chromatography: feedstock characterization and fermentation monitoring of biofuels part 1
DESCRIPTION
(originally aired 11-14-12) Although biofuels are an attractive alternative to fossil fuels, large scale development is currently challenging. Development of renewable fuel characterization, processes, and contaminant analysis using robust analytical methods is needed. Here, focus is on Ion Chromatography—a proven technique for providing fast, reliable answers during research to production—with HPAE-PAD technology for carbohydrate analysis in feedstock and method parameter optimization (including column chemistry) for efficient separation of mono- and disaccharides with good resolution, linearity, and accuracy over a broad dynamic range. Since some residual sucrose and cellobiose may be present, examples of monitoring them and other saccharides is covered, along with their impact on the fermentation process.TRANSCRIPT
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1 The world leader in serving science
Paul Voelker November 14, 2012
Feedstock Characterization and Fermentation Monitoring of Biofuels Part 1
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Operation • Measures current or charge
resulting from the oxidation or reduction of analyte on a specific electrode surface.
• Oxidation—electrons go from the analyte to the electrode.
• Reduction—electrons go from the electrode to the analyte.
Amperometry
Electron Transfer
e-
e-
Analyte
Amperometry
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Oxidation of Glucose During Pulsed Amperometric (PAD) Detection
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Summary of Thermo Scientific™ Dionex™ CarboPac™ Column Application Areas
• Dionex CarboPac PA10/PA20/SA10 • Mono- and disaccharides • Samples with few alditols • Linear polysaccharides • Sialic acids • Small sample amounts
• Dionex CarboPac PA1 • Oligosaccharides (better on
Dionex CarboPac PA100) • Official methods based on PA1 • Colominic acid, inulin, and
amylopectins
• Dionex CarboPac MA1 • Alditols • Separation of rhamnose and
GalN • Methylated carbohydrates • Separation of GalNAc and
GlcNAc
• Dionex CarboPac PA100/PA200 • Branched oligosaccharides • Sialylated oligosaccharides • Mono- and disaccharides • Lysaccharides polysaccharides
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Thermo Scientific Dionex ICS-5000 Capillary HPIC System
High Pressure Ion Chromatography • High pressure capable with capillary
systems • Continuous operation up to 5000 psi
when configured as a Reagent-Free™ (RFIC™) system
HPIC—High Resolution, Fast Analyses
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Feedstock Analysis of Mono- and Disaccharides Using HPAE-PAD with On-Line Eluent Generation
Page 1 Archana Pandey 10/29/2012
Archana Pandey, Senior Research Associate Analytics and R&D, LS9 Inc.
Part 2, Feedstock Characterization and Fermentation Monitoring of Biofuels Webinar
November 14, 2012
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Objective Resolution of eight sugars
Sucrose, arabinose, galactose, glucose, xylose, mannose, fructose and cellobiose
Establish dynamic range (Thermo Scientific™ Dionex™ CarboPac™ PA20 and SA10 columns) to meet our application needs
Determine accuracy and reproducibility over desired dynamic range
Analysis
Cellulosic feedstock
Monitoring sugars in fermentation broth
2 Archana Pandey 10/29/2012
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Xylose, mannose, and galactose
Fructose and arabinose
Separation of Four Sugars: sucrose, glucose, xylose, & fructose in 20 min
Peak coelution with other sugars
3 Archana Pandey 10/29/2012
Traditional LC Method (RI)
Traditional LC Method (RI)
Refractive Index (RI) Detector
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Glucose, galactose, xylose, and mannose
Tandem 45-Min LC Method RI
Method Development—Conversion from Transitional to Tandem LC
Arabinose & mannose coleute
Tandem 45-Min LC Method RI
4 Archana Pandey 10/29/2012
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Thermo Scientific Dionex ICS-5000 System with Pulsed Amperometric Detection (PAD) & the Dionex CarboPac PA 20 Column
Arabinose, glucose, galactose, xylose, mannose, and fructose
5 Archana Pandey 10/29/2012
Cellobiose
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Calibration—Quadratic Fit/R2/Range on Dionex CarboPac PA20
Range: 2.5–80 ppm
6 Archana Pandey 10/29/2012
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Accuracy: 93–103% RSD ≤ 3.0%
7 Archana Pandey 10/29/2012
QC: Glucose at 50 ppm
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8 Archana Pandey 10/29/2012
Oligomers Real Sample Profile: 10,000-Fold Dilution
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9 Archana Pandey 10/29/2012
Feedstock Analysis: Lot X Analysis on Dionex Carbo Pac PA 20 Column
Sugars mg/L % Composition LS9 (w/w)
% Composition Lot X (w/w)
Arabinose 0.7 0.7 0.8
Galactose 2.1 2.0 2.2
Glucose 48.0 45.7 43.7
Xylose 4.8 4.5 6.0
Mannose 14.5 13.8 13.6
Fructose 4.84 4.6 6.1
TOTAL 74.9 71.3 72.4
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100/120 mg/L
100/120 mg/L
Injected on Dionex CarboPac PA 20 Column
Concn Solution of Feedstock/Sugar (w/v)
Archana Pandey 10/29/2012
Comparison (Volumetric and Electronic)
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11 Archana Pandey 10/29/2012
Comparison Volumetric Pipette (VP) and Electronic Pipette (EP)
Feedstock % Composition Lot X
% Composition LS9 (VP)
% Composition LS9 (EP)
Arabinose 1.50 1.15 1.19
Galactose 2.81 2.79 2.77
Glucose 47.82 42.68 43.23
Xylose 6.22 4.55 4.68
Mannose 22.59 18.87 19.31
Fructose 0.09 NA NA
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12 Archana Pandey 10/29/2012
Analysis of Unknown Mix of Sugar from External Source
Sample 1 Carb 36-CVS
Dionex System
g/L
External Source
g/L
Arabinose 9.70 10.01
Glucose 9.82 10.01
Xylose 9.93 10.01
Galactose 9.77 10.01
Mannose 9.87 10.01
Sugars Sample Analyzed by Dilution with Electronic Pipette Not Based on Weight
Concn Dionex System
Sample 1 Carb 36-CVS ~ 10 g/L each 1000-Fold Dilution with Electronic Pipette for Dionex System
Sample 2 Carb 36-Level X ~ 36 g/L each 2000-Fold Dilution with Electronic Pipette for Dionex System
Sample 1 Carb 36-Level X
Dionex System
g/L
External Source
g/L
Arabinose 35.57 36.01
Glucose 37.00 36.01
Xylose 35.07 36.01
Galactose 35.60 36.01
Mannose 34.78 36.01
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Reduce method run time—transition to Dionex CarboPac SA10 column
Increased dynamic range using:
0.4 nL internal loop (4-port pod)
Thicker, 15-ml gasket
Linear fit instead of quadratic fit
Good reproducibility and accuracy over desired dynamic range
Introduce internal standard (ISTD) ‘fucose’ to address injection variability
Further Optimization of Method
13 Archana Pandey 10/29/2012
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10/29/2012 Archana Pandey 14
Dionex CarboPac SA10 Column—Separation of Nine Sugars in 10 min Fucose, sucrose, arabinose, galactos, glucose, xylose, mannose, fructose,
and cellobiose
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Linear Fit: R2 – 0.998-0.999 (5–300 ppm)
15 Archana Pandey 10/29/2012
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Accuracy on Standards ≥ 98%
16 Archana Pandey 10/29/2012
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QC at 50 ppm for Better Accuracy
Accuracy 98–102% RSD ≤ 1.0 %
17 Archana Pandey 10/29/2012
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10/29/2012 Archana Pandey 18
RSQC at 100 ppm Sugars with ISTD Fucose
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10/29/2012 Archana Pandey 19
Feedstock Sample Profile
Fermentation Sample Profile Broth: 50-fold dilution
1000–2000-fold dilution
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Feedstock Analysis: Lot X Analysis on Dionex CarboPac SA 10 Column
20 Archana Pandey 10/29/2012
% Composition
Sugars
% Composition
Lot X
% Composition
Day 1
% Composition
Day 2
% Composition
Day 3
Arabinose 0.79 0.71 0.68 0.70
Galactose 2.22 2.05 1.97 2.03
Glucose 43.73 44.00 43.73 44.29
Xylose 6.02 5.38 5.26 5.29
Mannose 13.55 12.98 12.72 12.89
Fructose 6.08 4.61 4.53 4.49
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10/29/2012 Archana Pandey 21
Conclusion
Optimization of method suitable for analytical purposes
Established good accuracy and precision over the desired dynamic range
Routine analysis of feedstock and fermentation samples
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Rapid and Selective HPAEC-PAD Determination of Carbohydrates in Biomass Samples
Dr. Kevin Chambliss
Department of Chemistry and Biochemistry
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Biomass-to-Bioproduct Production Paradigm
Feedstock Chemical Pretreatment
Enzymatic Hydrolysis Fermentation
1. Total sugar (measured as monomers) after quantitative saccharification of potential feedstocks.
2. Free (monomers + sucrose) and total sugar in aqueous extracts of lignocellulosic feedstocks (total – monomeric = oligomeric).
3. Free and total sugar in pretreatment liquors.
4. Free and total sugar in enzymatic hydrolysates.
5. Free sugar in fermentation broths.
Routine Carbohydrate Analyses in Biofuels R&D:
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Current Practice Relies Heavily on HPLC-RI Methods to Interrogate Carbohydrates
0
20000
40000
60000
80000
100000
120000
0 5 10 15 20 25
Resp
once
(RIu
)
Retention Time (min)
sucr
ose
arab
inos
e gala
ctos
e
gluc
ose
xylo
se
man
nose
fruc
tose
cello
bios
e
Shodex Sugar SP0810 (Pb2+-form); 30 cm × 3 mm Eluent: H2O at 0.6 mL/min Column Temperature: 85 °C
Run times approaching 60 min…
Benefits of RI include: (1) direct injection of samples due to wide linear dynamic range; and (2) simultaneous monitoring of additional compounds due to universal detection…
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Significant Improvements in Run Time and Resolution Observed with HPAEC-PAD
0
5
10
15
20
0 4 2 6 10 8
mal
tose
cello
bios
e
gluc
ose
sucr
ose
arab
inos
e ga
lact
ose xy
lose
m
anno
se
fruc
tose
IS1
IS2 Re
spon
se (n
C)
Retention Time (min)
CO32−/HCO3
−-modified Thermo Scientific™ Dionex™ CarboPac™ PA20
1.0 mM NaOH(aq) at 0.5 mL/min Column Temperature: 40 °C
Sevcik, R.A. et al. J. Chromatogr. A 2011, 1218, 1236–1243.
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Inherent Sensitivity of PAD Required Sample Dilutions up to 1:2000 Prior to Analysis
Sevcik, R.A. et al. J. Chromatogr. A 2011, 1218, 1236–1243.
1 3 2 4 5
0
15
20
10
5 Inte
nsity
(nC)
IS =
1
1
3 2 4
5
1
3 2
4
5
0
Corn Stover
1:1000 1:600 1:400
1 3 2 4 5 0 1 3 2 4 5 0
Retention Time (min)
Switchgrass Poplar Wood
arab
inos
e =
2 ga
lact
ose
= 3
gluc
ose
= 4
xylo
se =
5
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Dionex CarboPac SA10 Stationary Phase Alleviates the Need for Column Modification
0
5
10
15
20
25
30
0 2 4 6 8 10 12
Resp
once
(nC)
Retention Time (min)
sucr
ose
arab
inos
e ga
lact
ose
gluc
ose
xylo
se
man
nose
fr
ucto
se
cello
bios
e
Dionex CarboPac SA10; 25 cm × 4 mm 1.0 mM NaOH(aq) at 1.5 mL/min
Column Temperature: 45°C
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Thermo Fisher Scientific Has Also Addressed Sample Dilution Requirements Affiliated with PAD
2 mil → 62 mil gasket
10 μL → 400 nL internal sample loop
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Interlaboratory Comparison of Novel HPAEC-PAD Methodology
Richard Sevcik
Lipika Basumallick and Jeff Rohrer
Deb Hyman and Chris Scarlata
ACADEMIC
INDUSTRY
GOVERNMENT
Instrument Configuration: Thermo Scientific Dionex ICS-3000 equipped with an eluent generator, autosampler, low-volume injector, Dionex CarboPac SA10 column, large-volume PAD detection cell.
Objectives: (1) Verify the linear range of the PAD detector.
(2) Evaluate interlab reproducibility with ‘real’ samples.
(3) Compare concentrations determined via HPAEC-PAD with HPLC-RI.
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Linear Dynamic Range Spanned 1.0–1.5 Orders of Magnitude Independent of Analyte
10
11
12
13
14
0.0 1.0 2.0 3.0 4.0 5.0
Resp
onse
Fac
tor
Concentration (g/L)
Linear response region for glucose
0 2 4 6 8
10 12 14 16
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Resp
onse
Concentration (g/L)
± 5%
Glucose calibration curve (0.020–3.0 g/L) r2 = 0.9992
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Comparable Calibration Sensitivities Were Observed at Each Test Site (n = 6)
glucose Site 1 Site 2 Site 3
galactose Site 1 Site 2 Site 3
xylose
arabinose
fructose
mannose
sucrose
cellobiose
4.85 4.79 5.54
5.61 5.48 5.64
5.11
4.63
2.88
4.34
2.64
3.66
Slope Intercept 0.080 0.370 0.444
0.114 0.365 0.311
0.192
0.139
0.198
0.262
0.165
-0.086
0.9992 0.9972 0.9943
0.9995 0.9976 0.9958
0.9979
0.9992
0.9983
0.9967
0.9985
0.9992
r2 LOQ (102 g/L) 1.7 0.46 1.4
1.7 0.46 1.4
1.8
2.0
1.7
2.1
3.3
3.0
LOQs in Sevcik et. al were reported in units of 101 μg/L
10 μL injection; 2 mil spacer
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21 Opportunistic R&D Samples Were Analyzed to Support Reproducibility Comparisons
Feedstock Compositional Analysis (4): (2-stage hydrolysis w/ H2SO4…) 2 corn stover, miscanthus, NIST bagasse
Pretreatment Hydrolysates (8): (monomeric and total sugar…) 2 vertical reactor, steam gun, slurry
Saccharification/Fermentation Samples (8): (monomeric sugar…) 3 saccharification at T0 and 5 fermentation at Tf
Synthetic Sample (1): glucose, xylose, acetic acid, furfural, and 5-HMF in 0.7% H2SO4
Dilution Factor
No Dilution
1:50 or 1:10
1:10
1:50
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Excellent to Good Reproducibility Observed Between Labs for More Abundant Sugars
y = 1.00x
0 5
10 15 20 25 30 35
0 5 10 15 20 25 30 35
Site
2
Site 1
y = 1.08x
0 5
10 15 20 25 30 35
0 5 10 15 20 25 30 35
Site
3
Site 1
Mean Glucose (g/L) Mean Xylose (g/L)
y = 0.95x
0
20
40
60
80
100
0 20 40 60 80 100
Site
2
Site 1
y = 0.98x
0
20
40
60
80
100
0 20 40 60 80 100
Site
3
Site 1
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Similar Reproducibility Observed Between Labs for Quantitation of Minor Sugars
y = 1.02x
0
3
5
8
10
13
15
0 3 5 8 10 13 15
Site
3
Site 1
y = 0.99x
0
3
5
8
10
13
15
0 3 5 8 10 13 15
Site
3
Site 2
Mean Arabinose (g/L)
y = 1.04x
0
1
2
3
4
5
0 1 2 3 4 5
Site
3
Site 2
Mean Galactose (g/L)
y = 1.17x
0
1
2
3
4
5
0 1 2 3 4 5
Site
3
Site 1
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Concentrations of Major Sugars Determined via HPAEC-PAD Generally Agreed with HPLC-RI Data
y = 0.94x
0 5
10 15 20 25 30 35
0 5 10 15 20 25 30 35
HPL
C-RI
HPAEC-PAD
y = 0.92x
0
20
40
60
80
100
0 20 40 60 80 100
HPL
C-RI
HPAEC-PAD
Mean Glucose (g/L) Mean Xylose (g/L)
As sugar concentrations increased in test samples, HPAEC-PAD data trended high relative to concentrations determined via HPLC-RI.
Correlations for minor sugars deviated even further from the expected trend in both positive and negative directions.
Co-eluting species can cause RI information to be erroneous in either direction…
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Raw Data Strongly Support the Possibility of False Positives When HPLC-RI Is Used
Site 1 Site 2 Site 3 HPLC-RI Site 1 Site 2 Site 3 HPLC-RI Site 1 Site 2 Site 3 HPLC-RI
arabinose
ND ND ND
9.1(1)
5.41(8) 5.75(9) 5.65(1)
6.534(4)
11.3(5) 11.0(1)
10.81(1) 12.5(2)
galactose
ND ND ND
4.925(8)
2.250(3) 2.63(2) 2.65(1) 3.33(1)
ND ND ND
5.9(3)
glucose
9.5(2) 10.3(1) 10.7(2)
9.62(5)
12.28(2) 13.603(2)
14.18(7) 13.36(9)
24.8(5) 25.3(2) 27.3(1)
24.31(9)
xylose
83.2(4) 82.5(3) 83.4(7) 75.8(5)
40.9(4)* 37.1(3)*
36.94(5)* 43.06(1)
78.0(5) 80.5(5)
80.86(7) 74.2(4)
cellobiose
ND ND ND
1.66(2)
ND ND ND
1.26(2)
ND ND ND
2.55(2)
fructose
ND ND ND
1.3(2)
2.5(2) 2.42(2) 2.87(5) 2.22(2)
ND ND ND
4.2(2)
Frequency 9 12 4 0 2 7
PAD
PAD
PAD
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Concluding Remarks
• HPAEC-PAD is a superior approach to HPLC-RI for determination of sugars in biomass samples.
• The Dionex CarboPac SA10 stationary phase offers significant improvements in run time and resolution, especially of sucrose, relative to the Shodex SP0810 column (Pb2+-form).
• A novel method, utilizing a Dionex CarboPac SA10 column in combination with a low-volume injection valve and high-volume detection cell, proved to be both robust and reproducible in an interlaboratory comparison.
• Hardware modifications engineered at Thermo Fisher Scientific reduced PAD sensitivity such that samples of interest could be analyzed at reasonable dilution levels.
• False positives are likely when HPLC-RI methods are used to interrogate sugars in samples derived from biomass, especially for sugars present at low levels.