されているものが使
用でき、具体的には、良い〜悪いを５段階乃至は３段階
に分けてスコアリングする方法が好ましく例示できる。
即ち、５段階の評価法であれば、スコア５：良い、スコ
ア４：やや良い、スコア３：普通、スコア２：やや悪
い、スコア１：悪いと言う評価軸を例示できるし、３段
階の評価であれば、スコア３：良い、スコア２：普通、
スコア１：悪いと言う評価軸が例示できる。グロスメー
ター或いは摩擦感テスターは市販の機器があり、これを
利用することができる。The expression items of the hair condition targeted by the method for distinguishing the hair condition of the present invention include smoothness and gloss. These items are expression items that are different from each other but include different contents. In the method of differentiating the hair condition of the present invention, either one of them can be targeted, or both of them can be targeted. You can also Further, as the indication values of these expression items, one kind or two or more kinds selected from a measurement value by a gloss meter, a measurement value by a friction tester, a sensory test result of glossiness and a sensory test result of smoothness are used. It can be preferably exemplified. Here, as the sensory evaluation, those generally used in the field of cosmetics can be used, and specifically, a method of scoring good to bad in 5 stages or 3 stages can be preferably exemplified.
That is, in the case of the 5-step evaluation method, the evaluation axes of score 5: good, score 4: good, score 3: normal, score 2: bad, score 1: bad can be exemplified, and 3-step evaluation is possible. If so, score 3: good, score 2: normal,
Score 1: An evaluation axis called bad can be illustrated. There are commercially available devices for the gloss meter or friction tester, which can be used.
【０００９】PLS分析を行い、フーリエ変換近赤外吸収
スペクトルの二次微分値と示性値との２変数の検量線を
作成するためには、示性値の異なる少なくとも２種の毛
髪を用意する必要があるが、この様な示性値の異なる毛
髪は、自然に存在する毛髪の示性値を測定し、これらの
うちで示性値の異なるものを選択して用いることもでき
るし、１種の毛髪を人工的に処理して示性値の異なる毛
髪とし、これを用いることもできる。In order to perform a PLS analysis and create a calibration curve of two variables of the second derivative of the Fourier transform near infrared absorption spectrum and the indication value, at least two types of hair having different indication values are prepared. However, it is also possible to measure the indicator values of naturally-occurring hair and to select and use those having different indicator values among these. It is also possible to artificially treat one kind of hair to obtain hair having different responsiveness values, which can be used.
【００１０】かくして、準備した検量線上に検量線作成
に使用した毛髪のフーリエ変換近赤外吸収スペクトルの
二次微分値の広がりをプロットしたり、二次微分値軸上
に示性値をプロットしたりすることにより、大凡の毛髪
のフーリエ変換近赤外吸収スペクトルの二次微分値と毛
髪の状態の示性値との関係を知ることが出来、試験試料
の毛髪のフーリエ変換近赤外吸収スペクトルのPLS分析
にて、示性値が関連あると特定された波長の二次微分値
のプロットをこれらと比較することにより試験試料であ
る毛髪の示性値を算出することができる。この時簡易的
に検量線上に、或いは、二次微分値軸上に検量線作成に
使用した毛髪のプロットの二次微分値のメジアンや平均
値などの群代表値をプロットしておき、試験試料の毛髪
の二次微分値の群代表値をプロットし、群代表値同士で
比較し、示性値を算出することもできる。ここで、注目
すべきは、一つのフーリエ変換近赤外吸収スペクトルを
測定することにより、本発明の鑑別法に従って摩擦感、
グロス（光沢）の程度、官能値などの多数の毛髪の状態
の示性値を算出できることであり、言い換えれば、１つ
の測定結果より、毛髪の状態の多面的評価ができること
である。Thus, the spread of the second derivative of the Fourier transform near-infrared absorption spectrum of the hair used for preparing the calibration curve is plotted on the prepared calibration curve, or the rational value is plotted on the second derivative axis. It is possible to know the relationship between the second derivative of the Fourier transform near-infrared absorption spectrum of hair and the rational value of the state of the hair, and the Fourier transform near-infrared absorption spectrum of the hair of the test sample. In PLS analysis, the plot of the second derivative of the wavelength at which the plot is identified as being relevant can be compared with these to calculate the plot of the test sample hair. At this time, a group representative value such as a median or an average value of the secondary differential values of the plot of the hair used for creating the calibration curve was simply plotted on the calibration curve or on the secondary differential value axis, and the test sample was prepared. It is also possible to plot the group representative values of the second-order differential values of the hairs and compare the group representative values with each other to calculate the rational value. Here, it should be noted that by measuring one Fourier transform near-infrared absorption spectrum, the friction feeling according to the discrimination method of the present invention,
That is, it is possible to calculate a number of positivity values of the state of the hair such as the degree of gloss (gloss) and the sensory value. In other words, it is possible to perform multifaceted evaluation of the state of the hair from one measurement result.
【００１１】かくして算出された毛髪の状態の示性値
は、毛髪用の化粧料の処理効果の指標として、或いは、
ダメージを受けた毛髪の回復を確認するなど、毛髪の状
態のモニタリングなどに、又、毛髪の状態にあわせた毛
髪用の化粧料の選択に使用することができる。この内、
毛髪用の化粧料の処理効果の指標として用いる場合に
は、毛髪の近赤外吸収スペクトルを化粧料の処理の前後
に測定しておき、これらのスペクトルデータの処理よ
り、処理前後の毛髪の状態の示性値を算出し、その変化
を効果の指標とすればよい。この様な毛髪用の化粧料の
処理効果の指標として、或いは、ダメージを受けた毛髪
の回復を確認するなど、毛髪の状態のモニタリングなど
に、又、毛髪の状態にあわせた毛髪用の化粧料の選択は
本発明の方法によって算出した毛髪の状態の示性値の代
わりに毛髪のフーリエ変換近赤外吸収スペクトルのPLS
分析で特定された波長の二次微分値そのものを用いるこ
ともできる。これは、毛髪の状態の示性値と毛髪のフー
リエ変換近赤外吸収スペクトルのPLS分析で特定された
波長の二次微分値との間に良好な相関関係が存在するか
らである。この様な毛髪のフーリエ変換近赤外吸収スペ
クトルの二次微分値の使用も本発明の技術的範囲に属す
る。[0011] The thus calculated hair condition indicator is used as an index of the treatment effect of the cosmetic for hair, or
It can be used for monitoring the condition of hair, such as confirming the recovery of damaged hair, and for selecting a cosmetic for hair according to the condition of hair. Of this,
When used as an index of the treatment effect of cosmetics for hair, the near-infrared absorption spectrum of the hair is measured before and after the treatment of the cosmetics, and the state of the hair before and after the treatment is determined by the processing of these spectrum data. The merit value of is calculated, and the change may be used as an index of the effect. As an index of the treatment effect of such a cosmetic for hair, or for monitoring the condition of hair such as confirming the recovery of damaged hair, or a cosmetic for hair that matches the condition of hair. The selection of the PLS of the Fourier transform near-infrared absorption spectrum of the hair in place of the directional data of the state of the hair calculated by the method
It is also possible to use the second derivative itself of the wavelength specified by the analysis. This is because there is a good correlation between the indicative value of the state of the hair and the second derivative of the wavelength specified by PLS analysis of the Fourier transform near infrared absorption spectrum of the hair. The use of such a second derivative of the Fourier transform near infrared absorption spectrum of hair is also within the technical scope of the present invention.
【００１２】[0012]
【実施例】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、これら実施例にの
み限定されないことは言うまでもない。EXAMPLES The present invention will be described in more detail below with reference to examples, but it goes without saying that the present invention is not limited to these examples.
【００１３】＜実施例１＞予め用意した状態の異なる３
種の毛髪をグロスメーターで光沢値を測定した。状態の
異なる毛髪は、毛髪を濃度の異なるチオグリコール酸で
処理することにより、調整した。調整方法は下記に示
す。同時にこの毛髪のフーリエ変換近赤外吸収スペクト
ル（波長４７００〜５０００ｃｍ−１ ）を測定し、二
次微分を行った。光沢値と二次微分値について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１に示す。これより、
グロスメーターによる光沢値との間には良好な相関関係
があることが判る。検量線上に光沢値をプロットすると
光沢値ごとのブロックが形成されるようになり、この検
量線を用いることにより、試験試料の毛髪のフーリエ変
換近赤外吸収スペクトルより光沢値を算出することがで
きることがわかる。又、高い相関係数より、本発明の鑑
別法は定量性にも優れることが判る。<Example 1> 3 prepared in different states
The gloss value of each kind of hair was measured with a gloss meter. Hair in different states was prepared by treating the hair with different concentrations of thioglycolic acid. The adjustment method is shown below. At the same time, the Fourier transform near-infrared absorption spectrum (wavelength: 4700 to 5000 cm −1) of this hair was measured, and the second derivative was performed. For the gloss value and the second derivative, PLS analysis was performed using Unscrambler II, and a calibration curve was prepared by PLS analysis. The calibration curve is shown in FIG. Than this,
It can be seen that there is a good correlation with the gloss value by the gloss meter. When plotting the gloss value on the calibration curve, a block is formed for each gloss value, and by using this calibration curve, the gloss value can be calculated from the Fourier transform near infrared absorption spectrum of the hair of the test sample. I understand. Further, the high correlation coefficient shows that the discrimination method of the present invention is excellent in quantification.
【００１４】＜実施例２＞実施例１と同様に、実施例１
で使用した毛髪を用いて、摩擦感テスターで測定した摩
擦感値との関係を調べた。検量線を図２に示す。この検
量線上の摩擦感値をプロットすると、摩擦感値ごとにブ
ロックを形成していることが判り、これを利用して、こ
の検量線を用いることにより、試験試料の毛髪のフーリ
エ変換近赤外吸収スペクトルより摩擦感値を算出するこ
とができることがわかる。<Embodiment 2> Similar to Embodiment 1, Embodiment 1
Using the hair used in 1., the relationship with the friction feeling value measured by the friction feeling tester was examined. The calibration curve is shown in FIG. When the frictional sensitivity values on this calibration curve were plotted, it was found that blocks were formed for each frictional sensitivity value.By utilizing this, by using this calibration curve, the Fourier transform near infrared of the test sample hair was calculated. It is understood that the friction feeling value can be calculated from the absorption spectrum.
【００１５】＜実施例３＞実施例１と同様に、実施例１
で使用した毛髪を用いて、専門パネラーの評価した評価
値（なめらかさ）との関係を調べた。検量線を図３に示
す。この検量線上のひょかちのプロットの分布は評価値
ごとに部録を形成していることが判る。これを利用し
て、この検量線を用いることにより、試験試料の毛髪の
フーリエ変換近赤外吸収スペクトルより官能評価値を算
出することができることがわかる。この様な官能評価に
於いては、通常ある程度の熟練が必要とされるが、本発
明の鑑別法によれば、どの様な人でも簡便に再現性の高
い評価が行えることは注目に値する。<Embodiment 3> Similar to Embodiment 1, Embodiment 1
Using the hair used in 1., the relationship with the evaluation value (smoothness) evaluated by a professional panelist was investigated. The calibration curve is shown in FIG. It can be seen that the distribution of the plots on the calibration curve forms a part of each evaluation value. By utilizing this, it can be seen that the sensory evaluation value can be calculated from the Fourier transform near infrared absorption spectrum of the hair of the test sample by using this calibration curve. In such sensory evaluation, some skill is usually required, but it is worth noting that any person can easily perform highly reproducible evaluation by the discrimination method of the present invention.
【００１６】＜実施例４＞実施例１の測定結果を、主成
分分析にかけた。使用したソフトウェアは実施例１と同
じアンスクランブラーＩＩを用いた。結果を図４に示
す。これより、更に鮮明にグロス値ごとのクラス分けが
されていることが判る。<Example 4> The measurement results of Example 1 were subjected to principal component analysis. The software used was the same unscrambler II as in Example 1. The results are shown in Fig. 4. From this, it can be seen that the gloss values are classified more clearly.
【００１７】＜実施例５＞実施例２の測定結果を、実施
例６と同様に主成分分析にかけた。使用したソフトウェ
アは実施例１と同じアンスクランブラーＩＩを用いた。
結果を図５に示す。これより、更に鮮明に摩擦感値ごと
のクラス分けがされていることが判る。<Example 5> The measurement results of Example 2 were subjected to principal component analysis in the same manner as in Example 6. The software used was the same unscrambler II as in Example 1.
Results are shown in FIG. From this, it can be seen that the friction values are classified more clearly.
【００１８】＜実施例６＞実施例３の測定結果を、実施
例６と同様に主成分分析にかけた。使用したソフトウェ
アは実施例１と同じアンスクランブラーＩＩを用いた。
結果を図６に示す。これより、更に鮮明に評価値ごとの
クラス分けがされていることが判る。<Example 6> The measurement results of Example 3 were subjected to principal component analysis in the same manner as in Example 6. The software used was the same unscrambler II as in Example 1.
Results are shown in FIG. From this, it can be seen that the evaluation values are classified more clearly.
【００１９】＜実施例７＞実施例１、４と同様の検討を
波長５１００〜５３００ｃｍ−１ に変えて同様の検討
を行った。結果を図７、８にしめす。これに於いても優
れた回帰性とグロス値ごとの分布性がみられるが、４７
００〜５０００ｃｍ−１ の場合程ではないことが判
る。<Embodiment 7> The same investigation as in Embodiments 1 and 4 was changed to wavelengths 5100 to 5300 cm-1, and the same investigation was carried out. The results are shown in FIGS. Even in this case, excellent regressivity and distribution for each gloss value are observed.
It can be seen that it is not as great as in the case of 00 to 5000 cm-1.
【００２０】＜実施例８＞実施例７と同様の検討を波長
４０００〜１２０００ｃｍ−１ に変えて同様の検討を
行った。結果を図９、１０にしめす。これに於いても優
れた回帰性とグロス値ごとの分布性がみられるが、４７
００〜５０００ｃｍ−１ の場合程ではないことが判
る。<Embodiment 8> The same examination as in Example 7 was changed to a wavelength of 4000 to 12000 cm-1, and the same examination was carried out. The results are shown in FIGS. Even in this case, excellent regressivity and distribution for each gloss value are observed.
It can be seen that it is not as great as in the case of 00 to 5000 cm-1.
【００２１】[0021]
【発明の効果】毛髪用の化粧料の選択や評価、毛髪の状
態の変化のモニタリングなどに有用な、毛髪の状態の鑑
別において、非進取的に鑑別を行う方法、取り分け、非
侵襲的な毛髪の鑑別法であって、定量性のある鑑別法を
提供することができる。[Effects of the Invention] In the differentiation of the hair condition, which is useful for selection and evaluation of cosmetics for hair, monitoring of changes in the hair condition, etc., a non-progressive method for distinguishing, especially, non-invasive hair. It is possible to provide a differentiating method having a quantitative property.
【図１】 実施例１の結果を示す図である。FIG. 1 is a diagram showing the results of Example 1.
【図２】 実施例２の結果を示す図である。FIG. 2 is a diagram showing the results of Example 2.
【図３】 実施例３の結果を示す図である。FIG. 3 is a diagram showing the results of Example 3.
【図４】 実施例４の結果を示す図である。FIG. 4 is a diagram showing the results of Example 4.
【図５】 実施例５の結果を示す図である。FIG. 5 is a diagram showing the results of Example 5.
【図６】 実施例６の結果を示す図である。FIG. 6 is a diagram showing the results of Example 6.
【図７】 実施例７の多変量解析（ＰＬＳ分析）結果を
示す図であるFIG. 7 is a diagram showing the results of multivariate analysis (PLS analysis) in Example 7.
【図８】 実施例７の主成分分析の結果を示す図であ
る。FIG. 8 is a diagram showing the results of principal component analysis of Example 7.
【図９】 実施例８の多変量解析（PLS分析）の結果を
示す図である。FIG. 9 is a diagram showing the results of multivariate analysis (PLS analysis) in Example 8.
【図１０】 実施例８の主成分分析の結果を示す図であ
る。FIG. 10 is a diagram showing the results of principal component analysis of Example 8.
Claims (8)
の異なる２種以上の毛髪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
量解析し、該分析結果を指標として、これと試験試料の
近赤外吸収スペクトルとを比較し、試験試料を鑑別する
ことを特徴とする、毛髪の状態の鑑別法。1. A method for differentiating the state of hair, wherein the near-infrared absorption spectra of two or more types of hair having different states are measured in advance, and the near-infrared absorption spectrum and the indication value of the state are multivariate. A method for differentiating a hair condition, which comprises analyzing and using the analysis result as an index and comparing this with a near-infrared absorption spectrum of a test sample to distinguish the test sample.
変換近赤外吸収スペクトル及び／又はダイオードアレー
検出器によるものであることを特徴とする、請求項１に
記載の毛髪の状態の鑑別法。2. The method for differentiating the hair condition according to claim 1, wherein the near-infrared absorption spectrum is obtained by Fourier transform near-infrared absorption spectrum and / or diode array detector.
分析であることを特徴とする、請求項１又は２に記載の
毛髪の状態の鑑別法。3. The method for differentiating a hair condition according to claim 1, wherein the multivariate analysis is multiple regression analysis or principal component analysis.
び／又はつややかさであることを特徴とする、請求項１
〜３何れか１項に記載の毛髪の状態の鑑別法。4. The expression item of the condition of the hair is smoothness and / or glossiness.
[3] The method for distinguishing the state of hair according to any one of [3] to [3].
よる測定値、摩擦感テスターによる測定値、つややかさ
の官能試験結果及びなめらかさの官能試験結果から選ば
れる１種乃至は２種以上である、請求項１〜４何れか１
項に記載の毛髪の状態の鑑別法。5. The one or more kinds of the indicator values of the condition of the hair are selected from a gloss meter measurement value, a friction tester measurement value, a gloss sensory test result and a smooth sensory test result. Yes, any one of claims 1 to 4.
The method for distinguishing the condition of hair according to the item.
が、４７００〜５０００ｃｍ−１ であることを特徴と
する、請求項１〜５何れか１項に記載の毛髪の状態の鑑
別法。6. The method for differentiating the hair condition according to claim 1, wherein the measurement wavelength region of the near infrared absorption spectrum is 4700 to 5000 cm −1.
髪用の化粧料による処理の前後に於ける毛髪の状態を、
請求項１〜６何れか１項に記載の毛髪の状態の鑑別法に
よって鑑別して得られる化粧料による毛髪の状態の変化
を指標とすることを特徴とする、毛髪用の化粧料の評価
法。7. In the evaluation of a cosmetic composition for hair, the condition of the hair before and after the treatment with the cosmetic composition for hair is
A method for evaluating cosmetics for hair, characterized in that a change in the state of the hair due to the cosmetic obtained by differentiating the hair according to any one of claims 1 to 6 is used as an index. .
求項１〜６何れか１項に記載の毛髪の状態の鑑別法で鑑
別された毛髪の状態を指標とすることを特徴とする、毛
髪の状態のモニタリング方法。8. A hair condition characterized by using the hair condition identified by the method for differentiating the hair condition according to any one of claims 1 to 6 as an index in the monitoring of the hair condition. Monitoring method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2002074219A JP3703438B2 (en) | 2002-03-18 | 2002-03-18 | Differentiation of hair condition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2002074219A JP3703438B2 (en) | 2002-03-18 | 2002-03-18 | Differentiation of hair condition |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| JP2003270138A true JP2003270138A (en) | 2003-09-25 |
| JP2003270138A5 JP2003270138A5 (en) | 2004-10-28 |
| JP3703438B2 JP3703438B2 (en) | 2005-10-05 |
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| JP2005287853A (en) * | 2004-04-01 | 2005-10-20 | Pola Chem Ind Inc | Evaluation method for hair |
| WO2005096938A1 (en) * | 2004-03-31 | 2005-10-20 | Pola Chemical Industries Inc. | Method of judging degree of hair damaging |
| US7820972B2 (en) | 2005-09-02 | 2010-10-26 | Pola Chemical Industries Inc. | Method of evaluating skin conditions and method of estimating skin thickness |
| JP2011036369A (en) * | 2009-08-10 | 2011-02-24 | Kao Corp | Hair evaluation system and hair evaluation method |
| WO2017057835A1 (en) * | 2015-09-30 | 2017-04-06 | (주)아모레퍼시픽 | Method for calculating ageing index of scalp and hair |
| DE102016222193A1 (en) * | 2016-11-11 | 2018-05-17 | Henkel Ag & Co. Kgaa | Method for determining a user-specific hair treatment I |
| DE102016223916A1 (en) * | 2016-12-01 | 2018-06-07 | Henkel Ag & Co. Kgaa | Method for determining a user-specific hair treatment III |
| US20190183232A1 (en) * | 2016-07-05 | 2019-06-20 | Henkel Ag & Co. Kgaa | Method for determining a user-specific hair treatment |
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- 2002-03-18 JP JP2002074219A patent/JP3703438B2/en not_active Expired - Fee Related
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| WO2005096938A1 (en) * | 2004-03-31 | 2005-10-20 | Pola Chemical Industries Inc. | Method of judging degree of hair damaging |
| EP1629775A1 (en) * | 2004-03-31 | 2006-03-01 | Pola Chemical Industries Inc. | Method of judging degree of hair damaging |
| EP1629775A4 (en) * | 2004-03-31 | 2006-11-15 | Pola Chem Ind Inc | Method of judging degree of hair damaging |
| KR100749692B1 (en) * | 2004-03-31 | 2007-08-17 | 포라가세이고오교오가부시끼가이샤 | Method of evaluating degree of hair damage |
| JPWO2005096938A1 (en) * | 2004-03-31 | 2008-02-28 | ポーラ化成工業株式会社 | Method for judging the degree of hair damage |
| JP2005287853A (en) * | 2004-04-01 | 2005-10-20 | Pola Chem Ind Inc | Evaluation method for hair |
| JP5306651B2 (en) * | 2005-09-02 | 2013-10-02 | ポーラ化成工業株式会社 | Method for determining skin condition and method for predicting skin thickness |
| US7820972B2 (en) | 2005-09-02 | 2010-10-26 | Pola Chemical Industries Inc. | Method of evaluating skin conditions and method of estimating skin thickness |
| JP2011036369A (en) * | 2009-08-10 | 2011-02-24 | Kao Corp | Hair evaluation system and hair evaluation method |
| WO2017057835A1 (en) * | 2015-09-30 | 2017-04-06 | (주)아모레퍼시픽 | Method for calculating ageing index of scalp and hair |
| KR20170038215A (en) * | 2015-09-30 | 2017-04-07 | (주)아모레퍼시픽 | Age evaluating method of scalp and hair |
| KR102406640B1 (en) * | 2015-09-30 | 2022-06-08 | (주)아모레퍼시픽 | Age evaluating method of scalp and hair |
| US20190183232A1 (en) * | 2016-07-05 | 2019-06-20 | Henkel Ag & Co. Kgaa | Method for determining a user-specific hair treatment |
| US20190192067A1 (en) * | 2016-07-05 | 2019-06-27 | Henkel Ag & Co. Kgaa | Method and device for determining a degree of damage of hair, and method for determining a user-specific hair treatment agent |
| US11534106B2 (en) | 2016-07-05 | 2022-12-27 | Henkel Ag & Co. Kgaa | Method for determining a user-specific hair treatment |
| US11980279B2 (en) * | 2016-07-05 | 2024-05-14 | Henkel Ag & Co. Kgaa | System and method for establishing a user-specific hair treatment |
| DE102016222193A1 (en) * | 2016-11-11 | 2018-05-17 | Henkel Ag & Co. Kgaa | Method for determining a user-specific hair treatment I |
| DE102016223916A1 (en) * | 2016-12-01 | 2018-06-07 | Henkel Ag & Co. Kgaa | Method for determining a user-specific hair treatment III |
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