Discovery Based Analytics for Bio-Fuels Characterization and Food Quality Assessment

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Synovec Associate Chair, Graduate Program Faculty Director, CPAC Department of

1 Discovery Based Analytics for Bio-Fuels Characterization and Food Quality Assessment Robert E. Synovec Associate Chair, Graduate Program Faculty Director, CPAC Department of Chemistry, Box University of Washington Seattle, WA CPAC Spring Meeting, Seattle WA May 3, 2010

Knowledge and Technology: Fundamental Studies

Instrumentation and Sensor Development Data

Methodology Design and Optimization from

2 Synovec Research Group and Separation Technology Advances in Separation Science Knowledge and Technology: Fundamental Studies of Separation Science Principles and Metrics Instrumentation and Sensor Development Data Analysis Chemometrics Software 10 nm Methodology Design and Optimization from high-speed analysis of simple mixtures to the analysis of complex samples

3 Chemical Analysis Challenge: At the discovery-stage can we, with statistical confidence, find significant 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?! 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 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

Comprehensive Two-Dimensional Gas Chromatography (GC x GC) 15

chemical classes! Column 1 (Non-Polar) 10-m x 320-μm i.d. 0.

4 Comprehensive Two-Dimensional Gas Chromatography (GC x GC) 15 Component Mixture: REAL-TIME separation into different chemical classes! Column 1 (Non-Polar) 10-m x 320-μm i.d μm poly(5% diphenyl/ 95% dimethyl siloxane) 35 C initial, 120 C/min program, 25.5 psi H 2 Column 2 (Polar) 2-m x 250-μm i.d. 0.2-μm cyanopropyl polysiloxane 100 C, 25.0 psi H 2 FID Signal

Comprehensive Two-Dimensional Gas Chromatography with Time-of-Flight Mass Spectral Detection (GC x GC - TOFMS) Complete mass spectra peak identification

5 Comprehensive Two-Dimensional Gas Chromatography with Time-of-Flight Mass Spectral Detection (GC x GC - TOFMS) Complete mass spectra peak identification Fast 500 spectra / second Peak widths on column two ~ 50 ms Adds another selective dimension 3 rd - order technique, benefit by using chemometric software

m/z Data Cube m/z 217 m/z 128 Ion Counts Ion Counts m/z 73

6 3 rd Order Data Extracted Ion Chromatograms column 1 retention time column 2 retention time full mass spectrum at each point m/z Data Cube m/z 217 m/z 128 Ion Counts Ion Counts m/z 73 Time 1 We analyze the RAW data!.and apply Chemometrics Ion Counts

7 GC x GC Complex Real Sample 100 s of peaks Concentration range is ~ factor of 100,000 Many peaks overlap! Intensity

Metabolomics at the Discovery Stage of the process analysis effort. Metabolomics is the study of the small molecules that are an integral facet of cell biology.

8 Metabolomics at the Discovery Stage of the process analysis effort. 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. Need to learn what to control, and determine what is right/wrong with a process or feedstock!

9 Metabolomic analysis of fermenting and respiring yeast..wild-type and mutants High glucose Low glucose Ethanol Ethanol Metabolism: Transcription: Fermenting Repressed (R) Respiring Derepressed (DR) Collaboration with Ted Young, UW Biochemistry

10 Derivatization - getting ready for GC x GC-TOFMS analysis D-Ribose D-Ribose Meox 4TMS H O H O O O H H O 1) Oximation 2) Trimethylsilylation Si O Si O Si O O Si N O Metabolites: Organic acids Amino acids Sugars Sugar phosphates, etc Methoximate and Trimethylsilylate: (1) Reduce anomer formation (2) Increase volatility (3) Increase thermal stability (4) Decrease polarity

11 GC x GC TOFMS of Repressed Yeast Cell Extract, m/z = 73, Metabolites have been derivatized: m/z = TMS group is a selective channel Typical data, WT yeast grown in glucose conditions ISSUES: Over 590 peaks at this m/z alone - Complex! Many data runs a huge amount of data to process!

12 Discovery-Based Approach: comprehensively explore the data using chemometric data reduction methods to discover the sample class -distinguishing compounds Chemometric data analysis tools: utilize 3 rd 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, confirmed mass spectral compound identification and quantification PARAFAC GUI.state-of-the-art software tools to apply powerful Linear Algebra concepts From high throughput data reduction and analysis to valuable information!

13 Study Protein Function with Metabolomics (ΔSnf1 mutant study) Ethanol Snf1 cannot complete the shift R- glucose DR- ethanol X Glucose Acetyl CoA X TCA Cycle glycolysis 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 analyzed

14 Fumarate Glucose Normalized (TIC) PARAFAC volume Time (hours) Ethanol glycolysis Acetyl CoA TCA Cycle TCA Cycle is active in DR conditions Snf1 protein complex needed to make shift from R to DR conditions

15 Wild type vs. mutants Metabolite wt adr1 cat8 adr1 cat8 snf1 Carbohydrate consumption and metabolism a tool to optimize ethanol production. 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.

TCA Cycle and Metabolite Intermediates Ratio to wt 2.5 2.0 1.5 1.0 WT adr1 cat8 adr1 cat8 snf1 0.5 0.

16 TCA Cycle and Metabolite Intermediates Ratio to wt WT adr1 cat8 adr1 cat8 snf stearic acid citrate α-ketoglutaric succinate fumarate malate acid snf1 metabolite and RNA ratios to wt show good agreement

Biofuel B Biofuel D These tools can also be

fuels and to determine which fuels would be

17 End Products: Bio-Fuel Characterization Biofuel A Biofuel C Traditional Fuel Biofuel B Biofuel D 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.

18 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 Food Quality Assessment for Raw Materials BAR

Is there a way to distinguish high quality raw materials from poor quality prior to purchase and production?

19 Chocolate Industry: Quality Control for Raw Materials Cacao Bean Nibs 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? E. M. Humston, Y. Zhang, G. F. Brabeck, A. McShea, R. E. Synovec, J. Sep. Sci., 2009, 32,

20 Experimental Design To investigate effect of moisture damage unmolded Predict Damage Early?! molded Solid Phase Micro Extraction (SPME) to concentrate head space analytes (no derivatization needed) Surface Chemistry Metabolism! unmolded molded m/z m/z Col 1 Fisher ratio analysis to find differences between damaged and undamaged beans Col 1

21 Headspace analysis for volatile and semi-volatile compounds Solid Phase Microextraction (SPME) Headspace sampling GC/MS GC x GC-TOFMS analysis

22 SPME adapted to Cacao Bean Project GC x GC-TOFMS Heat bean in vile in water bath to 60 C Expose SPME fiber to bean headspace vapor Transfer SPME fiber to instrument for injection

Differences can be Identified Some analytes are consistently elevated in Unmolded Samples UNMOLDED Peak Area 1.8E8 1.6E8 1.4E8 1.2E8 1.0E8 8.0E7 6.0E7 4.0E7 2.

23 Differences can be Identified Some analytes are consistently elevated in Unmolded Samples UNMOLDED Peak Area 1.8E8 1.6E8 1.4E8 1.2E8 1.0E8 8.0E7 6.0E7 4.0E7 2.0E7 0 Acetic acid Bean Number 3.0E7 2.5E7 2.0E7 1.5E7 1.0E7 5.0E6 0 2-ethyl-1- hexanol Bean Number Peak Area 5E7 4E7 3E7 2E7 1E7 2.0E6 1.5E6 1.0E6 5.0E5 Unmolded Molded Others are consistently elevated in Molded Samples (pyrazine compounds have earthy odor quality) MOLDED 6E7 2.5E6 Tetramethyl-pyrazine 2,3-dimethyl-pyrazine Bean Number Bean Number

24 PCA for Classification.Biomarkers Independent of Geographical Origin! (U) (M) 2 methyl 3 Buten 2 ol tetramethyl pyrazine, trimethyl pyrazine, 2 nitro pentane, unmolded molded malonic acid, bis(2 TMS ethyl e benzenemethanol 4 cyanocyclohexene cyclopropane, 2 (1,1 dimethylpropanoic acid, 2 methyl, 2,2 2,5 cyclohexadiene 1,4 dione, 2 tridecane 2,6,10,14 tetramethyl heptade 2 ethyl 1 hexanol, propanoic acid, 2 methyl, 3 hy nonanal hexanoic acid m/z m/z Col 1 Col 1 0 U Costa Rica EcuadorIvory Coast Col 2 Col 2 x 3 Beans of Different Origin Loadings on PC 1 (66.53%) PCA Variables/Loadings from PARAFAC Costa Rica Ecuador Ivory Coast 0 M U M U M Bean Number

25 Acknowledgements Funding and Support: CPAC NIH, Honeywell-SRI-DARPA, WTC, Theo Chocolate, PNNL, CRC, ChevronTexaco *not pictured: Ryan Wilson, Emilie Viglino, Laura Snyder, Jason Reed

26 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 Chemistry University of Washington Seattle, WA CPAC Sponsor Meeting, Seattle WA May 4, 2010

Improved Characterization of Perfumes with GC GC-TOFMS

Improved Characterization of Perfumes with LECO Corporation; Saint Joseph, Michigan USA Key Words: Pegasus 4D, Peak Capacity, Thermal Focusing, Structured Chromatograms, Perfume Analysis 1. Introduction