초록 ▼

A sensor (200, 900) comprising an illuminator (212, 500, 804, 832, 858, 904), a receiver (216, 400, 420, 460, 480, 808, 836, 862, 924) and an analyzer (240) for detecting and identifying an analyte having a characteristic absorption band that is present in a sample region (208, 812, 824, 874, 922).

A sensor (200, 900) comprising an illuminator (212, 500, 804, 832, 858, 904), a receiver (216, 400, 420, 460, 480, 808, 836, 862, 924) and an analyzer (240) for detecting and identifying an analyte having a characteristic absorption band that is present in a sample region (208, 812, 824, 874, 922). The illuminator includes an illumination source (220) for illuminating the sample region with spectral energy across at least a portion of the characteristic absorption band. The receiver includes a detector (228, 404, 424, 460, 484, 866, 928) for sensing predetermined portions of the spectral energy band and for creating a sample spectral data vector (236). The analyzer uses the spectral data vector and known characteristic data to detect and identify the analyte.

대표청구항 ▼

What is claimed is: 1. A system for detecting an analyte having a characteristic absorption band, comprising: a) an illuminator operatively configured to emit a spectral energy band across at least a portion of the characteristic absorption band of the analyte, said illuminator consisting essential

What is claimed is: 1. A system for detecting an analyte having a characteristic absorption band, comprising: a) an illuminator operatively configured to emit a spectral energy band across at least a portion of the characteristic absorption band of the analyte, said illuminator consisting essentially of one or more broadband quantum-cascade lasers for providing said spectral energy band across a broadband of wavelengths; and b) a receiver operatively configured to selectively sense a plurality of predetermined sub-bands of said spectral energy band simultaneously. 2. A system according to claim 1, wherein said receiver comprises: a) a broadband detector array having a plurality of detecting regions; and b) a filter in operative relationship with said detector array so that each of said plurality of sensing regions receives a corresponding respective one of said plurality of predetermined sub-bands of said spectral energy band. 3. A system according to claim 2, wherein said filter comprises a linear variable interference filter. 4. A system according to claim 3, wherein said linear variable interference filter is applied to said broadband sensor array. 5. A system according to claim 2, wherein said filter comprises a diffraction grating. 6. A system according to claim 2, wherein said filter comprises a tunable non-dispersive filter. 7. A system according to claim 6, wherein said tunable filter is a stacked electro-optic tunable filter. 8. A system according to claim 6, wherein said tunable filter is applied to said broadband sensor array. 9. A system according to claim 1, wherein said receiver comprises a plurality of narrowband detectors each operatively configured to sense a corresponding respective one of said plurality of predetermined sub-bands of said spectral energy band. 10. A system according to claim 1, further comprising an analyzer operatively coupled to said receiver, said analyzer operatively configured to identify the analyte. 11. A system according to claim 10, wherein said analyzer is operatively configured to identify the analyte using a canonical vector analysis. 12. A system according to claim 10, wherein said analyzer is operatively configured to identify the analyte using least squares fit to known spectra. 13. A system according to claim 10, wherein the analyte comprises a plurality of particles and said analyzer is operatively configured to determine the size of the plurality of particles simultaneously with one another and with the system spaced from the analyte. 14. A system according to claim 13, wherein said analyzer is operatively configured to determine the size of the plurality of particles based on Rayleigh scattering. 15. A system according to claim 1, further comprising a communications device for communicating with at least one of the following: a like system and a remote computer. 16. A method of detecting and identifying an analyte having a characteristic absorption band, comprising the steps of: a) illuminating a region suspected of containing the analyte with a spectral energy band across at least a portion of the characteristic absorption band of the analyte using an illuminator consisting essentially of one or more broadband quantum cascade lasers so as to provide said spectral energy band across a broadband of wavelengths; and b) selectively sensing a plurality of predetermined portions of said spectral energy band simultaneously so as to create sample data. 17. A method according to claim 16, further comprising the step of generating a sample spectral data vector from said sample data. 18. A method according to claim 17, further comprising the step of analyzing said sample data vector relative to data for known groups of analyte. 19. A method of identifying a biological classification of bacteria present in a sample, comprising: a) illuminating a sample with a spectral energy band across at least a portion of the characteristic absorption band of the analyte using an illuminator consisting essentially of one or more broadband quantum cascade (QC) lasers so as to provide said spectral energy band across a broadband of wavelengths; b) collecting spectral data regarding said sample using a detector at a range of greater than two meters; and c) determining the biological classification of the bacteria as a function of said spectral data. 20. A method according to claim 19, wherein the spectral data comprises a plurality of spectral data points and step b) includes collecting said spectral data points simultaneously with one another. 21. A method according to claim 19, wherein step a) comprises illuminating the sample using a Fabry-Perot QC laser. 22. A method according to claim 19, wherein said determining of the biological classification of the bacteria includes identifying a bacterial species of the bacteria. 23. A method according to claim 19, wherein the bacteria includes bacteria of differing biological classifications, the method further comprising performing a multivariate analysis of said spectral data so as to distinguish between the differing biological classifications.