Abstract

We describe a multivariate optical computer that can implement multiple spectral filters simultaneously. By parallel detection of multiple outputs, our proposed approach is capable of identifying more than two spectra simultaneously, and therefore could significantly speed up spectrum recognition based on optical computing. We demonstrate our approach by recognizing two rare-earth-doped glass samples and a third white light sample spectrum with a fidelity of at least 0.83.

© 2014 Optical Society of America

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References

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    [Crossref]
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    [Crossref]
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    [Crossref] [PubMed]
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    [Crossref] [PubMed]
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    [Crossref]
  19. M. Bottema, W. Plummer, and J. Strong, “Water vapor in the atmosphere of Venus,” Astrophys. J. 139, 1021 (1964).
    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
  23. S. P. Love, “Programmable matched filter and Hadamard transform hyperspectral imagers based on micromirror arrays,” Proc. SPIE 7210, 721007 (2009).
    [Crossref]
  24. A. Wuttig and R. Riesenberg, “Sensitive Hadamard transform imaging spectrometer with a simple MEMS,” Proc. SPIE 4881, 167–178 (2003).
    [Crossref]
  25. D. M. Haaland and E. V. Thomas, “Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information,” Anal. Chem. 60, 1193–1202 (1988).
    [Crossref]
  26. R. G. Baraniuk, “Compressive sensing [lecture notes],” IEEE Signal Process. Mag. 24, 118–121 (2007).
    [Crossref]
  27. M. E. Gehm, S. T. McCain, N. P. Pitsianis, D. J. Brady, P. Potuluri, and M. E. Sullivan, “Static two-dimensional aperture coding for multimodal, multiplex spectroscopy,” Appl. Opt. 45, 2965–2974 (2006).
    [Crossref] [PubMed]
  28. R. M. Willett, M. E. Gehm, and D. J. Brady, “Multiscale reconstruction for computational spectral imaging,” Proc. SPIE 6498, 64980L (2007).
    [Crossref]

2013 (1)

G. T. Buzzard and B. J. Lucier, “Optimal filters for high-speed compressive detection in spectroscopy,” Proc. SPIE 8657, 865707 (2013).
[Crossref]

2012 (1)

D. S. Wilcox, G. T. Buzzard, B. J. Lucier, P. Wang, and D. Ben-Amotz, “Photon level chemical classification using digital compressive detection,” Anal. Chim. Acta 755, 17–27 (2012).
[Crossref] [PubMed]

2011 (4)

Z. J. Smith, S. Strombom, and S. Wachsmann-Hogiu, “Multivariate optical computing using a digital micromirror device for fluorescence and Raman spectroscopy,” Opt. Express 19, 16950–16962 (2011).
[Crossref] [PubMed]

D. B. Turner, K. W. Stone, K. Gundogdu, and K. A. Nelson, “Invited article: The coherent optical laser beam recombination technique (COLBERT) spectrometer: Coherent multidimensional spectroscopy made easier,” Rev. Sci. Instrum. 82, 081301 (2011).
[Crossref] [PubMed]

C. W. Freudiger, W. Min, G. R. Holtom, B. Xu, M. Dantus, and X. S. Xie, “Highly specific label-free molecular imaging with spectrally tailored excitation-stimulated Raman scattering (STE-SRS) microscopy,” Nature Photon. 5, 103–109 (2011).
[Crossref]

B. M. Davis, A. J. Hemphill, D. Cebeci Maltaş, M. A. Zipper, P. Wang, and D. Ben-Amotz, “Multivariate hyper-spectral Raman imaging using compressive detection,” Anal. Chem. 83, 5086–5092 (2011).
[Crossref] [PubMed]

2010 (1)

C. Gu, X. Yang, J. Zhang, R. Newhouse, and L. Cao, “Fiber sensors for molecular detection,” Proc. SPIE 7851, 785105 (2010).
[Crossref]

2009 (3)

N. Goldstein, P. Vujkovic-Cvijin, M. Fox, B. Gregor, J. Lee, J. Cline, and S. Adler-Golden, “DMD-based adaptive spectral imagers for hyperspectral imagery and direct detection of spectral signatures,” Proc. SPIE 7210, 721008 (2009).
[Crossref]

S. P. Love, “Programmable matched filter and Hadamard transform hyperspectral imagers based on micromirror arrays,” Proc. SPIE 7210, 721007 (2009).
[Crossref]

L. Cao and C. Gu, “Matched spectral filter based on reflection holograms for analyte identification,” Appl. Opt. 48, 6973–6979 (2009).
[Crossref] [PubMed]

2008 (1)

2007 (2)

R. G. Baraniuk, “Compressive sensing [lecture notes],” IEEE Signal Process. Mag. 24, 118–121 (2007).
[Crossref]

R. M. Willett, M. E. Gehm, and D. J. Brady, “Multiscale reconstruction for computational spectral imaging,” Proc. SPIE 6498, 64980L (2007).
[Crossref]

2006 (2)

M. E. Gehm, S. T. McCain, N. P. Pitsianis, D. J. Brady, P. Potuluri, and M. E. Sullivan, “Static two-dimensional aperture coding for multimodal, multiplex spectroscopy,” Appl. Opt. 45, 2965–2974 (2006).
[Crossref] [PubMed]

N. Uzunbajakava, P. de Peinder, G. W. ’t Hooft, and A. T. M. van Gogh, “Low-cost spectroscopy with a variable multivariate optical element,” Anal. Chem. 78, 7302–7308 (2006).
[Crossref] [PubMed]

2005 (1)

Z. Li, D. Psaltis, W. Liu, W. R. Johnson, and G. Bearman, “Volume holographic spectral imaging,” Proc. SPIE 5694, 33–40 (2005).
[Crossref]

2004 (2)

L. Cao, X. Ma, Q. He, H. Long, M. Wu, and G. Jin, “Imaging spectral device based on multiple volume holographic gratings,” Opt. Eng. 43, 2009–2016 (2004).
[Crossref]

F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Trans. Geosci. Remote Sens. 42, 1778–1790 (2004).
[Crossref]

2003 (1)

A. Wuttig and R. Riesenberg, “Sensitive Hadamard transform imaging spectrometer with a simple MEMS,” Proc. SPIE 4881, 167–178 (2003).
[Crossref]

2001 (1)

O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, and M. L. Myrick, “Design and testing of a multivariate optical element: The first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001).
[Crossref]

1999 (1)

A. M. Prakash, C. M. Stellman, and K. S. Booksh, “Optical regression: a method for improving quantitative precision of multivariate prediction with single channel spectrometers,” Chemometr. Intell. Lab. 46, 265–274 (1999).
[Crossref]

1998 (3)

W. C. Sweatt, C. A. A. Boye, S. M. Gentry, M. R. Descour, B. R. Stallard, and C. L. Grotbeck, “ISIS: An information-efficient spectral imaging system,” Proc. SPIE 3438, 98–106 (1998).
[Crossref]

M. P. Nelson, J. F. Aust, J. A. Dobrowolski, P. G. Verly, and M. L. Myrick, “Multivariate optical computation for predictive spectroscopy,” Anal. Chem. 70, 73–82 (1998).
[Crossref] [PubMed]

Q. S. Hanley, P. J. Verveer, and T. M. Jovin, “Optical sectioning fluorescence spectroscopy in a programmable array microscope,” Appl. Spectrosc. 52, 783–789 (1998).
[Crossref]

1995 (1)

1988 (1)

D. M. Haaland and E. V. Thomas, “Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information,” Anal. Chem. 60, 1193–1202 (1988).
[Crossref]

1964 (1)

M. Bottema, W. Plummer, and J. Strong, “Water vapor in the atmosphere of Venus,” Astrophys. J. 139, 1021 (1964).
[Crossref]

1958 (1)

P. Fellgett, “A contribution to the theory of the multiplex interferometric spectrometer. I.—les principes généraux des méthodes nouvelles en spectroscopie interférentielle—A propos de la théorie du spectromètre interférentiel multiplex,” J. Phys. Radium 19, 187–191 (1958).
[Crossref]

’t Hooft, G. W.

N. Uzunbajakava, P. de Peinder, G. W. ’t Hooft, and A. T. M. van Gogh, “Low-cost spectroscopy with a variable multivariate optical element,” Anal. Chem. 78, 7302–7308 (2006).
[Crossref] [PubMed]

Adler-Golden, S.

N. Goldstein, P. Vujkovic-Cvijin, M. Fox, B. Gregor, J. Lee, J. Cline, and S. Adler-Golden, “DMD-based adaptive spectral imagers for hyperspectral imagery and direct detection of spectral signatures,” Proc. SPIE 7210, 721008 (2009).
[Crossref]

Aust, J. F.

M. P. Nelson, J. F. Aust, J. A. Dobrowolski, P. G. Verly, and M. L. Myrick, “Multivariate optical computation for predictive spectroscopy,” Anal. Chem. 70, 73–82 (1998).
[Crossref] [PubMed]

Baraniuk, R. G.

R. G. Baraniuk, “Compressive sensing [lecture notes],” IEEE Signal Process. Mag. 24, 118–121 (2007).
[Crossref]

Bearman, G.

Z. Li, D. Psaltis, W. Liu, W. R. Johnson, and G. Bearman, “Volume holographic spectral imaging,” Proc. SPIE 5694, 33–40 (2005).
[Crossref]

Ben-Amotz, D.

D. S. Wilcox, G. T. Buzzard, B. J. Lucier, P. Wang, and D. Ben-Amotz, “Photon level chemical classification using digital compressive detection,” Anal. Chim. Acta 755, 17–27 (2012).
[Crossref] [PubMed]

B. M. Davis, A. J. Hemphill, D. Cebeci Maltaş, M. A. Zipper, P. Wang, and D. Ben-Amotz, “Multivariate hyper-spectral Raman imaging using compressive detection,” Anal. Chem. 83, 5086–5092 (2011).
[Crossref] [PubMed]

Booksh, K. S.

A. M. Prakash, C. M. Stellman, and K. S. Booksh, “Optical regression: a method for improving quantitative precision of multivariate prediction with single channel spectrometers,” Chemometr. Intell. Lab. 46, 265–274 (1999).
[Crossref]

Bottema, M.

M. Bottema, W. Plummer, and J. Strong, “Water vapor in the atmosphere of Venus,” Astrophys. J. 139, 1021 (1964).
[Crossref]

Boye, C. A. A.

W. C. Sweatt, C. A. A. Boye, S. M. Gentry, M. R. Descour, B. R. Stallard, and C. L. Grotbeck, “ISIS: An information-efficient spectral imaging system,” Proc. SPIE 3438, 98–106 (1998).
[Crossref]

Brady, D. J.

Bruzzone, L.

F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Trans. Geosci. Remote Sens. 42, 1778–1790 (2004).
[Crossref]

Buzzard, G. T.

G. T. Buzzard and B. J. Lucier, “Optimal filters for high-speed compressive detection in spectroscopy,” Proc. SPIE 8657, 865707 (2013).
[Crossref]

D. S. Wilcox, G. T. Buzzard, B. J. Lucier, P. Wang, and D. Ben-Amotz, “Photon level chemical classification using digital compressive detection,” Anal. Chim. Acta 755, 17–27 (2012).
[Crossref] [PubMed]

Cao, L.

C. Gu, X. Yang, J. Zhang, R. Newhouse, and L. Cao, “Fiber sensors for molecular detection,” Proc. SPIE 7851, 785105 (2010).
[Crossref]

L. Cao and C. Gu, “Matched spectral filter based on reflection holograms for analyte identification,” Appl. Opt. 48, 6973–6979 (2009).
[Crossref] [PubMed]

L. Cao, X. Ma, Q. He, H. Long, M. Wu, and G. Jin, “Imaging spectral device based on multiple volume holographic gratings,” Opt. Eng. 43, 2009–2016 (2004).
[Crossref]

Cebeci Maltas, D.

B. M. Davis, A. J. Hemphill, D. Cebeci Maltaş, M. A. Zipper, P. Wang, and D. Ben-Amotz, “Multivariate hyper-spectral Raman imaging using compressive detection,” Anal. Chem. 83, 5086–5092 (2011).
[Crossref] [PubMed]

Cline, J.

N. Goldstein, P. Vujkovic-Cvijin, M. Fox, B. Gregor, J. Lee, J. Cline, and S. Adler-Golden, “DMD-based adaptive spectral imagers for hyperspectral imagery and direct detection of spectral signatures,” Proc. SPIE 7210, 721008 (2009).
[Crossref]

Da Silva, E.

Dantus, M.

C. W. Freudiger, W. Min, G. R. Holtom, B. Xu, M. Dantus, and X. S. Xie, “Highly specific label-free molecular imaging with spectrally tailored excitation-stimulated Raman scattering (STE-SRS) microscopy,” Nature Photon. 5, 103–109 (2011).
[Crossref]

Dao, N. Q.

Davis, B. M.

B. M. Davis, A. J. Hemphill, D. Cebeci Maltaş, M. A. Zipper, P. Wang, and D. Ben-Amotz, “Multivariate hyper-spectral Raman imaging using compressive detection,” Anal. Chem. 83, 5086–5092 (2011).
[Crossref] [PubMed]

de Peinder, P.

N. Uzunbajakava, P. de Peinder, G. W. ’t Hooft, and A. T. M. van Gogh, “Low-cost spectroscopy with a variable multivariate optical element,” Anal. Chem. 78, 7302–7308 (2006).
[Crossref] [PubMed]

Descour, M. R.

W. C. Sweatt, C. A. A. Boye, S. M. Gentry, M. R. Descour, B. R. Stallard, and C. L. Grotbeck, “ISIS: An information-efficient spectral imaging system,” Proc. SPIE 3438, 98–106 (1998).
[Crossref]

Dobrowolski, J. A.

M. P. Nelson, J. F. Aust, J. A. Dobrowolski, P. G. Verly, and M. L. Myrick, “Multivariate optical computation for predictive spectroscopy,” Anal. Chem. 70, 73–82 (1998).
[Crossref] [PubMed]

Eastwood, D.

O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, and M. L. Myrick, “Design and testing of a multivariate optical element: The first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001).
[Crossref]

Fellgett, P.

P. Fellgett, “A contribution to the theory of the multiplex interferometric spectrometer. I.—les principes généraux des méthodes nouvelles en spectroscopie interférentielle—A propos de la théorie du spectromètre interférentiel multiplex,” J. Phys. Radium 19, 187–191 (1958).
[Crossref]

Fox, M.

N. Goldstein, P. Vujkovic-Cvijin, M. Fox, B. Gregor, J. Lee, J. Cline, and S. Adler-Golden, “DMD-based adaptive spectral imagers for hyperspectral imagery and direct detection of spectral signatures,” Proc. SPIE 7210, 721008 (2009).
[Crossref]

Freudiger, C. W.

C. W. Freudiger, W. Min, G. R. Holtom, B. Xu, M. Dantus, and X. S. Xie, “Highly specific label-free molecular imaging with spectrally tailored excitation-stimulated Raman scattering (STE-SRS) microscopy,” Nature Photon. 5, 103–109 (2011).
[Crossref]

Gehm, M. E.

Gemperline, P.

O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, and M. L. Myrick, “Design and testing of a multivariate optical element: The first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001).
[Crossref]

Gentry, S. M.

W. C. Sweatt, C. A. A. Boye, S. M. Gentry, M. R. Descour, B. R. Stallard, and C. L. Grotbeck, “ISIS: An information-efficient spectral imaging system,” Proc. SPIE 3438, 98–106 (1998).
[Crossref]

Goldstein, N.

N. Goldstein, P. Vujkovic-Cvijin, M. Fox, B. Gregor, J. Lee, J. Cline, and S. Adler-Golden, “DMD-based adaptive spectral imagers for hyperspectral imagery and direct detection of spectral signatures,” Proc. SPIE 7210, 721008 (2009).
[Crossref]

Gregor, B.

N. Goldstein, P. Vujkovic-Cvijin, M. Fox, B. Gregor, J. Lee, J. Cline, and S. Adler-Golden, “DMD-based adaptive spectral imagers for hyperspectral imagery and direct detection of spectral signatures,” Proc. SPIE 7210, 721008 (2009).
[Crossref]

Grotbeck, C. L.

W. C. Sweatt, C. A. A. Boye, S. M. Gentry, M. R. Descour, B. R. Stallard, and C. L. Grotbeck, “ISIS: An information-efficient spectral imaging system,” Proc. SPIE 3438, 98–106 (1998).
[Crossref]

Gu, C.

C. Gu, X. Yang, J. Zhang, R. Newhouse, and L. Cao, “Fiber sensors for molecular detection,” Proc. SPIE 7851, 785105 (2010).
[Crossref]

L. Cao and C. Gu, “Matched spectral filter based on reflection holograms for analyte identification,” Appl. Opt. 48, 6973–6979 (2009).
[Crossref] [PubMed]

Gundogdu, K.

D. B. Turner, K. W. Stone, K. Gundogdu, and K. A. Nelson, “Invited article: The coherent optical laser beam recombination technique (COLBERT) spectrometer: Coherent multidimensional spectroscopy made easier,” Rev. Sci. Instrum. 82, 081301 (2011).
[Crossref] [PubMed]

Haaland, D. M.

D. M. Haaland and E. V. Thomas, “Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information,” Anal. Chem. 60, 1193–1202 (1988).
[Crossref]

Hanley, Q. S.

He, Q.

L. Cao, X. Ma, Q. He, H. Long, M. Wu, and G. Jin, “Imaging spectral device based on multiple volume holographic gratings,” Opt. Eng. 43, 2009–2016 (2004).
[Crossref]

Hemphill, A. J.

B. M. Davis, A. J. Hemphill, D. Cebeci Maltaş, M. A. Zipper, P. Wang, and D. Ben-Amotz, “Multivariate hyper-spectral Raman imaging using compressive detection,” Anal. Chem. 83, 5086–5092 (2011).
[Crossref] [PubMed]

Holtom, G. R.

C. W. Freudiger, W. Min, G. R. Holtom, B. Xu, M. Dantus, and X. S. Xie, “Highly specific label-free molecular imaging with spectrally tailored excitation-stimulated Raman scattering (STE-SRS) microscopy,” Nature Photon. 5, 103–109 (2011).
[Crossref]

Jin, G.

L. Cao, X. Ma, Q. He, H. Long, M. Wu, and G. Jin, “Imaging spectral device based on multiple volume holographic gratings,” Opt. Eng. 43, 2009–2016 (2004).
[Crossref]

Johnson, W. R.

Z. Li, D. Psaltis, W. Liu, W. R. Johnson, and G. Bearman, “Volume holographic spectral imaging,” Proc. SPIE 5694, 33–40 (2005).
[Crossref]

Jouan, M. D.

Jovin, T. M.

Karunamuni, J.

O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, and M. L. Myrick, “Design and testing of a multivariate optical element: The first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001).
[Crossref]

Lee, J.

N. Goldstein, P. Vujkovic-Cvijin, M. Fox, B. Gregor, J. Lee, J. Cline, and S. Adler-Golden, “DMD-based adaptive spectral imagers for hyperspectral imagery and direct detection of spectral signatures,” Proc. SPIE 7210, 721008 (2009).
[Crossref]

Li, H.

O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, and M. L. Myrick, “Design and testing of a multivariate optical element: The first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001).
[Crossref]

Li, Z.

Z. Li, D. Psaltis, W. Liu, W. R. Johnson, and G. Bearman, “Volume holographic spectral imaging,” Proc. SPIE 5694, 33–40 (2005).
[Crossref]

Liu, W.

Z. Li, D. Psaltis, W. Liu, W. R. Johnson, and G. Bearman, “Volume holographic spectral imaging,” Proc. SPIE 5694, 33–40 (2005).
[Crossref]

Long, H.

L. Cao, X. Ma, Q. He, H. Long, M. Wu, and G. Jin, “Imaging spectral device based on multiple volume holographic gratings,” Opt. Eng. 43, 2009–2016 (2004).
[Crossref]

Love, S. P.

S. P. Love, “Programmable matched filter and Hadamard transform hyperspectral imagers based on micromirror arrays,” Proc. SPIE 7210, 721007 (2009).
[Crossref]

Lucier, B. J.

G. T. Buzzard and B. J. Lucier, “Optimal filters for high-speed compressive detection in spectroscopy,” Proc. SPIE 8657, 865707 (2013).
[Crossref]

D. S. Wilcox, G. T. Buzzard, B. J. Lucier, P. Wang, and D. Ben-Amotz, “Photon level chemical classification using digital compressive detection,” Anal. Chim. Acta 755, 17–27 (2012).
[Crossref] [PubMed]

Ma, X.

L. Cao, X. Ma, Q. He, H. Long, M. Wu, and G. Jin, “Imaging spectral device based on multiple volume holographic gratings,” Opt. Eng. 43, 2009–2016 (2004).
[Crossref]

Madden, S.

McCain, S. T.

Melgani, F.

F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Trans. Geosci. Remote Sens. 42, 1778–1790 (2004).
[Crossref]

Mignardi, M.

Min, W.

C. W. Freudiger, W. Min, G. R. Holtom, B. Xu, M. Dantus, and X. S. Xie, “Highly specific label-free molecular imaging with spectrally tailored excitation-stimulated Raman scattering (STE-SRS) microscopy,” Nature Photon. 5, 103–109 (2011).
[Crossref]

Myrick, M. L.

O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, and M. L. Myrick, “Design and testing of a multivariate optical element: The first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001).
[Crossref]

M. P. Nelson, J. F. Aust, J. A. Dobrowolski, P. G. Verly, and M. L. Myrick, “Multivariate optical computation for predictive spectroscopy,” Anal. Chem. 70, 73–82 (1998).
[Crossref] [PubMed]

Nelson, K. A.

D. B. Turner, K. W. Stone, K. Gundogdu, and K. A. Nelson, “Invited article: The coherent optical laser beam recombination technique (COLBERT) spectrometer: Coherent multidimensional spectroscopy made easier,” Rev. Sci. Instrum. 82, 081301 (2011).
[Crossref] [PubMed]

Nelson, M. P.

M. P. Nelson, J. F. Aust, J. A. Dobrowolski, P. G. Verly, and M. L. Myrick, “Multivariate optical computation for predictive spectroscopy,” Anal. Chem. 70, 73–82 (1998).
[Crossref] [PubMed]

Newhouse, R.

C. Gu, X. Yang, J. Zhang, R. Newhouse, and L. Cao, “Fiber sensors for molecular detection,” Proc. SPIE 7851, 785105 (2010).
[Crossref]

Pitsianis, N. P.

Plummer, W.

M. Bottema, W. Plummer, and J. Strong, “Water vapor in the atmosphere of Venus,” Astrophys. J. 139, 1021 (1964).
[Crossref]

Potuluri, P.

Prakash, A. M.

A. M. Prakash, C. M. Stellman, and K. S. Booksh, “Optical regression: a method for improving quantitative precision of multivariate prediction with single channel spectrometers,” Chemometr. Intell. Lab. 46, 265–274 (1999).
[Crossref]

Psaltis, D.

Z. Li, D. Psaltis, W. Liu, W. R. Johnson, and G. Bearman, “Volume holographic spectral imaging,” Proc. SPIE 5694, 33–40 (2005).
[Crossref]

Quyen, N. T.

Riesenberg, R.

A. Wuttig and R. Riesenberg, “Sensitive Hadamard transform imaging spectrometer with a simple MEMS,” Proc. SPIE 4881, 167–178 (2003).
[Crossref]

Smith, B. W.

Smith, Z. J.

Soyemi, O.

O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, and M. L. Myrick, “Design and testing of a multivariate optical element: The first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001).
[Crossref]

Stallard, B. R.

W. C. Sweatt, C. A. A. Boye, S. M. Gentry, M. R. Descour, B. R. Stallard, and C. L. Grotbeck, “ISIS: An information-efficient spectral imaging system,” Proc. SPIE 3438, 98–106 (1998).
[Crossref]

Stellman, C. M.

A. M. Prakash, C. M. Stellman, and K. S. Booksh, “Optical regression: a method for improving quantitative precision of multivariate prediction with single channel spectrometers,” Chemometr. Intell. Lab. 46, 265–274 (1999).
[Crossref]

Stone, K. W.

D. B. Turner, K. W. Stone, K. Gundogdu, and K. A. Nelson, “Invited article: The coherent optical laser beam recombination technique (COLBERT) spectrometer: Coherent multidimensional spectroscopy made easier,” Rev. Sci. Instrum. 82, 081301 (2011).
[Crossref] [PubMed]

Strombom, S.

Strong, J.

M. Bottema, W. Plummer, and J. Strong, “Water vapor in the atmosphere of Venus,” Astrophys. J. 139, 1021 (1964).
[Crossref]

Sullivan, M. E.

Sweatt, W. C.

W. C. Sweatt, C. A. A. Boye, S. M. Gentry, M. R. Descour, B. R. Stallard, and C. L. Grotbeck, “ISIS: An information-efficient spectral imaging system,” Proc. SPIE 3438, 98–106 (1998).
[Crossref]

Synowicki, R. A.

O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, and M. L. Myrick, “Design and testing of a multivariate optical element: The first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001).
[Crossref]

Thomas, E. V.

D. M. Haaland and E. V. Thomas, “Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information,” Anal. Chem. 60, 1193–1202 (1988).
[Crossref]

Turner, D. B.

D. B. Turner, K. W. Stone, K. Gundogdu, and K. A. Nelson, “Invited article: The coherent optical laser beam recombination technique (COLBERT) spectrometer: Coherent multidimensional spectroscopy made easier,” Rev. Sci. Instrum. 82, 081301 (2011).
[Crossref] [PubMed]

Uzunbajakava, N.

N. Uzunbajakava, P. de Peinder, G. W. ’t Hooft, and A. T. M. van Gogh, “Low-cost spectroscopy with a variable multivariate optical element,” Anal. Chem. 78, 7302–7308 (2006).
[Crossref] [PubMed]

van Gogh, A. T. M.

N. Uzunbajakava, P. de Peinder, G. W. ’t Hooft, and A. T. M. van Gogh, “Low-cost spectroscopy with a variable multivariate optical element,” Anal. Chem. 78, 7302–7308 (2006).
[Crossref] [PubMed]

Verly, P. G.

M. P. Nelson, J. F. Aust, J. A. Dobrowolski, P. G. Verly, and M. L. Myrick, “Multivariate optical computation for predictive spectroscopy,” Anal. Chem. 70, 73–82 (1998).
[Crossref] [PubMed]

Verveer, P. J.

Vujkovic-Cvijin, P.

N. Goldstein, P. Vujkovic-Cvijin, M. Fox, B. Gregor, J. Lee, J. Cline, and S. Adler-Golden, “DMD-based adaptive spectral imagers for hyperspectral imagery and direct detection of spectral signatures,” Proc. SPIE 7210, 721008 (2009).
[Crossref]

Wachsmann-Hogiu, S.

Wagner, E. P.

Wang, P.

D. S. Wilcox, G. T. Buzzard, B. J. Lucier, P. Wang, and D. Ben-Amotz, “Photon level chemical classification using digital compressive detection,” Anal. Chim. Acta 755, 17–27 (2012).
[Crossref] [PubMed]

B. M. Davis, A. J. Hemphill, D. Cebeci Maltaş, M. A. Zipper, P. Wang, and D. Ben-Amotz, “Multivariate hyper-spectral Raman imaging using compressive detection,” Anal. Chem. 83, 5086–5092 (2011).
[Crossref] [PubMed]

Wilcox, D. S.

D. S. Wilcox, G. T. Buzzard, B. J. Lucier, P. Wang, and D. Ben-Amotz, “Photon level chemical classification using digital compressive detection,” Anal. Chim. Acta 755, 17–27 (2012).
[Crossref] [PubMed]

Willett, R. M.

R. M. Willett, M. E. Gehm, and D. J. Brady, “Multiscale reconstruction for computational spectral imaging,” Proc. SPIE 6498, 64980L (2007).
[Crossref]

Winefordner, J. D.

Wu, M.

L. Cao, X. Ma, Q. He, H. Long, M. Wu, and G. Jin, “Imaging spectral device based on multiple volume holographic gratings,” Opt. Eng. 43, 2009–2016 (2004).
[Crossref]

Wuttig, A.

A. Wuttig and R. Riesenberg, “Sensitive Hadamard transform imaging spectrometer with a simple MEMS,” Proc. SPIE 4881, 167–178 (2003).
[Crossref]

Xie, X. S.

C. W. Freudiger, W. Min, G. R. Holtom, B. Xu, M. Dantus, and X. S. Xie, “Highly specific label-free molecular imaging with spectrally tailored excitation-stimulated Raman scattering (STE-SRS) microscopy,” Nature Photon. 5, 103–109 (2011).
[Crossref]

Xu, B.

C. W. Freudiger, W. Min, G. R. Holtom, B. Xu, M. Dantus, and X. S. Xie, “Highly specific label-free molecular imaging with spectrally tailored excitation-stimulated Raman scattering (STE-SRS) microscopy,” Nature Photon. 5, 103–109 (2011).
[Crossref]

Yang, X.

C. Gu, X. Yang, J. Zhang, R. Newhouse, and L. Cao, “Fiber sensors for molecular detection,” Proc. SPIE 7851, 785105 (2010).
[Crossref]

Zhang, J.

C. Gu, X. Yang, J. Zhang, R. Newhouse, and L. Cao, “Fiber sensors for molecular detection,” Proc. SPIE 7851, 785105 (2010).
[Crossref]

Zhang, L.

O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, and M. L. Myrick, “Design and testing of a multivariate optical element: The first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001).
[Crossref]

Zipper, M. A.

B. M. Davis, A. J. Hemphill, D. Cebeci Maltaş, M. A. Zipper, P. Wang, and D. Ben-Amotz, “Multivariate hyper-spectral Raman imaging using compressive detection,” Anal. Chem. 83, 5086–5092 (2011).
[Crossref] [PubMed]

Anal. Chem. (5)

M. P. Nelson, J. F. Aust, J. A. Dobrowolski, P. G. Verly, and M. L. Myrick, “Multivariate optical computation for predictive spectroscopy,” Anal. Chem. 70, 73–82 (1998).
[Crossref] [PubMed]

O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, and M. L. Myrick, “Design and testing of a multivariate optical element: The first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001).
[Crossref]

B. M. Davis, A. J. Hemphill, D. Cebeci Maltaş, M. A. Zipper, P. Wang, and D. Ben-Amotz, “Multivariate hyper-spectral Raman imaging using compressive detection,” Anal. Chem. 83, 5086–5092 (2011).
[Crossref] [PubMed]

N. Uzunbajakava, P. de Peinder, G. W. ’t Hooft, and A. T. M. van Gogh, “Low-cost spectroscopy with a variable multivariate optical element,” Anal. Chem. 78, 7302–7308 (2006).
[Crossref] [PubMed]

D. M. Haaland and E. V. Thomas, “Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information,” Anal. Chem. 60, 1193–1202 (1988).
[Crossref]

Anal. Chim. Acta (1)

D. S. Wilcox, G. T. Buzzard, B. J. Lucier, P. Wang, and D. Ben-Amotz, “Photon level chemical classification using digital compressive detection,” Anal. Chim. Acta 755, 17–27 (2012).
[Crossref] [PubMed]

Appl. Opt. (2)

Appl. Spectrosc. (3)

Astrophys. J. (1)

M. Bottema, W. Plummer, and J. Strong, “Water vapor in the atmosphere of Venus,” Astrophys. J. 139, 1021 (1964).
[Crossref]

Chemometr. Intell. Lab. (1)

A. M. Prakash, C. M. Stellman, and K. S. Booksh, “Optical regression: a method for improving quantitative precision of multivariate prediction with single channel spectrometers,” Chemometr. Intell. Lab. 46, 265–274 (1999).
[Crossref]

IEEE Signal Process. Mag. (1)

R. G. Baraniuk, “Compressive sensing [lecture notes],” IEEE Signal Process. Mag. 24, 118–121 (2007).
[Crossref]

IEEE Trans. Geosci. Remote Sens. (1)

F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Trans. Geosci. Remote Sens. 42, 1778–1790 (2004).
[Crossref]

J. Phys. Radium (1)

P. Fellgett, “A contribution to the theory of the multiplex interferometric spectrometer. I.—les principes généraux des méthodes nouvelles en spectroscopie interférentielle—A propos de la théorie du spectromètre interférentiel multiplex,” J. Phys. Radium 19, 187–191 (1958).
[Crossref]

Nature Photon. (1)

C. W. Freudiger, W. Min, G. R. Holtom, B. Xu, M. Dantus, and X. S. Xie, “Highly specific label-free molecular imaging with spectrally tailored excitation-stimulated Raman scattering (STE-SRS) microscopy,” Nature Photon. 5, 103–109 (2011).
[Crossref]

Opt. Eng. (1)

L. Cao, X. Ma, Q. He, H. Long, M. Wu, and G. Jin, “Imaging spectral device based on multiple volume holographic gratings,” Opt. Eng. 43, 2009–2016 (2004).
[Crossref]

Opt. Express (1)

Proc. SPIE (8)

G. T. Buzzard and B. J. Lucier, “Optimal filters for high-speed compressive detection in spectroscopy,” Proc. SPIE 8657, 865707 (2013).
[Crossref]

Z. Li, D. Psaltis, W. Liu, W. R. Johnson, and G. Bearman, “Volume holographic spectral imaging,” Proc. SPIE 5694, 33–40 (2005).
[Crossref]

W. C. Sweatt, C. A. A. Boye, S. M. Gentry, M. R. Descour, B. R. Stallard, and C. L. Grotbeck, “ISIS: An information-efficient spectral imaging system,” Proc. SPIE 3438, 98–106 (1998).
[Crossref]

C. Gu, X. Yang, J. Zhang, R. Newhouse, and L. Cao, “Fiber sensors for molecular detection,” Proc. SPIE 7851, 785105 (2010).
[Crossref]

N. Goldstein, P. Vujkovic-Cvijin, M. Fox, B. Gregor, J. Lee, J. Cline, and S. Adler-Golden, “DMD-based adaptive spectral imagers for hyperspectral imagery and direct detection of spectral signatures,” Proc. SPIE 7210, 721008 (2009).
[Crossref]

S. P. Love, “Programmable matched filter and Hadamard transform hyperspectral imagers based on micromirror arrays,” Proc. SPIE 7210, 721007 (2009).
[Crossref]

A. Wuttig and R. Riesenberg, “Sensitive Hadamard transform imaging spectrometer with a simple MEMS,” Proc. SPIE 4881, 167–178 (2003).
[Crossref]

R. M. Willett, M. E. Gehm, and D. J. Brady, “Multiscale reconstruction for computational spectral imaging,” Proc. SPIE 6498, 64980L (2007).
[Crossref]

Rev. Sci. Instrum. (1)

D. B. Turner, K. W. Stone, K. Gundogdu, and K. A. Nelson, “Invited article: The coherent optical laser beam recombination technique (COLBERT) spectrometer: Coherent multidimensional spectroscopy made easier,” Rev. Sci. Instrum. 82, 081301 (2011).
[Crossref] [PubMed]

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Figures (4)

Fig. 1
Fig. 1 Multiple-output MOC schematic diagram. Solid lines indicate mirrors; dashed lines indicate pickoff mirrors positioned beneath the transmitted optical path. The central thin line indicates the optical path; outer thin lines represent the marginal ray path schematically. The path length from the SLM to each detector is the same. The polarizer is oriented horizontally.
Fig. 2
Fig. 2 Multiple-output SLM methods. (a) Incoming and outgoing beams, shown schematically. In the experiment, the SLM is actually overfilled, rather than underfilled as shown here, and the light is imaged rather than collimated. (b) Simplified representation of (a). (c) Bipolar method. The positive half-filter (green) and negative half-filter (red) are directed towards different detectors. (d) Multiple-zone method. The SLM is divided into two vertical zones (yellow and blue); each implements one matched half-filter, and each is directed towards a different detector.
Fig. 3
Fig. 3 Top two plots: White light spectrum with no filter, didymium filter (Di), and holmium filter (Ho), as measured by the commercial USB2000 spectrometer and spectral scans using the MOC. Standard deviations are too small to be discernible. Middle two plots: Transmission spectra of the didymium (Di) and holmium (Ho) filters, computed from the spectrum data from the USB2000 and the MOC. Central lines show averages across multiple recordings, and thin outer lines show averages plus and minus one standard deviation. Bottom plot: Spectral matched filter coefficients, computed using MOC spectral data.
Fig. 4
Fig. 4 Spectrum recognition results using (a) a single output (one half-filter), (b) the bipolar (single-zone) method, and (c) the multiple-zone method with two zones. In each chart, the vertical axis denotes correlation value. The lower-left axis indicates the sample spectrum; WL: white light source with no filter; Di: didymium filter; Ho: holmium filter. The lower-right axis gives the spectral matched filter. Ideal correlation values are one along the diagonal and zero elsewhere. The fidelity (one minus the maximum deviation of any value from the ideal) is (a) 0.9337, (b) 0.8360, and (c) 0.8425.

Equations (13)

Equations on this page are rendered with MathJax. Learn more.

c j , k = s j a k .
c j , k = δ j , k ,
C = SA = I ,
S + S = I .
S + C = S + SA = S + I .
A = S + .
a k , i + = { a k , i if a k , i > 0 , 0 if a k , i 0 and a k , i = { 0 if a k , i 0 a k , i if a k , i < 0 .
m k ± = max i | a k , i ± | ,
b k ± = a k ± / m k ± .
P = s j r + P j , bg .
c j , k + = ( P j , k + P j , bg ) m k + ,
c j , k = ( P j , k + P j , bg ) m k ,
c j , k = c j , k + c j , k .

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