Detection of pathogenic microorganisms using fused sensor data

• US 7,945,393 B2
• Filed: 12/19/2008
• Issued: 05/17/2011
• Est. Priority Date: 01/10/2002
• Status: Active Grant

• Current Assignee: Chemimage Corporation
• Original Assignee: Chemimage Corporation
• Inventors: Treado, Patrick J, Schweitzer, Robert C
• Primary Examiner(s): Smith; Carolyn L.
• Application Number:
  US12/339,805
• Publication Number:
  US 20090163369A1
• Time in Patent Office: 879 Days
• Field of Search: None
• US Class Current: 702/19
• CPC Class Codes: G06V 20/698 Matching; Classification
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures
• CPC Class Codes: Y10T 436/24 Nuclear magnetic resonance, electron spin resonance or other spin effects or mass spectrometry

Abstract

A system and method to search spectral databases to identify unknown materials, specifically pathogenic microorganisms. A library is provided, having sublibraries containing reference data sets of known materials and test data sets, both generated by at least one spectroscopic data generating instrument. For each test data set, each sublibrary associated with the instrument used is searched. A set of scores for each searched sublibrary is produced, representing the likelihood of a match between the reference data set and test data set. Relative probability values are calculated for each searched sublibrary. All relative probability values are fused producing a set of final probability values, used in determining whether the unknown material is represented through a known material in the library. The known material represented in the libraries having the highest final probability value is reported, if the highest final probability value is greater than or equal to the minimum confidence value.

Claims

1. A method comprising:

a) providing a library having a plurality of sublibraries, wherein each said sublibrary contains a plurality of reference data sets generated by at least one of a corresponding one of a plurality of spectroscopic data generating instruments associated with said sublibrary, and wherein each reference data set characterizes a corresponding known pathogenic microorganism;

b) obtaining a plurality of test data sets characteristic of an unknown material, wherein each test data set is generated by at least one of the plurality of spectroscopic data generating instruments;

c) instructing a processor to perform the following;

d) for each test data set, searching each sublibrary associated with the spectroscopic data generating instrument used to generate said test data set, to thereby produce a corresponding set of scores for each searched sublibrary, wherein each score in said set of scores indicates a likelihood of a match between a corresponding one of said plurality of reference data sets in said searched sublibrary and said test data set;

e calculating a set of relative probability values for each searched sublibrary based on the corresponding set of scores for each searched sublibrary; and

f fusing all relative probability values using Bayesian fusion to thereby produce a set of final probability values to be used in determining whether said unknown material is represented through a corresponding known pathogenic microorganism characterized in the library.

2. The method of claim 1 wherein said plurality of test data sets are obtained by:

a) illuminating a sample containing said unknown material with substantially monochromatic light to thereby produce Raman scattered photons;

b) illuminating said sample with broadband light to thereby produce scattered photons;

c) receiving said Raman scattered photons and said scattered photons substantially simultaneously from a plurality of spatial locations of said sample using a fiber array spectral translator; and

d) detecting said Raman scattered photons and said scattered photons and providing therefrom a plurality of spatially accurate wavelength resolved spectra of said sample.

3. The method of claim 1 wherein said plurality, of test data sets are obtained by:

a) illuminating a sample containing said unknown material with substantially monochromatic light to thereby produce Raman scattered photons;

b) illuminating said sample with broadband light to thereby produce scattered photons;

c) receiving said Raman scattered photons and said scattered photons substantially simultaneously from a plurality of spatial locations of said sample; and

d) detecting said Raman scattered photons and said scattered photons and providing therefrom a plurality of spatially accurate wavelength resolved spectra of said sample.

4. The method of claim 1 wherein said plurality of test data sets are obtained by:

a) illuminating a sample to thereby produce scattered photons;

b) receiving said scattered photons using a fiber array spectral translator; and

c) detecting said scattered photons and providing therefrom a plurality of spatially accurate wavelength resolved spectra of said sample.

5. The method of claim 1 wherein said plurality of test data sets are obtained by:

a) illuminating a sample to thereby produce scattered photons;

b) receiving said scattered photons; and

c) detecting said scattered photons and providing therefrom a plurality of spatially accurate wavelength resolved spectra of said sample.

6. The method of claim 2 further comprising obtaining a spatially accurate wavelength resolved image of said sample.

7. The method of claim 3 further comprising obtaining a spatially accurate wavelength resolved image of said sample.

8. The method of claim 4 further comprising obtaining a spatially accurate wavelength resolved image of said sample.

9. The method of claim 5 further comprising obtaining a spatially accurate wavelength resolved image of said sample.

10. The method of claim 1 further comprising applying a weighting factor to each set of relative probability values, to thereby produce a set of weighted probability values for each searched sublibrary.

11. The method of claim 1 wherein said spectroscopic data generating instrument comprises at least one of the following:

a Raman spectrometer, an infrared spectrometer, an x-ray diffractometer, an energy dispersive x-ray analyzer, a mass spectrometer, a microscope, an image generating instrument, a chromatographic analyzer, a charge-coupled detector, and a fluorescence spectrometer.

12. The method of claim 1 wherein said test data comprises at least one of the following:

a Raman spectrum characteristic of said unknown, a Raman image characteristic of said unknown, an infrared spectrum characteristic of said unknown, a fluorescence image characteristic of said unknown, a fluorescence spectrum characteristic of said unknown, an infrared spectrum characteristic of said unknown, an infrared image characteristic of said unknown, an x-ray diffraction pattern characteristic of said unknown, an energy dispersive x-ray spectrum characteristic of said unknown, and a mass spectrum characteristic of said unknown.

13. The method of claim 1 further comprising:

a) providing a text description of each known pathogenic microorganism represented in the plurality of sublibraries;

b) individually searching each sublibrary, using a text query, that compares the text query to the text description of each known pathogenic microorganism to thereby produce a match answer or no match answer for each known pathogenic microorganism; and

c) removing the reference data set, from each sublibrary, for each known pathogenic microorganism producing the no match answer.

14. The method of claim 1 further comprising:

a) providing an image sublibrary containing a plurality of reference images generated by an image generating instrument associated with said image sublibrary;

b) wherein each reference image characterizes a corresponding known pathogenic microorganism, obtaining an image test data set characterizing an unknown material, wherein the image test data set is generated by said image generating instrument;

c) comparing the image test data set to the plurality of reference images; and

d) in accordance with said comparing, producing a match answer or a no match answer for each known pathogenic microorganism.

15. The method of claim 1 further comprising:

a) obtaining a spectra test data set characterizing an unknown material, wherein the spectra test data set is generated by said spectra generating instrument;

b) comparing the spectra test data set to the plurality of reference spectra; and

c) in accordance with said comparing, producing a match answer or a no match answer for each known pathogenic microorganism.

16. The method of claim 15, wherein for each known pathogenic microorganism producing a match answer, identifying one or more of the following:

a strain of said known pathogenic microorganism and a species of said known pathogenic microorganism.

17. The method of claim 1, wherein said reference data set comprises a plurality of reference spectra.

18. The method of claim 17 wherein said plurality of reference data sets are generated by at least two different of the corresponding plurality of spectroscopic data generating instruments associated with said sublibrary.

19. The method of claim 1, wherein said reference data set comprises a plurality of reference spectra.

20. The method of claim 19, wherein said plurality of reference spectra are generated by one corresponding plurality of spectroscopic data generating instruments associated with said sublibrary.

21. The method of claim 1 wherein said fusion is achieved by Bayesian fusion.

22. The method of claim 1 wherein the pathogenic microorganism is selected from the group consisting of filoviruses, naviruses, alphaviruses, and combinations thereof.

23. The method of claim 1 wherein the pathogenic microorganism is selected from the group of microorganisms consisting of protozoa, cryptosporidia microorganisms, Escherichia coli, Escherichia coli 157 microorganisms, Plague (Yersinia pestis), Smallpox (variola major), Tularemia (Francisella tularensis), Brucellosis (Brucella species), Clostridium perfringens, Salmonella, Shigella, Glanders (Burkholderia mallei), Melioidosis (Burkholderia pseudomallei), Psittacosis (Chlamydia psittaci), Q fever (Coxiella burnetil), Typhus fever (Rickettsia prowazekii), Vibrio cholerae, and combinations thereof.

24. The method of claim 1 wherein the pathogenic microorganism is selected from the group of bacteria consisting of Giardia, Candida albicans, Enterococcus faecalis, Staphylococcus epidermidis, Enterobacter aerogenes, Corynebacterium diphtheriae, Pseudomonas aeruginosa, Acinetobacter calcoaceticus, Klebsiella pneumoniae, and Serratia marcescens, and combinations thereof.

25. The method of claim 1 wherein the pathogenic microorganism is selected from the group fungus consisting of Microsporum audouini, Microspotum canis, Microsporum gypseum, Trichophyton mentagrophytes var. mentagrophytes, Trichophyton mentagrophytes var. interdigitale, Trichophyton rubrum, Trichophyton tonsurans, Trichophyton verrucosum, and Epidermophytum floccosum, and combinations thereof.

26. The method of claim 1 wherein the pathogenic microorganism is selected from the group consisting of:

influenza A, influenza B, Epstein Barr virus, Group A streptococcus, Group B streptococcus, and combinations thereof.

27. The method of claim 1 wherein the pathogenic microorganism is Staphylococcus aureus.

28. The method of claim 1 wherein the pathogenic microorganism is methicillin-resistant Staphylococcus aureus.

29. The method of claim 1 further comprising analyzing patterns characteristic of the pathogenic microorganism to determine viability of the pathogenic microorganism.