Discovery Based Analytics for Bio-Fuels...
Transcript of Discovery Based Analytics for Bio-Fuels...
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Discovery Based Analytics for Bio-Fuels Characterization and
Food Quality Assessment
Robert E. SynovecAssociate Chair, Graduate Program
Faculty Director, CPACDepartment of Chemistry, Box 351700
University of Washington Seattle, WA 98195
Email: [email protected]
CPAC Spring Meeting, Seattle WAMay 3, 2010
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• Fundamental Studies of Separation Science Principles and Metrics
• Instrumentation and Sensor Development
• Data Analysis – Chemometrics – Software
• Methodology Design and Optimization
Advances in Separation Science Knowledge and Technology:
10 nm
Synovec Research Groupand Separation Technology
………from high-speed analysis of simple mixtures to the analysis of complex samples
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Chemical Analysis Challenge:At the discovery-stage can we, with statistical confidence, findsignificant changes in chemical composition (from very large to very small) in extremely complex samples, for example, biological samples with literally 100’s to 1000’s of compounds?!
• Health Biomarker Discovery, Clinical Application• Feed Stock and Biochem Evaluation (eg., Biomass to Fuel)• Forensics• Environmental Analysis • Industrial Discovery (eg., Biotech)• Food Quality, Safety and Security
Changes between…Healthy vs. Diseased, Wild-type vs. Mutant, One growth condition vs. another growth condition, “Good” biomass vs. “Poor” biomass, Optimal yeast strain,Poor vs. Good Quality
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Comprehensive Two-Dimensional Gas Chromatography (GC x GC)
FID
Sig
nal
15 Component Mixture:REAL-TIME separation into different chemical classes!
Column 1 (Non-Polar)–10-m x 320-μm i.d. –0.25-μm poly(5% diphenyl/ 95% dimethyl siloxane) –35°C initial, 120°C/min program, 25.5 psi H2
Column 2 (Polar)–2-m x 250-μm i.d. –0.2-μm cyanopropyl polysiloxane –100°C, 25.0 psi H2
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Comprehensive Two-Dimensional Gas Chromatography with Time-of-Flight Mass
Spectral Detection (GC x GC - TOFMS)
• Complete mass spectra ⇒peak identification
• Fast ⇒ 500 spectra / secondPeak widths on column two ~ 50 ms
• Adds another selective dimension ⇒ 3rd - order technique, benefit by
using chemometric software
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Ion
Cou
nts
Ion
Cou
nts
Ion
Cou
nts
Extracted Ion Chromatograms
m/z 217
m/z 128
m/z 73
m/z
Time 1
Data Cube
3rd Order Data• column 1 retention time • column 2 retention time • full mass spectrum at each point
We analyze the RAW data!….and apply Chemometrics
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Complex “Real” SampleIn
tens
ity
100’s of peaks…Concentration range is
~ factor of 100,000Many peaks overlap!
GC x GC
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Metabolomics
“Metabolomics is the study of the small molecules that are an integral facet of cell biology. The metabolites are inextricably connected to protein expression as manifested by gene regulation.”
“Metabolomics is emerging as possibly the most important of the “-omics” fields, providing complementary information in relation to the genomics and proteomics fields.”
…at the Discovery Stage ofthe process analysis effort….
Need to learn what to control, and determine what is right/wrong with a process or feedstock!
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Metabolomic analysis of fermenting and respiring yeast…..wild-type and mutants
High glucose
Ethanol
FermentingRepressed (R)
RespiringDerepressed (DR)
Metabolism:Transcription:
Low glucose
Ethanol
Collaboration with Ted Young, UW Biochemistry
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OO
O
O
O
H
H
H
H
D-Ribose
SiO
OSi
O
Si OSi
NO
D-Ribose Meox 4TMS
1) Oximation
2) Trimethyl-silylation
Methoximate and Trimethylsilylate:
(1) Reduce anomer formation (2) Increase volatility (3) Increase thermal stability (4) Decrease polarity
Derivatization - getting ready for GC x GC-TOFMS analysis
Metabolites:
Organic acidsAmino acidsSugars Sugar phosphates, etc
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Typical data, WT yeast grown in glucose conditions
GC x GC –TOFMS of Repressed Yeast Cell Extract, m/z = 73,Metabolites have been derivatized: m/z = TMS group is a “selective” channel
•Over 590 peaks at this m/z alone - Complex !•Many data runs…a huge amount of data to process !
ISSUES:
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Chemometric data analysis tools: utilize 3rd order data structure
(1) Discover sample-class distinguishing peak locations in 2D separation space
– Data reduction by a 3D Fisher ratio method
(2) Targeted peak analysis: 3D mathematical resolution, confirmedmass spectral compound identification and quantification
– PARAFAC GUI ….state-of-the-art software tools to apply powerful Linear Algebra concepts
Discovery-Based Approach: comprehensively explore the data using chemometric data reduction methods to “discover” the sample class -distinguishing compounds
From high throughput data reduction and analysis to valuable information !
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TCA Cycle
Glucose
Ethanol Acetyl CoA
glycolysis
Study Protein Function with Metabolomics (ΔSnf1 mutant study)
XX∆Snf1
cannot complete the shift
• Study this mutant strain at metabolome level• Wild type (R & DR)• Mutant (R & DR)
• In the absence of specific proteins (Snf1 Protein Complex) cells are unable to switch from using glucose to ethanol
~ 160 metabolites analyzedR- glucoseDR- ethanol
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0
0.00002
0.00004
0.00006
0.00008
0.0001
0.00012
0.5 2 4 6Time (hours)
Nor
mal
ized
(TIC
) PA
RA
FAC
vol
ume
Fumarate
• TCA Cycle is active in DR conditions• Snf1 protein complex needed to make shift from R
to DR conditions
TCA Cycle
Glucose
Ethanol Acetyl CoAglycolysis
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Metabolite wt
adr1∆
cat8∆
snf1∆
adr1∆
-ca
t8∆
• Metabolites were found that differ between wt and various mutant strains related to the diauxic shift (switch from glucose to non-fermentable carbon sources)
• Provides insight to which factors are most essential in the shift
These metabolites, specifically, highlight which strains are most similar to wt (adr1∆) and which are most different (cat8∆, adr1∆cat8∆,snf1∆) Included in this list are the TCA intermediates.
Wild type vs. mutants
Carbohydrate consumptionand metabolism… a tool to optimize ethanol production.
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0.0
0.5
1.0
1.5
2.0
2.5
stearic acid
citrate α-ketoglutaricacid
succinate fumarate malate
Rat
io to
wt
WTadr1∆cat8∆adr1∆cat8∆snf1∆
snf1∆metabolite and RNA ratios to wt show good agreement
TCA Cycle and Metabolite Intermediates
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End Products: Bio-Fuel Characterization
These tools can also be used to identify compounds that differ between various bio-fuels to provide insight into the chemical similarity of the fuels and to determine which fuels would be better “drop-ins” and/or what changes can be made to the process.
Traditional FuelBiofuel A
Biofuel B
Biofuel C
Biofuel D
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Food Quality Assessment Study
• Collaboration with Theo Chocolates – Organic, fair trade, “bean to bar” chocolate manufacturer
• Use analytical chemistry (and chemometrics) to help monitor/improve the process
BEAN BARFood Quality Assessment
for Raw Materials
www.theochocolate.com
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Chocolate Industry: Quality Control for Raw Materials
• Quality of raw materials is very important. Fermentation is not sufficiently controlled ….moisture damage is a major concern.
• Is there a way to distinguish high quality raw materials from poor quality prior to purchase and production?
• Are there “bio-marker” compounds, that can be readily correlated to bean quality, that are independent of bean origin?
Cacao Bean Nibs
E. M. Humston, Y. Zhang, G. F. Brabeck, A. McShea, R. E. Synovec, J. Sep. Sci., 2009, 32, 2289-2295.
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Experimental Design• To investigate effect of moisture damage
Fisher ratio analysis to find differences between damaged and undamaged beans
• Solid Phase Micro Extraction (SPME) to concentrate head space analytes (no derivatization needed)
unmolded
m/z
Col 1
molded
m/z
Col 1
unmolded molded
SurfaceChemistryMetabolism!
Predict Damage Early?!
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Headspace analysis – for volatile and semi-volatile compounds
• Solid Phase Microextraction (SPME)
• Headspace sampling
• GC x GC-TOFMS analysis
GC/MS
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Heat bean in vile in water bath to 60°C
Expose SPME fiber to bean headspace
vapor
GC x GC-TOFMS
Transfer SPME fiber to instrument for injection
SPME –
adapted toCacao BeanProject
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Differences can be Identified
UNMOLDED
MOLDED
UnmoldedMolded
Tetramethyl-pyrazine
0
1E7
2E7
3E7
4E7
5E7
6E7
1 2 3 4 5 6 7 8 9 10
Peak
Are
a
Bean Number
2,3-dimethyl-pyrazine
1 2 3 4 5 6 7 8 9 100
5.0E5
1.0E6
1.5E6
2.0E6
2.5E6
Bean Number
Others are consistently elevated in Molded Samples (pyrazine compounds have “earthy” odor quality)
Some analytes are consistently elevated in Unmolded Samples
Peak
Are
a
Acetic acid
02.0E7
4.0E76.0E78.0E7
1.0E81.2E81.4E81.6E81.8E8
1 2 3 4 5 6 7 8 9 10Bean Number
2-ethyl-1-hexanol
0
5.0E6
1.0E7
1.5E7
2.0E7
2.5E7
3.0E7
1 2 3 4 5 6 7 8 9 10Bean Number
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PCA for Classification…….Biomarkers Independent of Geographical Origin !
2‐methyl‐3‐Buten‐2‐oltetramethyl‐pyrazine, trimethyl‐pyrazine, 2‐nitro‐pentane, malonic acid, bis(2‐TMS ethyl ebenzenemethanol4‐cyanocyclohexenecyclopropane, 2‐(1,1‐dimethyl‐propanoic acid, 2‐methyl‐, 2,2‐2,5‐cyclohexadiene‐1,4‐dione, 2tridecane 2,6,10,14‐tetramethyl‐heptade2‐ethyl‐1‐hexanol, propanoic acid, 2‐methyl‐, 3‐hynonanalhexanoic acid
Costa RicaU
0
2
4
6
8
10
EcuadorIvory CoastM U M U M 5 10 15 20 25 300
0.1
0.2
0.3
Bean Number
Load
ings
on
PC
1 (
66.5
3%)
PCA Variables/Loadings from PARAFAC
Costa Rica
Ecuador Ivory Coast
unmolded
Col 2
m/z
Col 1
molded
Col 2
m/z
Col 1 x 3 Beans of Different Origin(U)
(M)
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Acknowledgements
*not pictured: Ryan Wilson, Emilie Viglino, Laura Snyder,Jason Reed
Funding and Support:
CPACNIH, Honeywell-SRI-DARPA, WTC, Theo Chocolate, PNNL, CRC, ChevronTexaco
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Process Gas Chromatography with Chemometrics
W. Christopher Siegler, Jeremy S. Nadeau, Elizabeth M. Humston, Ryan B. Wilson, Jamin C. Hoggard and Robert E. Synovec
Department of ChemistryUniversity of Washington
Seattle, WA
CPAC Sponsor Meeting, Seattle WAMay 4, 2010