Method for quantification of stratum corneum hydration using diffuse reflectance spectroscopy

• US 6,442,408 B1
• Filed: 09/25/2000
• Issued: 08/27/2002
• Est. Priority Date: 07/22/1999
• Status: Expired due to Fees

• Current Assignee: GLT Acquisition Corporation
• Original Assignee: Instrumentation Metrics Inc.
• Inventors: Blank, Thomas, Wenzel, Brian J., Ruchti, Timothy L., Monfre, Stephen L., Rennert, Jessica, Grochocki, Frank, Meissner, Ken
• Primary Examiner(s): WINAKUR, ERIC FRANK
• Application Number:
  US09/669,781
• Time in Patent Office: 701 Days
• Field of Search: 600/310, 600/322, 600/473, 600/475, 356/303, 250/339.01, 250/339.07, 250/339.09, 250/339.1, 250/339.11, 250/340, 250/341.5, 250/341.8
• US Class Current: 600/310
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0233 Optical standards
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/441 Skin evaluation, e.g. for skin disorder diagnosis
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G01N 21/274 Calibration, base line adjustment, drift correction
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 21/4785 Standardising light scatter apparatus; Standards therefor
• CPC Class Codes: G01N 21/49 within a body or fluid

Abstract

An apparatus and method for non-invasively quantifying the hydration of the stratum corneum of a living subject utilizes in vivo spectral measurements made by irradiating skin tissue with near infrared (NIR) light. The apparatus includes a spectroscopic instrument in conjunction with a subject interface. The resulting NIR absorption spectra are passed to an analyzer for further processing, which includes detecting and eliminating invalid spectral measurements, and preprocessing to increase the signal-to-noise ratio. Finally, a calibration model developed from an exemplary set of measurements is applied to predict the SC hydration for the sample. The method of SC hydration measurement provides additional information about primary sources of systematic tissue variability, namely, the water content of the epidermal layer of skin and the penetration depth of the incident light. The stratum corneum hydration measurement is therefore suitable for further spectral analysis and the quantification of biological and chemical compounds, such as blood analytes.

Claims

1. A method for quantifying hydration of living tissue non-invasively, based on in vivo NIR spectral measurements, comprising the steps of:

providing an apparatus for measuring NIR absorbance spectra at a tissue measurement site;

providing a calibration set of exemplary samples and independent measurements, wherein a prediction model is developed from said calibration set;

providing a living subject and selecting a tissue measurement site thereon;

measuring an NIR absorbance spectrum at said tissue measurement site using said apparatus;

detecting outliers, wherein said outliers comprise invalid measurements;

preprocessing said measured spectrum, said preprocessing step comprising transformations that attenuate noise and instrumental variation without affecting the signal of interest; and

predicting hydration at said tissue measurement site based on said preprocessed spectrum.

2. The method of claim 1, wherein said measurements are made using any of transmissive, diffuse reflectance and other methods, and wherein parameters for said method are determined based on information provided by said calibration set.

3. The method of claim 1, wherein said spectrum is denoted by a vector mε

^N of absorbance values pertaining to a set of N wavelengths λ

ε

^N spanning a wavelength region of approximately 700 to 2500 nm.

4. The method of claim 3, wherein said outlier detection step comprises the steps of:

performing a principal components analysis (PCA) of said measured spectrum; and

analyzing resulting residuals.

5. The method of claim 4, wherein said PCA comprises projecting said measured spectrum m onto five eigenvectors contained in a matrix o that were previously developed through a PCA of absorbance spectra from said exemplary data set, wherein the calculation is given by:

${\mathrm{xpc}}_0=\underset{k=1}{\overset{5}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{mo}}_k,$

wherein a one by five vector of scores is produced, xpc₀, where o_k is the k^th column of the matrix o.

6. The method of claim 5, wherein the residual, q, is determined according to:

7. The method of claim 1, wherein said invalid measurements result from any of instrument problems, poor sampling technique, and subjects outside of the calibration data set.

8. The method of claim 1, wherein said preprocessing transformations include any of:

scaling, normalization, smoothing, and filtering.

9. The method of claim 8, wherein a preprocessed measurement is determined according to:

10. The method of claim 1, wherein said prediction step includes any of multiple least squares regression (MLR), principle component regression (PCR), and partial least squares regression (PLR) analysis wherein the measurement yε

^N is processed according to;

11. The method of claim 1, wherein said apparatus comprises an energy source, said energy source comprising a plurality of LED'"'"'s, each of said LED'"'"'s emitting energy at a different targeted wavelength within a wavelength range of approximately 700-2500 nm;

a sample probe head, said sample probe head comprising a subject interface and adapted for being substantially in contact with said tissue measurement site and delivering NIR energy emitted by said energy source to said tissue measurement site; and

a reference probe head, said reference probe head delivering NIR energy emitted by said energy source to an internal reference standard having known spectral characteristics.

12. The method of claim 11, wherein said tissue measurement site comprises an area of the skin of said living subject.

13. The method of claim 12, wherein the absorbance spectrum is calculated by:

 

m=−

log R/R_o,

where m is the absorbance spectrum, and R is an intensity signal from said sample probe head, R₀ is an intensity signal from said reference probe head.

14. The method of claim 13, wherein said spectrum, m, is analyzed to detect outliers, said outliers comprising invalid measurements, or readings outside the range of said prediction model.

15. The method of claim 14, wherein said spectrum is preprocessed, said preprocessing step comprising:

multiplicative scatter correction (MSC), wherein said spectrum is processed through a rotation that fits it to a reference spectrum {overscore (m)} determined from said calibration set; and

mean centering.

16. The method of claim 15, wherein said MSC step comprises the steps of:

fitting said spectrum via linear regression according to;

17. The method of claim 16, wherein said mean centering step comprises the steps of:

calculating a mean for each LED absorbance from said calibration set;

subtracting said mean from each LED absorbance in the measured spectrum.

18. The method of claim 12, wherein said prediction model is a multiple linear regression (MLR) model for predicting Stratum Corneum hydration.

19. The method of claim 18, wherein the prediction calculation is given by:

20. The method of claim 19, wherein the coefficients are calculated by:

21. The method of claim 1, further comprising the step of developing said prediction model using factor-based analytical methods, wherein a set of abstract features is developed that is capable of representing spectral variation related to tissue hydration.

22. The method of claim 21, wherein said developing step comprises the steps of:

providing NIR absorbance spectra;

selecting wavelengths from said spectra, wherein said spectra are sub-divided into one or more regions according to wavelength;

preprocessing and normalizing said spectra, wherein spectral variation related to tissue hydration is enhanced;

projecting said measurements onto one or more sets of previously determined factors, said factors comprising eigenvectors, to determine scores, wherein said scores constitute extracted features; and

subjecting said scores to a prediction procedure, said procedure comprising any of linear discriminant analysis, SIMCA, k nearest neighbor, fuzzy classification, and artificial neural networks.