Multivariate Optical Elements (MOEs)

  • Wideband Optical Interference Filters with Application-Specific Spectral Patterns
  • Increase Sensitivity and Specificity of Analyte Detection
  • Fabricated for UV, Visible, and Infrared with Traditional Bandpass Filter Methods

Wideband, Analyte-Specific Spectral Response of an Example MOE

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Wideband Optical Filters for Efficient Analyte Detection

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Multivariate Optical Element Multiplication
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Example Optical Multiplication by a MOE; transmission t is at wavelength λ.

At the beginning of May 2019, Thorlabs welcomed Cirtemo and their Multivariate Optical Elements (MOEs) technology. This team has joined us as Thorlabs Spectral Works and has over 40 patents granted and perpetually licensed for the MOE technology. Please send all inquiries to


  • Detection of Powders, Liquids, Slurries, and Gases
  • Process Control (e.g. Pharmaceutical, Food and Beverage, Industrial)
  • Oceanic Monitoring
  • Life Sciences
Bandpass Filter vs MOE Comparison
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Bandpass Filter and MOE Comparison; the t1 and t2 correspond to the top image example.

Multivariate optical elements (MOEs) are wide-band optical spectral filters capable of sampling more spectral wavelengths than discrete bandpass filters. Manufactured using the same methods as traditional optical bandpass filters, MOEs have an optimized design that varies the thickness of the layers to achieve the desired transmission or reflection response. These specific spectral features provide a higher level of sensitivity and specificity for real-time chemical detection of analytes (e.g., powders, liquids, gases) with transmission signatures from 250 nm to 14 μm.

Chemometric analysis is used to determine a regression curve, which is encoded into the MOE. The MOE introduces a spectroscopic weighting of the incident signal to perform an optical multiplication (as shown above) of the application-specific regression and the unknown spectrum. The signals are then effectively summed by a detector. This simple setup eliminates the need to use a complex wavelength-sensitive instrument. The integration of MOEs enables spectroscopic optical systems to be smaller, lighter, and more resilient than lab-grade spectrometers, while achieving the same analyte detection capabilities.

MOEs can be integrated in almost any optical system where a traditional bandpass filter could be used, enhancing the capabilities of filter photometers, hyperspectral imagers, or application-specific sensors. MOE-enhanced photometers are ideal for in-line process monitoring, point-of-care clinical use, and incorporation into field-based instruments. Modeling can be used to determine the technical feasibility for specific applications before beginning design and fabrication of any MOEs. The Thorlabs Spectral Works team is eager to work cooperatively to design the right size, wavelength range, and spectral response of each MOE needed for a given application. For more information about how MOEs work and MOE applications see the Webinars tab.

Thorlabs Spectral Works has also developed pattern transfer nanomanufacturing, which uses magnetic nanoparticles to create patterned optics with nanometer-scale features. Click here to learn more.

Selected Publications Using MOEs



Jones CM, Dai B, Price J, Li J, Pearl M, Soltmann B, and Myrick M. "A New Multivariate Optical Computing Microelement and Miniature Sensor for Spectroscopic Chemical Sensing in Harsh Environments: Design, Fabrication, and Testing." Sensors. 2019 Feb 8; 19 (3): 701.



Dai B, Jones CM, Pearl M, Pelletier M, and Myrick M. "Hydrogen Sulfide Gas Detection via Multivariate Optical Computing." Sensors. 2018 Jun 22; 18 (7): 2006.

Rekully CM, Faulkner ST, Lachenmyer EM, Cunningham BR, Shaw TJ, Richardson TL, and Myrick ML. "Fluorescence Excitation Spectroscopy for Phytoplankton Species Classification Using an All-Pairs Method: Characterization of a System with Unexpectedly Low Rank." Appl. Spectrosc. 2018 Mar 1; 72 (3): 442–462.



Priore R, Dougherty J, Cohen O, Bikov L, and Hirsh I. "Design of a Miniature SWIR Hyperspectral Snapshot Imager Utilizing Multivariate Optical Elements." Emerg. Imaging Sens. Technol. - Proc. SPIE. 2016 Oct 25; 9992: 999205.

Priore RJ and Jacksen N. "Spectral Imaging of Chemical Compounds Using Multivariate Optically Enhanced Filters Integrated with InGaAs VGA Cameras." Chem. Biol. Radiol. Nucl. Explos. Sens. XVII - Proc. SPIE. 2016 May 12; 9824: 98240P .



Priore RJ and Swanstrom JA. "Multivariate Optical Computing for Fluorochrome Discrimination." Prog. Biomed. Opt. Imaging - Proc. SPIE. 2015 Mar 9; 9332.



Jones CM, Freese R, Perkins D, and Dai B. "Multivariate Optical Computing Enables Accurate Harsh-Environment Sensing for the Oil and Gas Industry." Laser Focus World. 2014 Aug 6, pp 27–31.

Priore RJ and Swanstrom JA. "Multivariate Optical Element Platform for Compressed Detection of Fluorescence Markers."Next-Generation Spectrosc. Technol. VII - Proc. SPIE. 2014 May 21; 9101, 91010E.

Ice Core Technology in East Africa. Pipeline. 2014; pp 142–145.



Eriksen KO, Petroleum S, Jones C, Freese R, Zuilekom A, Van, Gao L, Chen D, Gascooke D, and Engelman B. "Field Tests of a New Optical Sensor Based on Integrated Computational Elements for Downhole Fluid Analysis." Soc. Pet. Eng. 2013 Sep 30; 166415.

Jones C, Gao L, Perkins D, Chen D, and Gascooke D. "Field Test of the Integrated Computational Elements: A New Optical Sensor for Downhole Fluid Analysis." Soc. Petrophysicists Well Log Anal. 2013 Jun 22; 1–10.

Priore RJ. "Multivariate Optical Elements Beat Bandpass Filters in Fluorescence Analysis." Laser Focus World. 2013 Jun 10; 49–52.

Swanstrom JA, Bruckman LS, Pearl MR, Simcock MN, Donaldson KA, Richardson TL, Shaw TJ, and Myrick ML. "Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part I: Design and Theoretical Performance of Multivariate Optical Elements."Appl. Spectrosc. 2013 Jun 1; 67 (6): 620–629.

Swanstrom JA, Bruckman LS, Pearl MR, Abernathy E, Richardson TL, Shaw TJ, and Myrick ML. "Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part II: Design and Experimental Protocol of a Shipboard Fluorescence Imaging Photometer." Appl. Spectrosc. 2013 Jun 1; 67 (6): 630–639.

Pearl MR, Swanstrom JA, Bruckman LS, Richardson TL, ShawTJ, Sosik HM, and Myrick ML. "Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part III: Demonstration." Appl. Spectrosc. 2013 Jun 1; 67 (6): 640–647.



Jones C, Freese B, Pelletier M, Perkins D, Chen D, Shen J, and Atkinson R. "Laboratory Quality Optical Analysis in Harsh Environments." Soc. Pet. Eng. 2012 Dec 10; 163289.



Profeta LTM and Myrick ML. "Spectral Resolution in Multivariate Optical Computing.Spectrochim. Acta - Part A Mol. Biomol. Spectrosc. 2007 Jun; 67 (2): 483–502.

Simcock MN and Myrick ML. "Precision in Imaging Multivariate Optical Computing." Appl. Opt. 2007 Mar 1; 46 (7): 1066–1080.



Priore RJ, Haibach FG, Schiza MV, Greer AE, Perkins DL, and Myrick ML. "Miniature Stereo Spectral Imaging System for Multivariate Optical Computing." Appl. Spectrosc. 2004 Jul 1; 58 (7): 870–873.

Haibach FG and Myrick ML. "Precision in Multivariate Optical Computing." Appl. Opt. 2004 Apr 1; 43 (10): 2130.



Haibach FG, Greer AE, Schiza MV, Priore RJ, Soyemi OO, and Myrick ML. "On-Line Reoptimization of Filter Designs for Multivariate Optical Elements." Appl. Opt. 2003 Apr 1; 42 (10): 1833.



Soyemi OO, Haibach FG, Gemperline PJ, and Myrick ML. "Design of Angle-Tolerant Multivariate Optical Elements for Chemical Imaging." Appl. Opt. 2002 May; 41 (10): 1936–1941.

Soyemi OO, Haibach FG, Gemperline PJ, and Myrick ML. "Nonlinear Optimization Algorithm for Multivariate Optical Element Design." Appl. Spectrosc. 2002 Apr 1; 56 (4): 477–487.

Myrick ML, Soyemi OO, Karunamuni J, Eastwood D, Li H, Zhang L, Greer AE, and Gemperline PA. "Single-Element All-Optical Approach to Chemometric Prediction." Vib. Spectrosc. 2002 Feb 28; 28 (1): 73–81.

Myrick ML, Soyemi OO, Haibach F, Zhang L, Greer A, Li H, Priore R, Schiza MV, and Farr JR. "Application of Multivariate Optical Computing to Near-Infrared Imaging." Vib. Spectrosc. Sens. Syst. - Proc. SPIE. 2002 Feb 22; 4577.

Myrick ML, Soyemi OO, Schiza MV, Farr JR, Haibach FG, Greer AE, Li H, and Priore RJ. "Application of Multivariate Optical Computing to Simple Near-Infrared Point Measurements." Instrum. Air Pollut. Glob. Atmos. Monit. - Proc. SPIE. 2002 Feb 7; 4574.



Soyemi O, Eastwood D, Zhang L, Li H, Karunamuni J, Gemperline P, Synowicki RA, and Myrick ML. "Design and Testing of a Multivariate Optical Element: The First Demonstration of Multivariate Optical Computing for Predictive Spectroscopy." Anal. Chem. 2001 Feb 10; 73 (6): 1069–1079.

Myrick ML, Soyemi O, Li H, Zhang L, and Eastwood D. "Spectral Tolerance Determination for Multivariate Optical Element Design." Fresenius J. Anal. Chem. 2001 Feb; 369: 351–355.



Nelson MP, Aust JF, Dobrowolski JA, Verly PG, and Myrick ML. "Multivariate Optical Computation for Predictive Spectroscopy." Anal. Chem. 1998 Jan 1; 70 (1): 73–82.

2021 Photonics West Demonstration

An overview of the Thorlabs’ Multivariate Optical Element (MOE) technology starting with where MOEs can be deployed. Team Lead at Thorlabs Spectral Works, Adam Fisher, then demonstrates how MOEs can be used in an optical system to detect which food oil is being measured. Throughout his demonstration he explains the basics of how an MOE works, how to design an optical system around an MOE, and real-wordl applications of MOEs.

Video Timestamps

  • 02:26 - Demonstration and Application Principles
  • 03:55 - Introduction to the Food Oil Demonstration
  • 07:09 - Experiment Design Process
  • 08:15 - Explanation of How MOEs Work to Indentify a Specific Signature in a Complex Spectrum
  • 11:05 - Applying Previously Explained Principles to the Food Oil Demonstration
  • 13:58 - Completion of the Food Oil Demonstration
  • 15:18 - Uses for MOEs and Real-World Applications

2021 Realtime Spectroscopy via Multivariate Optical Computing Webinar

An overview of the Thorlabs’ Multivariate Optical Element (MOE) technology starting with a brief history of MOEs. Technology Manager, Ryan Priore, Ph.D. then explains how MOEs function from an analytical standpoint. He then finishes his discussion with the process of encoding an MOE and MOE applications.

Video Timestamps

  • 01:41 - MOE Commercialization History
  • 02:55 - Real-World Measurement Problems
  • 03:31 - Concepts Behind an MOE (Multivariate Calibration and Multivariate Analysis)
  • 06:06 - Introduction to MOEs
  • 08:17 - How a Signal is Encoded into an MOE
  • 09:55 - In-Depth Walkthrough of MOE Image Example: Threat Targets
  • 13:36 - Discussion on a few MOE Inline Measurement Examples

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