APPLICATIONS OF RAMAN AND MINIATURIZATION IN INDUSTRIAL MINIATURIZATION IN INDUSTRIAL AND BIOSYSTEMS APPLICATION:

BRIAN MARQUARDT CPAC SUMMER INSTITUTEWESLEY THOMPSON JULY 17TH, 2008

Applied Optical Sensing Labpp p g

Raman Sampling ApplicationsRaman Sampling Applications

Raman ScatteringRaman Scattering

• Two types of scatter of electromagnetic radiation occur

• Elastic (Rayleigh scatter, very intense)as c ( ay e g sca e , ve y e se)• Inelastic (Raman scatter, weak phenomenon)

vibrational informationd id if l lused to identify molecules

Diagram of a Raman InstrumentDiagram of a Raman Instrument

E it tiExcitationOptical Fiber

CCDLaser Filtered

Probe Spectrograph

CCDDetector

Lens

Sample

Computer

Raman and IR SpectraRaman and IR Spectra

• Raman spectroscopy gives us information about the vibrational energies of molecules

• Raman is complementary to IR, producing relatively stronger peaks for symmetrical stretching vs. anti-symmetrical stretching modesy g y g

• Raman spectra tend to be less cluttered than IR, much less affected by water and it is usually easier to implement/sample than IR

Advantages of Raman Spectroscopy

• Little or no sample preparation is required

Advantages of Raman Spectroscopy

Little or no sample preparation is required • Water is a weak scatterer - no special accessories are needed for measuring

aqueous solutions • Water and CO vapors are very weak scatterers purging is unnecessary• Water and CO2 vapors are very weak scatterers - purging is unnecessary • Inexpensive glass sample holders, non-invasive probes and immersion probes

are ideal in most cases ib i ( 100' f i l h) b d f l• Fiber optics (up to 100's of meters in length) can be used for remote analyses

• Since fundamental modes are measured, Raman bands can be easily related to chemical structure (very good for fingerprinting)

• The standard spectral range reaches well below 400 cm-1, making the technique ideal for both organic and inorganic species g g p

• Raman spectroscopy can be used to measure bands of symmetric linkages which are weak in an infrared spectrum (e.g. -S-S-, -C-S-, -C=C-)

Disadvantages of RamanDisadvantages of Raman

• Inherently not sensitive (need ~ 1 million incident photons to generate 1 Raman scattered photon)

• Fluorescence is a common background issue• Typical detection limits in the parts per thousand

range• Fluorescence Probability versus Probability of

Raman Scatter ( 1 in 103 105 vs 1 in 107 1010)Raman Scatter ( 1 in 103-105 vs 1 in 107-1010)• Requires expensive lasers, detectors and filters

Analysis of a Batch Fermentation Process

Real time Fermentation Yeast Fermentation Process

y

Real-time FermentationMonitoring

Yeast Fermentation Process

Image from Purves et al., Life: The Science of Biology, 4th Edition g gy

Raw Raman Data for Fermentation Batch Reaction (8 day run)

F t ti R R S tF t ti R R S t

Batch Reaction (8 day run)

3000

3500Fermentation Raw Raman Spectra

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3500Fermentation Raw Raman Spectra

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Int

0 200 400 600 800 1000 1200 1400 1600 18000

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0 200 400 600 800 1000 1200 1400 1600 18000

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0 200 400 600 800 1000 1200 1400 1600 1800Raman Shift (cm-1)

0 200 400 600 800 1000 1200 1400 1600 1800Raman Shift (cm-1)

• 10 second acquisition, 20 accumulations, sample every 10 minutes • Analysis was run continuously for 8 days

Raman Data After Fluorescence Correction Algorithm Applied

F e r m e n t a t i o n # 3 R a m a n F l u o r e s c e n c e C o r r e c t e d S p e c t r a

Correction Algorithm Applied

4 0 0 0

4 5 0 0

y

F e r m e n t a t i o n # 3 R a m a n F l u o r e s c e n c e C o r r e c t e d S p e c t r a

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ecte

d In

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esce

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re

4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0 1 4 0 0 1 6 0 0 1 8 0 0

-5 0 0

R a m a n S h i f t (c m -1 )

3D Plot of Corrected Raman Data for F t ti B t h R tiFermentation Batch Reaction

Ethanol

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nsity

me)

um N

umbe

r (~ti

me)

MaltoseMaltose

Raman Shift (cm-1)

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trum

Reactants/Products CurvesFlourescence Corrected Raman Signals for 3 Maltose and 1 Ehtanol Peaks

Reactants/Products Curves

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nsity Maltose 540 cm-1

Maltose 911 cm-1Maltose 1120 cm-1Product

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ed I Ethanol 875 cm-1Product

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Elapsed Hours

Experiment Descriptionp p

Created a designed experiment that spanned the g p pconcentrations of interest5 sugars at 4 different concentrations:

Arabinose, Cellobiose, Galactose, Glucose, Xylose0, 0.5, 1 and 2 % of each sugar (D-Optimal mixture model)

Collected spectra of these 43 solutions on each Collected spectra of these 43 solutions on each instrument:

Agilent Dielectric Network Analyzer (500 MHz to 50 GHz)785nm Kaiser Holoplex Raman532nm Kaiser Rxn3 RamanR tIR FT IRReactIR FT-IR

Di l i S

500 MH 50 GH

Dielectric Spectroscopy

500 MHz to 50 GHz1000 points per spectrum (50MHz spacing)

"'

0rrr jεεε

εεκ −===Dielectric Theory

3580

0y

25

30

60

70

A measure of how much energy from an external electric field is

e’

15

20

e''

40

50

e'

from an external electric field is stored in a material

Called the loss factor, a measure of how dissipative or lossy a material is to an external

e’’

5

10

20

30 electric field

0 1 2 3 4 50

GHz0 1 2 3 4 510

GHz

Ionic Polarization

N

Electronic or Atomic Polarization Orientation Polarization (Dipole Rotation) T

Na+Cl- Cl-Cl- Cl- Cl-Cl-

Cl-Cl-Na+

Na+

Na+

Na+

Na+

Na+

Na+ E+-

+ -−

+

EF

F

Dielectric e’ spectra from full modelp

Not sure if this is calibration shifting or due to sugars affecting dielectricaffecting dielectric

Glucose e’ Model DS

Arabinose e’ Model DS

Spread in Arabinose is typical of the other four sugars

Water e’ Model DS

Modeling just the concentration of water is a much tighter model. Could be useful as a total sugars measurement.

Dielectric e” spectra from full modelp

Same as e’, could be calibration shiftbe calibration shift.

Glucose e” Model DS

Cellobiose e” Model DS

Spread typical of the other sugars.

Water e” Model DS

Again, total sugar concentration is tighter model

R d L R

785 170 W l

Red Laser Raman

•785nm – 170mW at sample•10 second exposure, 5 accumulations, 5

li treplicates

U d fi i i d b d 4th•Used fingerprint region and subtracted 4th

order polynomial to improve model fit

Raman 785nm Raw Spectrap

Used these peaks for fingerprinting. Subtracted a 4th

order polynomial from data to b li b f d libaseline before modeling.

Raman 785nm Corrected Spectrap

Glucose Model 785nm

Galactose Model 785nm

• Typical of all five sugars• Water model correlation similar to sugar modelssimilar to sugar models

G L R

532 170 W l

Green Laser Raman

•532nm – 170mW at sample•10 second exposure, 5 accumulations, 5

li treplicates

U d fi i i•Used fingerprint region•Attempted both baseline and SNV correction to

fl b f d liremove fluorescence before modeling

Raman 532nm Raw Spectrap

Baseline Corrected 532 Base

Glucose Model 532 Base

• Typical of all five sugars• Water model correlation similar to sugar modelssimilar to sugar models

SNV Corrected 532 SNV

Glucose Model 532 SNV

Xylose Model 532 SNVy

• Typical of all five sugars• Water model correlation similar to sugar modelssimilar to sugar models

IR S

U d f l d h C H d

IR Spectroscopy

•Used parts of spectrum related to the C-H and C-O stretch to improve model

IR Spectrap

-OH stretch Water

-OH from Sugar C-O Stretch region

CH Stretching

H2O bending

H2O vaporCO2

Glucose Model IR

• Typical of all five sugars• Water model correlation similar to sugar modelssimilar to sugar models

Water Model IR

Summaryy

For these simple solutions, all four instruments are For these simple solutions, all four instruments are able to collect usable dataThe Raman data requires some preprocessing to The Raman data requires some preprocessing to remove background before tight PLS models can be createdDielectric data is more scattered, possibly could tell concentration of total sugars but identification of individual sugars may prove challenging

PLS Correlations

Correlation R^2 Correlation R^2 Correlation R^2 Correlation R^2Arabinose 0.729 0.532 0.710 0.504 0.998 0.997 0.988 0.977C ll bi 0 702 0 492 0 734 0 538 0 986 0 973 0 980 0 961

Raman 532Baselined SNV

Dielectrice' e''

Cellobiose 0.702 0.492 0.734 0.538 0.986 0.973 0.980 0.961Galactose 0.644 0.415 0.670 0.450 0.997 0.993 0.983 0.966Glucose 0.633 0.401 0.678 0.460 0.983 0.967 0.969 0.939Xylose 0.884 0.782 0.882 0.777 0.994 0.990 0.978 0.956Water 0 994 0 989 0 988 0 977 0 983 0 965 0 982 0 965Water 0.994 0.989 0.988 0.977 0.983 0.965 0.982 0.965

Correlation R^2 Correlation R^2

Raman 785 IR From these data it seems that the optical measurements outperform the dielectric. HoweverCorrelation R 2 Correlation R 2

Arabinose 0.986 0.973 0.996 0.993Cellobiose 0.986 0.972 0.951 0.904Galactose 0.990 0.980 0.958 0.917Glucose 0.992 0.983 0.941 0.885

measurements outperform the dielectric.  However it must be remembered that in an actual hydrolysate or fermentation liquor there may be fluorescence problems that overwhelm the optical signal of the sugars

Xylose 0.990 0.978 0.997 0.993Water 0.987 0.973 0.987 0.974

Future Work

Create “unknown” solutions for analysis with current Create unknown solutions for analysis with current PLS models for validationEvaluate different processing steps to improve Evaluate different processing steps to improve models. Options include multiple preprocessing steps (Polynomial subtraction and SNV correction, p ( yetc.) and focus on smaller regions of the spectrumAssess feasibility of these instruments in more complex solutions

All work and no play…p y

A k l d tAcknowledgementsCPACCPAC

Charlie Branham – CPAC/APLDuPont

John SteichenJames Cronin

AgilentAgilentRoger StancliffShelley Begley

UW ForestryRick GustafsonRenata BuraRenata Bura