Multivariate optical computing explained

Multivariate optical computing, also known as molecular factor computing, is an approach to the development of compressed sensing spectroscopic instruments, particularly for industrial applications such as process analytical support. "Conventional" spectroscopic methods often employ multivariate and chemometric methods, such as multivariate calibration, pattern recognition, and classification, to extract analytical information (including concentration) from data collected at many different wavelengths. Multivariate optical computing uses an optical computer to analyze the data as it is collected. The goal of this approach is to produce instruments which are simple and rugged, yet retain the benefits of multivariate techniques for the accuracy and precision of the result.

An instrument which implements this approach may be described as a multivariate optical computer. Since it describes an approach, rather than any specific wavelength range, multivariate optical computers may be built using a variety of different instruments (including Fourier Transform Infrared (FTIR)[1] and Raman).

The "software" in multivariate optical computing is encoded directly into an optical element spectral calculation engine such as an interference filter based multivariate optical element (MOE), holographic grating, liquid crystal tunable filter, spatial light modulator (SLM), or digital micromirror device (DMD) and is specific to the particular application. The optical pattern for the spectral calculation engine is designed for the specific purpose of measuring the magnitude of that multi-wavelength pattern in the spectrum of a sample, without actually measuring a spectrum.[2]

Multivariate optical computing allows instruments to be made with the mathematics of pattern recognition designed directly into an optical computer, which extracts information from light without recording a spectrum. This makes it possible to achieve the speed, dependability, and ruggedness necessary for real time, in-line process control instruments.

Multivariate optical computing encodes an analog optical regression vector of a transmission function for an optical element. Light which emanates from a sample contains the spectral information of that sample, whether the spectrum is discovered or not. As light passes from a sample through the element, the normalized intensity, which is detected by a broad band detector, is proportional to the dot product of the regression vector with that spectrum, i.e. is proportional to the concentration of the analyte for which the regression vector was designed. The quality of the analysis is then equal to the quality of the regression vector which is encoded. If the resolution of the regression vector is encoded to the resolution of the laboratory instrument from which that regression vector was designed and the resolution of the detector is equivalent, then the measurement made by Multivariate Optical Computing will be equivalent to that laboratory instrument by conventional means. The technique is making headway in a niche market for harsh environment detection. Specifically the technique has been adopted for use in the oil industry for detection of hydrocarbon composition in oil wells and pipeline monitoring. In such situations, laboratory quality measurements are necessary, but in harsh environments.

History

Although the concept of using a single optical element for analyte regression and detection was suggested in 1986,[3] the first full MOC concept device was published in 1997 from the Myrick group at the University of South Carolina,[4] with a subsequent demonstration in 2001.[5] The technique has received much recognition in the optics industry as a new method to perform optical analysis with advantages for harsh environment sensing.[6] [7] [8] The technique has been applied to Raman spectroscopy,[9] [10] fluorescence spectroscopy,[11] [12] [13] [14] [15] [16] [17] absorbance spectroscopy in the UV-Vis,[18] NIR[19] [20] [21] and MIR,[22] [23] microscopy,[24] reflectance spectroscopy[25] and hyperspectral imaging.[26] [27] In the years since first demonstration, applications have been demonstrated for defence,[28] forensics,[29] monitoring of chemical reactions,[30] environmental monitoring,[31] [32] recycling,[33] food and drug,[26] [34] medical and life sciences, and the petroleum industry.[35] [36] [37] [38] [39] [40] The first published demonstration for use of MOC in the harsh environments, was 2012 with a laboratory study with temperatures from 150F to 350F and pressures from 3000psi to 20,000psi, followed in 2013 with field trials in oil wells.

Notes and References

  1. 1
  2. Vornehm . J.E. Jr . Dong . A.J. . Boyd . R.W. . etal . 2014. Multiple-output multivariate optical computing for spectrum recognition. Optics Express. 22. 21 . 25005–14. 2014OExpr..2225005V. 10.1364/OE.22.025005. 25401534 . 28584987 . free .
  3. Bialkowski . S . 1986 . Species discrimination and quantitative estimation using incoherent linear optical signal processing of emission signals . Analytical Chemistry . 58 . 12. 2561–2563 . 10.1021/ac00125a043.
  4. Book: 10.1117/12.290200. Design of thin-film filters for the monitoring of chemical reactions. Optical Thin Films V: New Developments. 3133. 38. 1997. Hall. Randolph L. Dobrowolski. Jerzy A.. Verly. Pierre G.. Myrick. Michael L.. Nelson. Matthew P.. Aust. Jeffrey F.. 135787454.
  5. Soyemi . O. . Eastwood . D. . Zhang . L. . etal . 2001 . Design and Testing of a Multivariate Optical Element: The First Demonstration of Multivariate Optical Computing for Predictive Spectroscopy . Analytical Chemistry . 73 . 6. 1069–1079 . 10.1021/ac0012896 .
  6. Book: 10.1117/12.417366. Field applications of stand-off sensing using visible/NIR multivariate optical computing. Water, Ground, and Air Pollution Monitoring and Remediation. 4199. 105. 2001. Vo-Dinh. Tuan. Eastwood. Delyle. Soyemi. Olusola O.. Karunamuni. Jeevanandra. Zhang. Lixia. Li. Hongli. Myrick. Michael L.. 93350247. Spellicy. Robert L.
  7. Myrick, M.L. (2002). "Multivariate optical elements simplify spectroscopy". Laser Focus World. 38 (3): 91–94.
  8. Jones, C.M., Freese, B., Pelletier, M. et al. 2012. Laboratory Quality Optical Analysis in Harsh Environments. Presented at the SPE Kuwait International Petroleum Conference and Exhibition,
  9. Davis . B.M. . Hemphill . A.J. . Maltaş . D.C. . etal . 2011 . Multivariate Hyperspectral Raman Imaging Using Compressive Detection . Analytical Chemistry . 83 . 13. 5086–5092 . 10.1021/ac103259v . 21604741 .
  10. Smith . Z.J. . Strombom . S. . Wachsmann-Hogiu . S. . 2011 . Multivariate optical computing using a digital micromirror device for fluorescence and Raman spectroscopy . Optics Express . 19 . 18. 16950–16962 . 10.1364/OE.19.016950 . 21935055 . 2011OExpr..1916950S . free .
  11. Book: 10.1117/12.2080996. Multivariate optical computing for fluorochrome discrimination. Optical Diagnostics and Sensing XV: Toward Point-of-Care Diagnostics. 9332. 933212. 2015. Coté. Gerard L. Priore. Ryan J.. Swanstrom. Joseph A.. 120527052.
  12. Book: 10.1117/12.2053570. Multivariate optical element platform for compressed detection of fluorescence markers. Next-Generation Spectroscopic Technologies VII. 9101. 91010E. 2014. Druy. Mark A. Crocombe. Richard A. Priore. Ryan J.. Swanstrom. Joseph A.. 120097929.
  13. Priore, R.J. (2013). "OPTICS FOR BIOPHOTONICS: Multivariate optical elements beat bandpass filters in fluorescence analysis". Laser Focus World. 49 (6): 49–52.
  14. Swanstrom . J.A. . Bruckman . L.S . Pearl . M.R. . etal . 2013 . Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part I: Design and Theoretical Performance of Multivariate Optical Elements . Applied Spectroscopy . 67 . 6. 220–229 . 10.1366/12-06783 . 23735247 . 2013ApSpe..67..620S . 5400202 .
  15. Swanstrom . J.A. . Bruckman . L.S. . Pearl . M.R. . etal . 2013 . Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part II: Design and Experimental Protocol of a Shipboard Fluorescence Imaging Photometer . Applied Spectroscopy . 67 . 6. 230–239 . 10.1366/12-06784 . 23735248 . 2013ApSpe..67..630S . 25533573 .
  16. Pearl . M.R. . Swanstrom . J.A. . Bruckman . L.S. . etal . 2013 . Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part III: Demonstration . Applied Spectroscopy . 67 . 6. 240–247 . 10.1366/12-06785 . 23735249 . 2013ApSpe..67..640P . 12109872 .
  17. Qu . J.Y. . Chang . H. . Xiong . S. . 2002 . Fluorescence spectral imaging for characterization of tissue based on multivariate statistical analysis . Journal of the Optical Society of America A . 19 . 9. 1823–1831 . 10.1364/JOSAA.19.001823 . 12216876 . 2002JOSAA..19.1823Q . 12214976 .
  18. Priore, R.J., Greer, A.E., Haibach, F.G. et al. 2003. Novel Imaging Systems: Multivariate Optical Computing in the UV-VIS. In Proc., IS&T's NIP19: International Conference on Digital Printing Technologies, Vol. 19, 906–910. New Orleans, Louisiana.
  19. Book: 10.1117/12.2177430. Latest developments in Texas Instruments DLP near-infrared spectrometers enable the next generation of embedded compact, portable systems. Next-Generation Spectroscopic Technologies VIII. 9482. 94820C. 2015. Druy. Mark A. Crocombe. Richard A. Bannon. David P. Pruett. Eric. 114904996.
  20. Book: 10.1117/12.455732. Application of multivariate optical computing to near-infrared imaging. Vibrational Spectroscopy-based Sensor Systems. 4577. 148. 2002. Christesen. Steven D. Myrick. Michael L.. Soyemi. Olusola O.. Haibach. Fred. Zhang. Lixia. Greer. Ashley. Li. Hongli. Priore. Ryan. Schiza. Maria V.. Farr. J. R.. 109007082. Sedlacek Iii. Arthur J.
  21. Book: 10.1117/12.455161. Application of multivariate optical computing to simple near-infrared point measurements. Instrumentation for Air Pollution and Global Atmospheric Monitoring. 4574. 208–215. 2002. Jensen. James O. Myrick. Michael L.. Soyemi. Olusola O.. Schiza. M. V.. Farr. J. R.. Haibach. Fred. Greer. Ashley. Li. Hong. Priore. Ryan. 110288509. Spellicy. Robert L.
  22. Coates . J . 2005 . A New Approach to Near- and Mid-Infrared Process Analysis – Encoded photometric infrared technology has the ability to address the demands of modern process applications, including those of the PAT initiative . Spectroscopy . 20 . 1. 32–35 .
  23. Jones, C., Gao, L., Perkins, D. et al. 2013. Field Test of the Integrated Computational Elements: A New Optical Sensor for Downhole Fluid Analysis. Presented at the SPWLA 54th Annual Logging Symposium, New Orleans, Louisiana, 22–26 June. SPWLA-2013-YY.
  24. Book: 10.1117/12.310558. Multivariate optical computation for predictive spectroscopy. Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V. 3261. 232–243. 1998. Cogswell. Carol J. Nelson. Matthew P.. Aust. Jeffrey F.. Dobrowolski. Jerzy A.. Verly. Pierre G.. Myrick. Michael L.. 108965881. Conchello. Jose-Angel. Lerner. Jeremy M. Lu. Thomas T. Wilson. Tony.
  25. Boysworth . M.K. . Banerija . S. . Wilson . D.M. . etal . 2007 . Generalization of multivariate optical computations as a method for improving the speed and precision of spectroscopic analyses . Journal of Chemometrics . 22 . 6. 355–365 . 10.1002/cem.1132 . 122073990 .
  26. Mendendorp . J. . Lodder . R.A. . 2005 . Applications of integrated sensing and processing in spectroscopic imaging and sensing . Journal of Chemometrics . 19 . 10. 533–542 . 10.1002/cem.961 . 10.1.1.141.4078 . 17681571 .
  27. Priore . R.J. . Haibach . F.G. . Schiza . M.V. . etal . 2004 . A Miniature Stereo Spectral Imaging System for Multivariate Optical Computing . Applied Spectroscopy . 58 . 7. 870–873 . 10.1366/0003702041389418 . 2004ApSpe..58..870P . 15282055 . 39015203 .
  28. Book: 10.1117/12.426870. Simple optical computing device for chemical analysis. Functional Integration of Opto-Electro-Mechanical Devices and Systems. 4284. 17–28. 2001. Descour. Michael R. Soyemi. Olusola O.. Zhang. Lixia. Eastwood. Delyle. Li. Hongli. Gemperline. Paul J.. Paul J. Gemperline. Myrick. Michael L.. 137444406. Rantala. Juha T.
  29. Myrick . M.L. . Soyemi . O. . Li . H. . etal . 2001 . Spectral tolerance determination for multivariate optical element design . Fresenius' Journal of Analytical Chemistry . 369 . 3–4. 351–355 . 10.1007/s002160000642 . 11293715 . 19109 .
  30. Fratkin, M. 2008. On-Line Oil Quality Sensors. Presented at the CTMA Symposium, Baltimore, Maryland, 7–9 April.
  31. Book: 10.1117/12.417462. Novel filter design algorithm for multivariate optical computing. Advanced Environmental and Chemical Sensing Technology. 4205. 288. 2001. Vo-Dinh. Tuan. Soyemi. Olusola O.. Gemperline. Paul J.. Paul J. Gemperline. Zhang. Lixia. Eastwood. Delyle. Li. Hong. Myrick. Michael L.. 110391915. Buettgenbach. Stephanus.
  32. Book: 10.1117/12.372890. New approaches to implementing predictive spectroscopy. Pattern Recognition, Chemometrics, and Imaging for Optical Environmental Monitoring. 3854. 98–102. 1999. Siddiqui. Khalid J. Myrick. Michael L.. 119947119. Eastwood. Delyle.
  33. Pruett, E. 2015. Techniques and applications of programmable spectral pattern coding in Texas Instruments DLP spectroscopy. In Proc. SPIE 9376, Emerging Digital Micromirror Device Based Systems and Applications VII, 93760H, eds. M.R. Douglass, P.S. King, and B.L. Lee. San Francisco, California, 10 March.
  34. Dai . B. . Urbas . A. . Douglas . C.C. . etal . 2007 . Molecular Factor Computing for Predictive Spectroscopy . Pharmaceutical Research . 24 . 8. 1441–1449 . 10.1007/s11095-007-9260-1 . 17380265 . 10.1.1.141.5296 . 3223005 .
  35. Jones, C.M., van Zuilekom, T., and Iskander, F. 2016. How Accurate Is Enhanced Optical Fluid Analysis Compared to Lab PVT Measurements? Presented at the SPWLA 57th Annual Symposium, Reykjavik, Iceland, 25–29 June. SPWLA-2016-JJJ.
  36. Jones, C.M., He, T., Dai, B. et al. 2015. Measurement and Use of Formation Fluid, Saturate, and Aromatic Content, with Wireline Formation Testers. Presented at the SPWLA 56th Annual Symposium, Long Beach, California, 18–22 July. SPWLA-2015-EE.
  37. Hunt, I. 2014. ICE Core Technology in East Africa. Pipeline November (209): 142–145.
  38. Chemali . R. . Semac . W. . Balliet . R. . etal . 2014 . Formation-Evaluation Challenges and Opportunities in Deepwater . Petrophysics . 55 . 2. 124–135 .
  39. Jones, C. 2014. Optical Sensors Analyze Fluids In Situ. The American Oil and Gas Reporter September: 117–123.
  40. Eriksen, K.O. (Statoil), Jones, C.M., Freese, R. et al. 2013. Field Tests of a New Optical Sensor Based on Integrated Computational. Presented at SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 30 September–2 October. SPE-166415-MS.

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