The Impact of Metabolomics
on Flavor Chemistry
Josephine Charve and Gary Reineccius
Department of Food Science and Nutrition
University of Minnesota
Flavoromics

My definition - The application of
chemometrics to the study of a broad
array of chemical stimuli involved in
forming human flavor perception.
Historically – volatiles were the
primary interest of the flavor
chemist
Chewing gum - menthone,
sucrose and perceived intensity
(Davidson et. al, 2000)
Aroma
Sensory and
Sucrose
Perception is multimodal
Mouthfeel
Olfaction
Texture
Taste
Perception
Sound
Appearance
Experience
Why take this approach – value?
 Improved
prediction of sensory
properties
 Better product characterization
 Discovery - statistically linking stimuli
to perception
 New
contributors to perception
 Understanding of pathways leading to
stimuli
Why now?

Developments in “omics” are driving the
development (and availability) of
instrumentation, approaches, and data
handling and analysis.

Our University
Two new metabolomics faculty
 Over $2,000,000 in advanced MS and some
nmr instrumentation
 Staffing with data handling/analysis experts
from Super Computing Center

Presentation

What is metabolomics bringing us?
Sample preparation/isolation for analysis
 Data collection (instrumentation)
 Data handling
 Data analysis


Many similar challenges we face
Preparation/Isolation

Volatiles – not much help.


We recognize the limitations of any
extraction/isolation method
Help with instrumentation for volatiles
Non-volatiles – helping us

Searching for “best” method for us.

Going through a host of methods
evaluating each for sensitivity and breath
Non-volatiles – common approach

Solvent extraction of solid tissues
 Mechanical disruption of the tissue (grinding,
vibration or other methods on a frozen
sample.)
 Solvent selection varies widely with
compounds of interest;
 polar
compounds being best extracted with
isopropanol, ethanol, methanol, acidic methanol,
acetonitrile, water, and methanol:water.
 Non-polar compounds are most often extracted
with chloroform or ethyl acetate

(Dettmer et al., 2007).
Polar substances in potatoes

Polar substances – (potatoes) best
extraction method
methanol and heating (for enzyme
deactivation)
 Methoxylation of sugars and silylation
 GC-MS profiling resulted in 150 polar
compounds, 77 of which were identified


(Roessner et al. 2000)
Secondary plant metabolites

Extraction with acidified aqueous methanol.
(75% methanol, 0.1% formic acid, water from the
sample or added as necessary).

Key factor is simplicity

De Vos et al. (2007)
Analysis

MS –GC/GC, LC (UPLC), direct analysis



Long history of application of basic techniques to
foods
Impressive evolution in methods
nmr


Applied to foods in related work for more than two
decades.
Historically, emphasis was to detect adulteration and
fraud
GCxGC
Better resolution
 Better sensitivity – no back ground
 Better quantitative data


Going from unique use to standard
method
GC-MS

Broad use of TOF instruments







Improved sensitivity (4X vs. quad)
Fast scans – permits peak deconvolution (peaks with 1 sec
difference in peak apex)
Also minor peaks not well separated from major peaks
Accurate mass at fast scan
Leco – GC/MS and GCxGC/MS - low resolution MS but
peak deconvolution due to high sampling rate
Thermo – triple quad also high resolution magnetic
sector
Waters – GCxGC with accurate mass TOF
UPLC or Capillary LC

UPLC – sub -2μm stationary phase and high
linear velocity of the mobile phase

Faster analysis (high throughput)
Better peak resolution
Higher peak capacity
Must be interfaced with fast MS – often TOF

(Plumb et al., 2005).



Ambient ionization – gas analysis

PTR-MS or API-MS

Ideally suited to providing an ion profile of
a gaseous sample

Many current applications in flavor
chemistry
Ambient Ionization (AI) Methods
Ionization Method
Ionizing Agent
Sample State
Desorption ElectroSpray
Ionization (DESI)
Charged droplets/ions
Condensed phase,
gas phase
Desorption Atmospheric
Pressure Chemical Ionization
(DAPCI)
Primary ions via corona
discharge or charged droplets
Condensed phase,
gas phase
Direct Ionization in Real Time
(DART)
Metastable atoms
Condensed phase,
gas phase
Atmospheric Pressure Matrix
Assisted Laser Desortion
Ionization (AP-MALDI)
Laser irradiation/Matrix
Condensed phase
Atmospheric Pressure Solids
Ionization Probe (ASAP)
Primary ions via corona
discharge
Condensed phase
Electrospray Laser Desorption
Ionization (ELDI)
Laser irradiation/Charged
droplets
Condensed phase



Ionization methods allow for analysis of samples under ambient conditions
Many AI methods require no sample preparation
Currently, AI methods are receiving considerable attention
DESI Instrumentation

Implementation of DESI




DESI spray head – generation of charged droplets/primary ions
Atmospheric pressure ionization compatible mass spectrometer – mass analysis
of generated secondary ions
Some ancillary equipment/supplies – either necessary for experiment or
convenience
To date, majority of DESI data has been generated using linear
quadrupole ion traps


DESI has been demonstrated on nearly all mass analyzers
Several different API interfaces
solvent
N2
HV
spray capillary
inlet of mass
spectrometer
gas jet
spray
desorbed
ions
nebulizer capillary
a
dt-s
surface
b
sample
Example - Detection of lysozyme
DESI spectrum of lysozyme present on PTFE surface; average surface concentration 50 ng/cm 2
Microbial contamination on food processing surfaces?
nmr

MS offers sensitivity and capacity to detect compounds in
mixtures but are limited to ionizable species, have difficulties
resolving isomers, and usually require standard compounds
for quantification.

NMR has the capacity to characterize chemical structure
and quantity but is limited to the 20-50 most abundant
compounds in a given sample without isotope labeling.
(several other limitations)




Quantitative and reproducible – statistical analysis
Sensitivity not limited by same factors as MS
High throughput – 500 samples per day
Hegeman et al. Anal. Chem. 2007, 79, 6912-6921, Hegeman for isotope labeling
studies; (Pan and Raftery, 2007; (Lindon et al., 2004
Simple sample preparation






Freeze sample
Freeze dry
Reconstitute in 80:20 D2O:CD3OD containing
0.05% w/v TSP-d4 (sodium salt of
trimethylsilylpropionic acid)
Sample heated (50C – 10 min)
Micro centrifugation
Ward J, Harris C, Lewis J, Beale MH, Phytochemistry 62
(2003) 949–957
Data handling/Analysis

Multivariate statistical projection methods
(partial least squares, principle component
analysis) are commonly used (as starting
point)

Lend themselves well to biological data
because of their ability to correlate multiple
variables in a robust and easily
interpretable fashion

(Jonsson et al., 2005).
Statistical heterospectroscopy
(SHY)

New technique used to correlate nmr data with
UPLC-MS results by cross-assigning the signals.

(Crockford et al., 2006). (Pan and Raftery, 2007).


Technique used to combine nmr with DESI-MS
to correlate known biomarkers with specific
metabolites
(Pan et al., 2007).

http://www.nmr.ch/ CARA
MetAlignTM




Used in numerous publications for data
extraction from GC-MS and LC-MS data sets.
Pulls out all of the masses and sorts which are
the same in all of the data sets and which could
differentiate the data.
Allows the user to look for unique peaks in the
sample set above a chosen noise threshold
(Lomen et al., 2006).
MetAlign

“MetAlign is a software program for full
scan LC-MS and GC-MS comparisons and
was designed and written by Arjen
Lommen of RIKILT-Institute of Food
Safety. It has been extensively tested in
collaboration with Plant Research
International.”

http://www.metalign.nl/UK/
Commercial packages

For example – Marker Lynx (Waters),
Xaminer™ Thermo and ?
Umetrics SIMCA-P

http://www.umetrics.com/default.asp/page
name/software_simcapplus/c/4

Select compounds to focus efforts on – not
try to identify everything
Identifications

Most existing metabolite libraries are either
proprietary, insufficiently comprehensive,
collected under non-standardized conditions or
unsearchable by computers.

Exceptions:
Human Metabolome Database (HMDB;
http://www.hmdb.ca/) (>20,000 metabolites)
and
Madison Metabolomics Consortium
(MMC) Database (MMCD; http://mmcd.
nmrfam.wisc.edu/), a web-based tool that
contains data pertaining to biologically
relevant small molecules from a variety of
species.

Conclusions
Not much help in isolation methods for
volatiles – information on semi or non—
volatiles
 New instrumentation – GC/GC, MS and
sample interfaces, nmr, LC-MS
 New tools for data handling and analysis



Also combining instrument data e.g. nmr w/
MS
Establishing public databases
Greatest contribution?

Availability

Methodologies
Will demand collaboration
Volatiles/Semi-volatiles
•Extraction
•SAFE ?
•SPME
Instrumental
Analysis
•GC-TOF-MS
•Accurate mass
Non-volatiles/Semivolatiles
•Extraction
•MeOH/H20
Instrumental
Analysis
•LC-MS-TOF MS/MS
•NMR ?
Data Analysis
MetAlign
•PLS with DA
•PCA
•SHY
Descriptive Sensory
Analysis
Collect LC
fractions for
descriptive
sensory
analysis
Figure 1. GC-MS-based metabolomics. A, Analytical approach used
B, Conventional approach. C, Alternative, unbiased approach to
GCMS data analysis.
Tikunov Y, Lommen A, Ric de Vos CH, Verhoeven HA, Bino RJ, Hall RD, Bovy AJ Plant
Physiology, November 2005, Vol. 139, pp. 1125–1137
Formatted to export data

For example - to SIMCA-P
Case study
Strawberry metabolites



Fourier Transform Ion Cyclotron Mass
Spectrometry (FTMS)
FTMS - only MS system capable of routinely
achieving ultra high resolution at high acquisition
rate (100–1,000 amu scan/sec) allowing multiple
scans in (1–2 min).
Separation of the metabolites achieved solely by
ultra-high mass resolution, eliminating the need
for time consuming chromatography and
derivatization.
Aarón A, Ric De Vos, Verhoeven HA, Maliepaard CA, Kruppa G, Bino R,
Goodenowe DB. Omics 6(3), 2002 (217-234)
Method

4 stages of ripeness for berries
Extraction –50/50 MeOH/0.1% formic acid
or 100% acetonitrile (AN)
 Direct injection into FTMS
 MS ionization - electrospray (ESI + or -) or
atmospheric pressure chemical ionization
(APCI + or -)

Strawberry metabolite
1ppm mass accuracy
10 ppm mass accuracy
(2 possible only 1 is logical)
Result



total of 5,250 unique 12C masses were obtained
from extracts of the four different developmental
fruit stages, ionized in the four ionization modes.
In the red stage extract, 55% of the masses
were assigned a single empirical formula, 10%
two formulae, and 35% three or more formulae.
Went to data databases for help - 159,000
natural products (Chapman and Hall, Dictionary
of Natural Products)
Next steps

Look for changes or relationships of data
to some attribute via multivariate statistics

Identify the compounds of interest.


HPLC, mass spectrometry (MS/MS) and/or
nuclear magnetic resonance (NMR) must be
employed.
Evaluate result for value