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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
3000
3500Fermentation Raw Raman Spectra
2000
2500
tens
ity
2000
2500
tens
ity
1000
1500
Int
1000
1500
Int
0 200 400 600 800 1000 1200 1400 1600 18000
500
0 200 400 600 800 1000 1200 1400 1600 18000
500
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
2 5 0 0
3 0 0 0
3 5 0 0
ecte
d In
tens
ity
1 0 0 0
1 5 0 0
2 0 0 0
esce
nce
Cor
re
0
5 0 0
1 0 0 0
Fluo
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
Inte
nsity
me)
um N
umbe
r (~ti
me)
MaltoseMaltose
Raman Shift (cm-1)
Spec
trum
Reactants/Products CurvesFlourescence Corrected Raman Signals for 3 Maltose and 1 Ehtanol Peaks
Reactants/Products Curves
4000
4500
3000
3500
Inte
nsity Maltose 540 cm-1
Maltose 911 cm-1Maltose 1120 cm-1Product
2000
2500
Cor
rect
ed I Ethanol 875 cm-1Product
500
1000
1500Reactants
20 40 60 80 100 120 140 160 180 200
500
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