cirtemo_overview_with hyperspectral example-pre-nda

The Multivariate Optical

Element Platform

Febuary 2, 2015

Overview

• CIRTEMO Overview

• The Multivariate Optical

Element Platform

• Questions & Answers

© CIRTEMO, LLC 2015. All Rights Reserved.

CIRTEMO Products and Services are protected by U.S. and International issued patents and pending patent applications. 2

Today’s goal:

Introduce the Multivariate Optical Element

Platform and address any questions

regarding the underlying technology.

CIRTEMO™ Corporate Overview

• CIRTEMO was founded in late 2012 and is headquartered in Columbia, SC

• CIRTEMO designs and manufactures patented optical filters:

– Called Multivariate Optical Elements (MOE)

– Encoded to detect and measure complex chemical signatures or attributes.

• MOEs enable optical systems to

– Detect and measure specific chemicals or attributes that cannot be achieved with traditional optical filters

– Achieve better performances from optical components and systems

• CIRTEMO has 40+ patents granted and perpetually licensed worldwide around MOE technology

• CIRTEMO is partnering with

– Optical Filter Manufacturers (OFMs)

– Optical System Manufacturers (OSMs)



CIRTEMO™ Technology Overview

• Chemometrics – is a method for modeling multivariate data (eg. optical spectra)

– Model parameters can be applied to data from a spectrometer (or series of bandpass measurements) to estimate the composition of unknowns

• Multivariate Optical Computing (MOC)

– is an alternative method for modeling multivariate optical spectra

– Is the optical equivalent of a dot product in which simple optical systems may achieve the sensitivity/specificity of a laboratory grade spectrometer.

– is mostly achieved by refinement of optical interference filter structures that we call Multivariate Optical Elements (MOEs)

– MOEs can be installed in a photometer to estimate or predict the composition of unknowns.



Wavelength (nm)

Inte

ns

ity

Sample #

Pre

dic

tio

n

Wavelength (nm)

Re

gre

ss

ion

+

-

x =

The Multivariate Optical Element (MOE) Platform

• Multivariate Optical Computing is the optical equivalent of a dot product

– ŷ - estimated analytical property (eg. concentration)

– t - scaled regression vector

– - analytical spectroscopic response (eg. SWIR spectrum)

• Multivariate Optical Elements (MOEs)

– are patented, wide-band, optical interference filters encoded with an application-specific regression (or pattern) to detect/measure complex chemical signatures.

– realize the measurement advantages of Multivariate Optical Computing (MOC)

– enable a filter based instrument to achieve the sensitivity/specificity of a laboratory spectrometer as well as convert a focal plane array into a real-time hyperspectral imager.



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).

ŷ = 𝒕 • 𝜆 = 𝑡𝒊 • 𝜆𝒊

𝑵

𝒊

Multiplication Addition

1

Optical Filter

(t1 = 0.9; t2 = 0.5)

0.91

Detector

2

(0.91+ 0.52)

0.52

Wavelength (nm)

% T

ran

sm

iss

ion

MOE

The Multivariate Optical Element (MOE) Platform

• MOEs may be incorporated into optical systems in a variety of ways:

– Beam-splitter configuration (single MOE; multiple detectors)

– Filter photometer configuration (multiple MOEs; single detector)

– Snapshot array configuration (multiple MOEs; multiple detectors)



MOE DetectorT

DetectorR

50 100 150 2000

20

40

60

80

100

Interested Wavelength

%R

50 100 150 2000

20

40

60

80

100

Interested Wavelength

%T

Beam-splitter Configuration

MOE1

Detector

Filter Photometer Configuration

MOE2

ND

Snapshot Array Configuration

Notional MOE Hyperspectral Imager Detection

1. Imager collects

light from scene

2. SWIR light passes through patterned, target-

specific, MOE filters before pixel readout

3. Target-specific imagery is extracted from patterned

pixel readout or compiled from discrete MOE filters

5. Report detection

events

2. SWIR light passes through target-

specific, MOE filters before pixel readout

4. Image Processing



Wavelength (nm)

% T

ran

sm

iss

ion

Discrete Bandpass Measurements

Wavelength (nm)

% T

ran

sm

iss

ion

MOE Measurements (MOE1 & MOE2)

Radiation

from Scene

Hyperspectral Imaging with Multivariate Optical Elements



Wavelength (nm)

PL

S R

eg

res

sio

n

+

-

PLS (or Optical) Regression

Op

tica

l Reg

res

sio

n (M

1 –

M2

)

Detection

–

MOE2

=

MOE1

Scene

Narrow Bandpass

or Tunable Filters

1. Narrow band images are

measured (HSI data cube)

2. Regression is applied to

each pixel (or spectrum) to

yield analyte score image

Multivariate

Optical Element(s)

1. MOE intensity images are

measured

2. Simple image math is

performed to yield analyte

score image

The MOE Platform vs. Band Pass Filters

Feature Benefit(s)

• Higher sensitivity than traditional band

pass filters

• Pure optical amplification of analyte signal permits

lower detection limits

• Higher specificity than traditional band

pass filters

• Reduced crosstalk

• Multiplexing opportunities (more analytes can be

detected simultaneously) in complex mixtures

• Higher signal-to-noise ratio

measurement than traditional narrow

band pass filters

• Less sample material (smaller volume) can be used

• Less expensive/powerful subcomponents may be used

• Measurement flexibility • Environmental interference compensation may be

rolled up into the MOE design



Multivariate Optical Elements can increase the sensitivity and

specificity of analyte detection compared to band pass filters.

cirtemo_overview_with hyperspectral example-pre-nda

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