Beer-omics: Molecular Fingerprinting of Craft Beers by High Resolution Mass Spectrometry

Transcription

1 Beer-omics: Molecular Fingerprinting of Craft Beers by High Resolution Mass Spectrometry Christine A. Hughey Department of Chemistry & Biochemistry James Madison University Harrisonburg, VA CoSMoS, September 11, 2012

2 Things You Should Know about JMU 150+ chemistry and biochemistry majors 30+ graduates per year JMU s NSF REU program is entering its 3rd decade JMU ranks 9th among PUIs whose graduates earn Ph.D.s in STEM fields

3 LC/MS Undergraduate Research Center at JMU 1290 UHPLC TOF 1200 Rapid Res LCs QQQ 1290 UHPLC q-tof NSF MRI, Sept 2009 NSF MRI, January 2010

4 Our Research Negative Ion Electrospray Food Metabolomics/ Differential Analysis Fundamentals Almonds Cranberry Mechanisms Ionization Efficiency Cocoa Grapes Role of modifiers Role of solvent Beer Analyte Properties

5 Mikkeller s Single Hop American-Style India Pale Ales (IPAs) QC Identical malts and yeast was used in all the Mikkeller beers. Malt: 67% Pilsner, 11% Cara-Crystal, 11% Munich II, 11% Flaked Oats Yeast: American Ale (Wyeast 1056/WLP099) 6.9% ABV ~100 IBUs --Reported by Mikkel (the brewer) on the Beer Brewing Network.com

6 Mikkeller s Single Hop India Pale Ales (IPAs) 21 Single Hop Beers 6 hops were used in two separate runs (2010 & 2011) 1 Quality Control = Green Flash West Coast IPA 28 BEERS TOTAL 2010 Amarillo Cascade Centennial Chinook Nelson Sauvin Nugget Simcoe Tomahawk Warrior 2011 Amarillo Bravo Cascade Centennial Challenger Cluster Columbus East Kent Galena Magnum 2011 Mt. Hood Nugget Palisade Simcoe Super Galena Tettnanger Warrior Willamette

7 Objectives Utilize stringent quality controls in order to continuously monitor data quality Six replicates of the WC IPA were obtained on each data collection day to monitor retention time reproducibility, mass accuracy and instrument response Obtain a molecular fingerprint for each single-hop Mikkeller IPA Use these fingerprints to: Develop a predictive model to identify hops in beers Test this model on single hop beers and beers made with hop mixtures Use this data set to teach metabolomic techniques to undergraduates.

8 Timeline Summer Mikkeller Beers Fall Mikkeller Beers Spring & 2011 Mikkeller Beers TOF Summer & 2011 Mikkeller Beers Q-TOF + I.S.

9 Beer Sonicate, filter, aliquot into cryovials, freeze at -80 C 1290 UHPLC TOF Workflow (Spring 2012) Molecular fingerprinting w/ Agilent s Mass Profiler Professional software X3 Negative ion ESI Targeted Quantitation

10 Overlaid TICs: Quality Control WC IPA 9 data sets collected over 4 weeks (Spring 2012) new LC column After 3 weeks Retention Time (min.)

11 Targeted Quantitation: WC IPA (QC) Catechin Quercetin Kaempferol Quercetin-3-O-rutinoside Kaempferol-3-O-glucoside Quercetin-3-O-glucoside (not shown)

12 Statistics for WC IPA (QC) on TOF MS 51 Injections, 9 data sets collected over 4 weeks 1290 LC MS Response Mass Accuracy (ppm) Compound RT % RSD Intraday % RSD Interday % RSD Min. Max. Avg. Catechin 0.42% 1.48% 11.8% Epicatechin 0.36% 1.66% 12.2% Quercetin-3-Orutinoside Quercetin-3-Oglucoside Kaempferol-3-Oglucoside 0.33% 1.85% 12.2% % 1.24% 11.3% % 2.11% 12.9% Quercetin 0.29% 4.21% 16.3% Kaempferol 0.28% 3.56% 22.8%

13 Data Analysis: WC IPA (QC) Raw Data Targeted Quantitation Determine data extraction parameters Mass window: 50 ppm RT window: 0.2 min. Molecular Feature Extraction MF must be present in all triplicate samples Data Filtering Eliminate one-hit wonders Identify outliers among data sets Robustness Analysis Principle component analysis & hierarchical clustering

14 WC IPA PCA in MFs Found in all Sample Replicates 3/19 3/21 4/12 3/22 3/28 4/16 4/18 3/29 4/19

15 WC IPA Clustering by Data Collection Day Data normalized to quercetin-3-o-rutinoside 3/19 3/21 3/22 3/28 3/29 4/16 4/18 4/19 4/12

16 Mikkeller Single Hop IPAs 2010 vs 2011: Warrior 4/19 4/18

17 Mikkeller Single Hop IPAs vs. WC IPA Un-normalized data Amarillo Amarillo Columbus Willamette Bravo Galena Sup. Galena Magnum Challenger Mt. Hood Cluster Nugget Palisade Tettnanger Warrior Simcoe Cascade Centennial WC IPA Centennial Nugget Simcoe Chinook East Kent Nelson Sauv Warrior Tomahawk Cascade

18 Mikkeller Single Hop IPAs PCA on MFs Found in all Sample Replicates 2011 Beers 2010 Beers 4/12

19 Beer Sonicate, filter, aliquot into cryovials, freeze at -80 C Randomize samples, 10 µl 50 ppm 1-naphthoic acid (i.s.) HO O Workflow (Summer 2012) Molecular fingerprinting w/ Agilent s Mass Profiler Professional software X UHPLC q-tof 10 ppm mass window Negative ion ESI Targeted Quantitation

20 Statistics for WC IPA (QC) on q-tof MS 66 Injections, 11 data sets collected over 5 weeks

21 Internal Standard Statistics Avg. Intraday RSD = 5.8% Interday RSD = 22.8%

22 WC IPA PCA in MFs Found in all Sample Replicates Data normalized against internal standard 6/27 6/22 6/25 6/15 6/17 6/19 7/23 6/29 6/28

23 WC IPA Clustering by Data Collection Day Data normalized against internal standard 6/15 6/22 6/25 6/17 6/19 6/27 6/29 7/18 7/23 7/28 7/2

24 Mikkeller Single Hop IPAs PCA on MFs Found in all Sample Replicates 2010 Beers 2011 Beers

25 Class Prediction: Partial Least Squares Discrimination Analysis (PLSD) Mikkeller IPAs Build Model Measure Accuracy of Model WC IPA Known Mikkeller IPAs Unknown Mikkeller IPAs Agilent Application Note: Metabolomic Profiling of Wines using LC/QTOF MS and MassHunter Data Mining and Statistical Tools

26 WC IPA Hop Recipe Simcoe = 2.75 oz. Columbus = 2.25 oz. Cascade = 1.0 oz. Amarillo = 0.5 oz. Centennial = 0.5 oz. Amount Name Form Alpha IBU Boil Time 0.50 oz. Simcoe Pellet min 0.25 oz. Columbus Pellet min 0.25 oz. Simcoe Pellet min 0.25 oz. Columbus Pellet min 0.25 oz. Simcoe Pellet min 0.75 oz. Columbus Pellet min 0.75 oz. Simcoe Pellet min 1.00 oz. Cascade Pellet min 0.50 oz. Columbus Pellet min 0.50 oz. Simcoe Pellet min 0.50 oz. Amarillo Pellet Dry Hop 0.50 oz. Centennial Whole Dry Hop 0.50 oz. Columbus Whole Dry Hop 0.50 oz. Simcoe Pellet Dry Hop

27 Class Prediction: 2010 Mikkeller Beers vs. WC IPA Amarillo Cascade Centennial Chinook Nelson Sauvin Nugget Simcoe Tomahawk Warrior? Simcoe Prediction conducted in Summer 2011

28 Class Prediction: 2010 & 2011 Mikkeller Beers vs. WC IPA Amarillo Cascade Centennial Chinook Nelson Sauvin Nugget Simcoe Tomahawk Warrior Bravo Challenger Cluster Columbus East Kent Galena Mt. Hood Magnum Palisade Supper Galena Tettnanger Willamette? 3/19 Simcoe 11 3/21 Centennial 11 3/22 East Kent 3/28 Bravo 4/12 Columbus 3/29, 4/18, 4/19 Warrior 11

29 Class Prediction: Partial Least Squares Discrimination Analysis (PLSD) Training Algorithm Outputs Model w/ Mikkeller beers only Model w/ Mikkeller beers + WC IPA WC IPAs

30 Class Prediction: 2010 & 2011 Mikkeller Beers Unknown # Predicted Result Mikkeller Beer Accuracy 1 Amarillo 2010 Amarillo % 2 Tomahawk Tomahawk 100% 3 Centennial 2011 Centennial % 4 Amarillo 2011 Amarillo % 5 Warrior 2010 Warrior % 6 Warrior 2011 Warrior % 7 Centennial 2010 Centennial % 8 Columbus Columbus 100%

31 Bitterness: Iso-α acids Conventionally measured with a spectrophotometric method West Coast IPA IBU (bitterness units) = 95 out of 100 TIC Response EIC Retention Time (min.)

32 Bitterness: Iso-α acids Adhumulone & Humulone (Summed m/z 361.2) Response in Mikkeller/Response in WC IPA

33 Identification of MFs Raw Data Targeted Quantitation Molecular Feature Extraction Data Filtering Database Searching

34 Identification of MFs 10,901 unique MFs in 27 Mikkeller beers JMU Flavonoid DB (~1700 cmpds) METLIN Metabolite DB 8,329 Hits (70%) phenolic acids (e.g., coumaric, caffeic, ferulic, syringic), long chain unsaturated acids, polyphenolics, prenylflavonoids, di- and tri-peptides, glucuronides, punagladins

35 Conclusions Off-the-shelf software was utilized to fingerprint 28 craft beers PCA and hierarchical clustering analyses were used to: Identify outlier days in data collection Differentiate between Mikkeller production batches (2010 vs. 2011) Class prediction was used to: Attempt to identify the main hop in WC IPA Identify unknown Mikkeller beers This unique data set teaches students: The importance of stringent quality controls How to manipulate large data sets

36 Future Work Identify MFs unique to each hop through MS/MS experiments. Match MS/MS spectra to the METLIN library Continue development of the class prediction model so that it better accommodates mixtures. Collect data in positive ESI mode and compare to negative mode. Combining positive and negative ESI data may increase the robustness of the model by expanding the classes of compounds ionized. Publish results!

37 Acknowledgements Agilent Technologies: Nathan Miller Mike Scott Andy Gieschen Rick Reisdorph (MPP Training) Steve Fischer Elaine Ricicki Bruce Wilcox NSF CHE NSF CHE NSF REU Research Corp. Department Development Grant 7957