System And Method For Unmixing Spectroscopic Observations With Nonnegative Matrix Factorization

• US 20090248320A1
• Filed: 03/27/2009
• Published: 10/01/2009
• Est. Priority Date: 03/27/2008
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Nellcor Puritan Bennett LLC (Medtronic Plc)
• Inventors: Pav, Steven E.
• Granted Patent: US 8,140,272 B2
• Time in Patent Office: Days
• US Class Current: 702/23
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G06F 18/2134 based on separation criteria, e.g. independent component analysis

Abstract

Systems and methods for unmixing spectroscopic data using nonnegative matrix factorization during spectrographic data processing are provided according to various embodiments. In an embodiment, a method of processing spectrographic data may include receiving optical absorbance data associated with a sample and iteratively computing values for component spectra using nonnegative matrix factorization. The values for component spectra may be iteratively computed until optical absorbance data is approximately equal to a Hadamard product of a pathlength matrix and a matrix product of a concentration matrix and a component spectra matrix. The method may also include iteratively computing values for pathlength using nonnegative matrix factorization, in which pathlength values may be iteratively computed until optical absorbance data is approximately equal to a Hadamard product of the pathlength matrix and the matrix product of the concentration matrix and the component spectra matrix.

Claims

1. A method of processing spectrographic data comprising:

receiving optical absorbance data associated with a sample; and

iteratively computing values for component spectra based at least in part upon nonnegative matrix factorization.

2. The method of claim 1, wherein iteratively computing values for component spectra using nonnegative matrix factorization comprises iteratively computing values for a component spectra matrix until optical absorbance data is approximately equal to a Hadamard product of a pathlength matrix and a matrix product of a concentration matrix and the component spectra matrix.

3. The method of claim 1, comprising iteratively computing values for pathlength based at least in part upon nonnegative matrix factorization.

4. The method of claim 3, wherein iteratively computing values for pathlength using nonnegative matrix factorization comprises iteratively computing values for a pathlength matrix until optical absorbance data is approximately equal to a Hadamard product of the pathlength matrix and a matrix product of a concentration matrix and a component spectra matrix.

5. The method of claim 1, wherein iteratively computing values for component spectra using nonnegative matrix factorization comprises limiting total pathlength degrees of freedom.

6. The method of claim 5, wherein total pathlength degrees of freedom are generally limited by approximating pathlength as a linear combination of basis functions.

7. The method of claim 1, wherein receiving optical absorbance data associated with the sample comprises receiving optical absorbance data associated with a multicomponent sample having absorptive interferents.

8. The method of claim 7, comprising iteratively computing values inteiferent spectra and interferent concentration using nonnegative matrix factorization.

9. The method of claim 8, wherein iteratively computing values for interferent spectra and interferent concentration using nonnegative matrix factorization comprises iteratively computing values for an inteiferent spectra matrix and an interferent concentration matrix until optical absorbance data is approximately equal to a Hadamard product of a pathlength matrix and a matrix sum of a matrix product of a concentration matrix and a component spectra matrix and a matrix product of the interferent spectra matrix and the interferent concentration matrix.

10. The method of claim 1, comprising receiving values of component concentrations.

11. A spectroscopy system comprising:

an electromagnetic radiation source;

an electromagnetic radiation detector configured to measure electromagnetic radiation from the source; and

processing circuitry configured to receive optical absorbance data associated with a sample disposed between the source and the detector and to iteratively compute values for sample component concentrations or sample component spectra using nonnegative matrix factorization,

12. The system of claim 11, wherein the processing circuitry is configured to iteratively compute values for component concentrations until optical absorbance data is approximately equal to a Hadamard product of a pathlength matrix and a matrix product of a component concentration matrix and a component spectra matrix.

13. The system of claim 11, wherein the processing circuitry is configured to iteratively compute values for component spectra until optical absorbance data is approximately equal to a Hadamard product of a pathlength matrix and a matrix product of a concentration matrix and the component spectra matrix.

14. The system of claim 11, wherein the processing circuitry is configured to receive optical absorbance data associated with a multicomponent sample.

15. The system of claim 11, wherein the processing circuitry is configured to receive optical absorbance data associated with a heterogeneous or turbid multicomponent sample.

16. A method of processing spectrographic data comprising:

receiving electromagnetic absorbance data associated with a heterogeneous or turbid multicomponent sample; and

iteratively computing values for sample component concentrations or sample component spectra such that only positive values are computed.

17. The method of claim 16, wherein iteratively computing values for sample component concentrations or sample component spectra comprises using nonnegative matrix factorization.

18. The method of claim 17, wherein iteratively computing values for sample component concentrations or sample component spectra comprises using a weighted Frobenius norm having a weighting matrix equal to the Hadamard inverse of the Hadamard square of a matrix of component spectra.

19. The method of claim 17, wherein iteratively computing values for sample component concentrations or sample component spectra comprises using a weighted Frobenius norm having a weighting matrix chosen based on an iteratively reweighted least squares approach.