Pre- and post-processing of spectral data for calibration using mutivariate analysis techniques

• US 20020010401A1
• Filed: 05/16/2001
• Published: 01/24/2002
• Est. Priority Date: 05/18/2000
• Status: Abandoned Application

• Current Assignee: Argose, Inc.
• Original Assignee: Argose, Inc.
• Inventors: Bushmakin, Andrew, Trepagnier, Pierre, Mansfield, James R.
• Application Number:
  US09/855,755
• Publication Number:
  US 20020010401A1
• Time in Patent Office: Days
• US Class Current: 600/476
• 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: G01N 21/31 Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry G01N21/72 takes precedence
• CPC Class Codes: G01N 21/6486 Measuring fluorescence of biological material, e.g. DNA, RNA, cells G01N21/6428 takes precedence

Abstract

This invention relates to a method for quantitating the relationship between an analyte level in in vivo tissue and the auto-fluorescent spectral characteristics in the tissue.

Claims

1. A method of quantitating a relationship between an analyte level in in vivo tissue and auto-fluorescent spectral characteristics in said tissue, comprising:

generating a single excitation wavelength or plurality of different excitation wavelengths of green to ultraviolet light;

irradiating the tissue with said light and measuring the intensity of the stimulated emission of the sample at a minimum of two different wavelengths of lower energy than the excitation light or at a plurality of wavelengths of lower energy than the excitation light;

applying a transformation to the wavelength data;

analyzing the transformed data; and

inverting the original transformation to yield analytical results in standard units.

2. The method of claim 1 wherein the analyte is glucose and the tissue is skin.

3. The method of claim 2 wherein relative transformations of glucose and spectra are selected from the group comprising the single-point transformations (g|s)_k=(G|S)_k−

(G|S)_N or (g|s)_k=(G|S)_k÷

(G|S)_N and the point-by-point transformations (g|s)_k=(G|S)_k−

(G|S)<sub>k−

1</sub> or (g|s)_k=(G|S)_k÷

(G|S)<sub>k−

1</sub>.

4. A method of quantitating a relationship between an analyte level in tissue and an absorption spectrum of said tissue, wherein a concentration of said analyte is not being directly measured, but rather indirectly inferred through its effect on components of said tissue, said method comprising:

irradiating the tissue with electromagnetic radiation and measuring the absorption spectrum of said electromagnetic radiation;

applying a relative transformation to the spectral data and another relative transformation to the analyte, the relative transformation in each case being selected from a group comprising either point-by-point or single-point relative transformations;

analyzing the transformed data using multivariate techniques; and

inverting the original transformation to yield analytical results in standard units.

5. The method of claim 4 wherein the electromagnetic radiation is near-ultraviolet to visible light.

6. The method of claim 4 wherein the electromagnetic radiation is visible to near-infrared light.

7. The method of claim 4 wherein the electromagnetic radiation is infrared radiation.

8. A method of quantitating a relative relationship between a set of absolute values, G_i, and a set of corresponding experimental spectra, S_i, wherein each respective pair (G_i, S_i) within the set are acquired simultaneously, comprising the steps of:

transforming two or more of said pairs according to an algorithm into one or more transformed pairs (g_k, S_k);

analyzing the set of transformed pairs (g_k, s_k) using an analysis technique to determine a first statistical model relating g_k to s_k; and

inverting said first statistical model relating g_k to s_k according to said algorithm to create a second statistical model relating a set of experimental values S_k to a set of absolute values G_k,

wherein said second statistical model is used to predict an absolute value of an analyte from an experimental spectrum taken of said analyte.

9. The method of claim 8 wherein said algorithm comprises a single point process.

10. The method of claim 9 wherein said single point process is selected from the group consisting of:

(g|s)_k=(G|S)_k−

(G|S)_N or (g|s)_k=(G|S)_k÷

(G|S)_N.

11. The method of claim 8 wherein said algorithm comprises a point-by-pint process.

12. The method of claim 11 wherein said point-by-point process is selected from the group consisting of:

(g|s)_k=(G|S)_k−

(G|S)<sub>k−

1</sub> or (g|s)_k=(G|S)_k÷

(G|S)<sub>k−

1</sub>.

13. The method of claim 8 further comprising the step of smoothing or averaging said pairs prior to transforming.

14. The method of claim 13 wherein said averaging comprises replacing two or more of said pairs with their average.

15. The method of claim 13 wherein said smoothing comprises applying a running filter so that each data point is replaced by a weighted sum of nearby points.

16. The method of claim 15 wherein said running filter is a 5-point Chebyshev filter.

17. The method of claim 8 wherein said analysis technique is a multivariate analysis technique.

18. The method of claim 17 wherein said multivariate analysis technique comprises partial least squares analysis.

19. The method of claim 8 wherein said analyte is glucose and said experimental spectrum comprises two or more wavelengths of light emitted from a sample comprising said glucose.

20. The method of claim 19 wherein said sample is stimulated by excitation light comprising one or more wavelengths in a range of green to ultraviolet light.