Food allergens are an issue of major concern as accidental ingestion of the culprit food can lead to severe clinical symptoms.

1. ©2015 Waters Corporation 1
The Advantages of LC-MS/MS analysis
for Food Allergen Detection

2. ©2015 Waters Corporation 2
Overview
 Food allergens – background & regulatory status
– Major classifications (within EU) & threshold dose establishment
 Current detection strategies?
 What information can MS provide?
 LC-MS/MS strategy
– Discovery phase (proteomic based)
– Translation to routine quantitation (tandem quadrupole)
– Recommendations for MS based allergen analysis
 Summary and future prospects?

3. ©2015 Waters Corporation 3
Immunological Aspects of Food Allergy
 Food allergic reaction is an IgE mediated reaction to specific
food proteins
– Prevalent in c. 2% of the adult and 8% of child population
– Symptoms can range from mild to severe (life-threatening)

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Food Allergy – avoidance &
preventative actions?
 No curative treatment is available for food allergy
 Accidental ingestion of the culprit food can lead to severe clinical
symptoms
– Elimination diet
o Reduce the risk of allergic reactions
o Disadvantages: deficiencies, eating disorders, growth retardation
– Emergency medication
o Antihistamines (H1 blockers)
o EpiPen (adrenaline-autoinjector)
o Corticosteroids
Preventative actions?
Effective tools for detection & quantitation are
needed for effective labelling

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EU perspective – Statutory Food
Labelling Laws
 The rules for pre-packed foods establish a list of 14 food
allergens, which must be indicated by reference to the source
allergen whenever they, or ingredients made from them, are used at
any level in pre-packed foods, including alcoholic drinks
 Labelling rules in European Directives 2003/89/EC & 2006/142/EC
ensure that all consumers are given comprehensive ingredient
listing information and make it easier for people with food
allergies to identify ingredients they need to avoid
 Food Information for Consumers Regulation (EU) No. 1169/2011 builds
on current allergen labelling provisions for pre-packed foods &
introduces a new requirement for allergen information to be provided
for foods sold non-packed or pre-packed for direct sale
– Allergen labelling rules will be changing in December 2014

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Allergen Classification
EU 14 major priorities
Cereals containing gluten, crustaceans, molluscs, eggs,
fish, peanuts, nuts, soybeans, milk, celery, mustard,
sesame, lupin and sulfur dioxide (at levels >10mg/kg or 10
mg/litre, expressed as SO2 )

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Establishment of Threshold Doses
 Threshold dose establishment – ongoing research activity
– Safety assessment LOAEL or NOAEL
 Commission Regulation (EC) No. 41/2009 established levels of
gluten for foods claiming to be either 'gluten-free' or 'very low
gluten‘ (January 2012)
– 'gluten-free': at 20 parts per million of gluten or less
– 'very low gluten': at 100 parts per million of gluten or less -
however, only foods with cereal ingredients that have been specially
processed to remove the gluten may make a 'very low gluten' claim
 These regulations apply to all foods, pre-packed or sold loose,
such as in health food stores or in catering establishments

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Analysis of Allergens
Current detection strategies
IncreasingCurrentUsage
IncreasingInstrument
Complexity&Price
ELISA
IgG antibody based recognition of
whole protein or peptides
PCR
Determination of
allergic protein DNA
MS

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Effect of food processing?
 The majority of allergen testing is performed on processed
foods
 The effect of cooking procedures on the target protein analytes
need to be considered along with the effects of the matrix
components (other proteins; carbohydrates; oils etc.)
 Processing-induced modifications also affect analyte extraction
– May compromise immuno-based methods as processing induced
changes may affect antibody reactivity & specificity
– For PCR based methods it is often the case that no DNA can be
extracted from highly processed products or protein concentrates

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LC-MS/MS Advantage?
Specificity
• MRM acquisition mode
• Multiple transitions & ion ratios (selectivity)
Robustness
• Matrix effect reduction
• Good S:N in complex matrices
• Repeatability (r) & reproducibility (R)
Sensitivity
• Trace level detection (sub ppb LoDs)
• Use of labeled internal standards
• Simultaneous analysis of multiple allergens in
processed foodstuffs
Ideal for routine quantitative method

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What information can MS detection
provide?
 MS technologies allow a broad range of different types of
protein analysis to be conducted e.g. protein identification;
characterisation & quantitation
 Analyse peptide markers of the protein causing the allergic
reaction
 Targeted and specific (m/z) analysis
 Quantifiable technique
 Potential to use a multi-allergen approach
 Capability to modify / optimise routine methods for challenging
matrices (without additional cost)

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Discovery of peptide markers of allergenic
proteins
QTof & ion mobility enabled QTof

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Which type of MS technology is
applicable for discovery?
 Time-of-flight MS analysers (Tof; QTof; ion mobility enabled
QTof)
 The inherent characteristics of Tof MS are extreme sensitivity (all
ions are detected), almost unlimited mass range, speed of analysis
(>10 full spectra / s) and >>5ppm mass accuracy
 Confirmation of elemental composition
– Identification of unknown compounds
 Additional dimension of specificity
– Quantitation in accurate mass MS mode (rather than MS/MS) mode
to reduce chemical interferences
– Differentiation of nominal isobars in combinatorial libraries
– Improved protein database search results
– Improved de novo protein sequencing results

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Waters Accurate Mass Instrument
Portfolio
Xevo G2-XS Q-ToF
Xevo G2-XS MS
SYNAPT G2-Si HDMS
SYNAPT G2-Si MS
Ion mobility
enabled QTof

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“Discovery phase”
Bottom-up proteomic based approach
1. Enzyme
digestion
2. UPLC
separation
Precursor
ions
MSE product
ions
3. MS
analysis
4. Data
interpretation

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“Discovery phase”
HRMS workflow
SAMPLE PREPARATION
(1) Tryptic digest (2) ADH addition
DATA ACQUISITION
Acquire data-independent MSE or HDMSE Data
SOFTWARE PROCESSING
PLGS & IdentityE and Proteomic database (e.g. UniProt)
ANALYTICAL SYSTEMS
(1) ACQUITY M-Class UPLC ® (2) XevoTM G2-XS QTof or Synapt G2-Si

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Food Proteomic Workflow
Software processing
SOFTWARE PROCESSING
PLGS and IdentityE
Positive matches referenced to
database library
Increasing confidence in peptide assignment - - >

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Progenesis™ software

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Progenesis™ Workflow to Aid the
Discovery Process
alignment
peak detection
identification
protein
quantitation
statistics
metabolomics/lipidomics
proteomics
peptide
quantitation
alignment
peak detection
compound
quantitation
identification
statistics
deconvolution

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Ion mobility enables QTof
HDMSE acquisition mode

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Ion mobility enabled QTof
additional peak capacity
3 Dimensions
of resolution
X = m/z
Y = Intensity
Z = Drift time
OVT peptide m/z 878.7726 AIANNEADAISLDGG

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Ion mobility enabled QTof
discover more peptides…

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“In Silico” Approach
Software-based strategy to identify peptide
markers

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Scientific publications
Major allergens from
milk, soy and egg
“In silico” digestion
Theoretical peptides
Specific peptides
STEP1
Extraction and purification
from a reference material
Suitable sample
preparation method
Xevo TQ-S Analytical method for
peptides from STEP 1
STEP2
“In silico” Workflow Strategy
Combination of observed & theoretical peptides

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Sample Preparation Strategy
 Solubilise and extract protein using aq buffer (PBS) from complex
matrix
 Protein denaturation using detergents (RapiGest™ or SDS) to
linearise the 3D structure
 Proteolytic digestion using trypsin to cleave the protein into
reproducible and peptide sequences (6 – 12 amino acids)
 Additional sample clean-up & enrichment
– Immuno-affinity column using specific anti-peptide IgG
– Ultra-filtration or SPE?
 Filtration & dilution in mobile phase A prior to LC-MS analysis

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Allergic
food
Number of
allergens
Reference protein for
method development
Number of specific petpides
(Skyline© - literature)
Egg white 3 Ovalbumin 4 - 14
Egg yolk 3 Phosvitin 30 - 3
Milk 6 Casein αS1 3 - 3
Soy 11 Kunitz Tripsin Inhibitor 4 - 2
Selection of the optimum protein &
peptide sequences
Ovalbumin
Peptide from literature Source Comment Skyline matching
AFKDEDTQAMPFR Faeste et al., 2011 Review OK
ISQAVHAAHAEINEAGR
Faeste et al., 2011 et
Lee et al., 2010
OK
HIATNAVLFFGR
Heick et al., 2011
Multi-allergen
method
OK
YPILPEYLQCVK OK
DILNQITKPNDVYSFSLASR OK
ELINSWVESQTNGIIR OK
DVYSFSLA
Azarnia et al., 2013 Heat proof
OK
ISQAVHAAHAEINEAGR OK
HIATNAVLFFGR OK
GGLEPINFQTAADQAR OK
LTEWTSSNVMEER OK
VTEQESKPVQMMYQIGLFR OK
EVVGSAEAGVDAASVSEEFRA OK
Determine robustness of the peptides to thermal processing (commercial baking
conditions 200o C for 20 mins)

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Skyline Experimental Design
Peptide
settings
Transition
settings
MRM
generation

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Translation to routine quantitative analysis
using tandem quadrupole
TQ-S

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Acquisition Mode
Multiple Reaction Monitoring (MRM)
Quadrupole 1
Collision
Cell
Static (m/z 821.5) Static (m/z 768.5)
Ar (2.5 – 3.0e-3mBar)
Precursor(s)
Product(s)
Quadrupole 1

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MRM transition development
process
 Reference samples may be used to determine & optimise the
MRM’s for each of the peptide sequences
 Information from Skyline is used to predict the parents &
fragments for each peptide sequence
Step 1
• Full Scan MS
• Determine precursor m/z & charge state
• N.B. 2+& 3+ ions give better fragmentation
Step 2
• Product ion scan
• Determine selective products of precursor (minimum
number of 3 per peptide)
Step 3
• In the presence of matrix
• Optimise cone voltage(s)
• Optimise collision energies

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Method Parameters
ACQUITY UPLC conditions
LC system ACQUITY UPLC I-Class
Column ACQUITY BEH300 C18, 2.1 x 150
mm, 1.7 µm
Column temp 40°C
Solvent A Water + 0.1% formic acid
Solvent B ACN 0.1% formic acid
Injection volume 2 μl
Time
(min)
Flow rate
(mL/min)
% A % B Curve
0.0 0.5 98 2 -
30 0.5 60 40 6
30.1 0.5 10 90 6
32.1 0.5 10 90 6
32.2 0.5 98 2 6
35 0.5 98 2 6

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Peanut allergens
Targeted peptides & MRMs
Peptide Protein Precursor Product Cone Voltage (V) Collision Energy (eV)
930.45 25 24
1077.52 25 24
1148.55 25 24
804.35 25 28
1247.51 25 28
1360.6 25 28
587.3 30 30
1219.53 30 30
1347.58 30 30
175.12 30 19
505.26 30 19
761.37 30 19
660.31 25 31
807.37 25 31
1050.46 25 31
147.11 25 18
656.35 25 18
275.17 25 18
672.35 25 18
1186.53 25 18
147.11 30 27
299.8 30 27
404.25 30 27
809.37 30 27
923.42 30 27
1181.5 30 27
473.58 25 15
695.16 25 15
750.91 25 15
376.19 30 17
475.26 30 17
590.29 30 17
653.28 25 19
781.34 25 19
878.39 25 19
Arah 2 N Term
Arah2 C Term1
Arah2 C Term2
Arah1 N Term Prepro
Arah1 N Term
Arah1 C Term
553.26Arah7.1
Arah6 Uni
Arah6 1
Arah3 4 Acidic
Arah3 4 Basic
543.02
547.00
695.17
767.84
582.92
489.53
SPDIYNPQAGSLK
QQPEENACQFQR
IMGEQEQYDSYDIR
CDLDVSGGR
NLPQNCGFR
688.83
786.87
809.95
543.02
863.57
DLAFPGSGEQVEK
VLLEENAGGEQEER
CLQSCQQEPDDLK
NLPQQCGLR
CCNELNEFENNQR
ANLRPCEQHLMQK

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Milk allergens
Targeted peptides & MRMs
Peptide
Precursor
(m/z)
Product
(m/z)
YLGYLEQLLR (casein S1) 423.2 529.3
634.4 658.4
634.4 771.5
634.4 934.5
VPQLEIVPNSAEER (casein S1) 527.6 802.4
790.9 779.5
790.9 802.4
790.9 1014.5
FFVAPFPEVFGK (casein S1) 692.9 465.2
692.9 676.4
692.9 920.5
692.9 991.5
ALNEINQFYQK (casein S2) 456.6 827.4
684.3 713.4
684.3 827.4
684.3 940.5
FALPQYLK (casein S2) 490.2 332.2
490.2 551.3
490.2 648.4
490.2 761.5

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Egg white allergen
Targeted peptides & MRMs
Peptide Precursor
(m/z)
Product
(m/z)
DILNQITKPNDVYSFSLASR (ovalbumin) 761.0 767.4
761.0 930.5
761.0 1355.7
1141.1 1355.7
GGLEPINFQTAADQAR (ovalbumin) 563.3 732.4
844.4 860.4
844.4 1007.5
844.4 1121.5
844.4 1331.7
ELINSWVESQTNGIIR (ovalbumin) 620.3 673.4
620.3 888.5
930.0 1017.5
930.0 1116.6
EVVGSAEAGVDAASVSEEFR (ovalbumin) 670.3 853.4
670.3 924.4
1005.0 1110.5
1005.0 1266.6

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Compound name: YLGYLEQLLR (casein S1)
Correlation coefficient: r = 0.995512, r^2 = 0.991045
Calibration curve: 59440.7 * x + -3078.23
Response type: External Std, Area
Curve type: Linear, Origin: Exclude, Weighting: 1/x, Axis trans: None
Conc
0 10 20 30 40 50 60 70 80 90 100
Response
0
1000000
2000000
3000000
4000000
5000001
Compound name: ALNEINQFYQK (casein S2)
Correlation coefficient: r = 0.995324, r^2 = 0.990670
Calibration curve: 4296.6 * x+ -289.732
Response type: External Std, Area
Curve type: Linear, Origin: Exclude, Weighting: 1/x, Axis trans: None
Conc
0 10 20 30 40 50 60 70 80 90 100
Response
0
100000
200000
300000
Compound name: FALPQYLK (casein S2)
Correlation coefficient: r = 0.996969, r^2 = 0.993948
Calibration curve: 62929.5 * x + 567.125
Response type: External Std, Area
Curve type: Linear, Origin: Exclude, Weighting: 1/x, Axis trans: None
Conc
0 10 20 30 40 50 60 70 80 90 100
Response
0
1000000
2000000
3000000
4000000
5000001
Compound name: GGLEPINFQTAADQAR (ovalbumin)
Correlation coefficient: r = 0.992720, r^2 = 0.985493
Calibration curve: 26267.2 * x + -5862.54
Response type: External Std, Area
Curve type: Linear, Origin: Exclude, Weighting: 1/x, Axis trans: None
Conc
0 10 20 30 40 50 60 70 80 90 100
Response
0
500000
1000000
1500000
2000000
2500000
Quantitative performance - linearity
Matrix matched calibration standards

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Peptide Identification &
Confirmation
Using ACQUITY UPLC & Xevo TQ-S
1. Retention time
2. Standard MRM transitions

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Peptide Identification &
Confirmation
Using ACQUITY UPLC & Xevo TQ-S
1. Retention time
2. Standard MRM transitions
3. Standard MRM transitions & full scan data
4. Product ion scanning confirmation
(PICs)

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Method Development with Skyline
Use of RADAR for Food Matrices
- Parallel MRM with full scan data acquisition
}

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Method Development with Skyline
Use of RADAR for Food Matrices
 Once the most selective, and then the most sensitive MRMs have been selected for a subset of
food matrices, RADAR can be used to support routine analysis
CaseinS1-
YLG
CaseinS1-
FFV
Ovalbumin-
EVV
Ovalbumin-
GGL
Ovalbumin-
DILOvalbumin-
ELI
CaseinS2-
FAL
CaseinS1-
VPQ
CaseinS2-
ALN
4
3
2
1
6
5
Full scan
MRMs

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Summary
Practical Considerations?
 Choice of analyte?
– MS analysis presents the opportunity to directly analyze for the
presence of the molecules that cause allergic reactions
 Choice of MS Marker Peptides?
– not all proteins & peptides are stable after common processing steps
in the food industry like heating, baking, roasting, or pressure
treatment
 Choice of Standard?
– Isotopically labelled (proteins) or peptides
 Optimization of Protease Digestion
– Quant protein MS requires reproducible & effective protease
digestion, optimised for each matrix / target combination
 Harmonization of Methods & Results
– Development of naturally incurred reference materials & validation
protocols & inter-lab trials

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AOAC recommendations for Allergen
Analysis by LC-MS
Johnson et al.: Journal of AOAC
International vol. 94, no. 4, 2011

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Acknowledgements
 University of Manchester, UK
 Prof Claire Mills
 Phil Johnson
 CER, Marloie, Belgium
 Nathalie Gillard
 Samuel Nemes
Technology Strategy Board funding for
“Allergen analysis developing integrated approaches” (13045-83259)