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Identification of Meat Species in
Processed Foods using
Mass Spectrometry

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Presentation Overview
 Background
– Food labelling regulations
– Water retaining agents in chicken products
– Use of gelatine
 Research Objectives
 Proposed solution
– Sample preparation procedure
– LC-MS approach
– Data interpretation
 Results
– Identification of potential peptide markers
– Quantification of markers
 Future work using Xevo TQ-S
 Conclusions

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Meat speciation – regulatory
requirements
 Authenticity of food and the accuracy of package labeling is important
to both consumers and food producers
 Within the EC food labeling regulations exist
– ~5 M people in UK have preferences concerning consumption of certain
species
 Composition of injection powders?
– Undeclared water-retaining hydrolysed proteins from pork and beef
used in chicken products
– Some chicken products potentially unsuitable for consumers
 Need to verify the species of gelatine used in food

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Injection powders - gelatine
 What is it?
– Bi-product from the meat / fish industry
– partial hydrolysis of collagen extracted from skin,
bones, connective tissue
 Properties of gelatine
– Gelling agent
– Semi-solid colloid gel
– Texture enhancer
 Approx annual production:
– Europe: 117kt (70% used in food)
 What is it used for?
– Food and Beverages
– Drugs and capsules
– Cosmetics
– Photographic film
– Fertilisers…

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Research objectives and aims
 Develop a method to detect species and tissue origin of meat
ingredients present in meat products
1. To identify unequivocal markers that can indicate the presence of
bovine or porcine gelatine
2. To determine whether chicken products have been adulterated
with proteins from other animal species
3. To develop a robust and transferable method
 Current limitations…
– Paper trail is not sufficient
– FSA concluded that PCR / IA based strategies are not reliable
• DNA markers damaged by processing conditions
• False negative rate
 Possible Solutions?
– LC-MS/MS using a proteomic workflow

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Alternative strategy =
proteomic based analytical strategy to
identify peptide markers using HR-MS

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Bottom-up proteomic experiment
1. Enzyme
digestion
2. UPLC
separation
Precursor
ions
MSE product
ions
3. MS
analysis
4. Data
interpretation

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Meat Speciation Workflow
SAMPLE PREPARATION
(1) Tryptic digest (2) ADH addition
DATA ACQUISITION
Acquire data-independent MSE Data
SOFTWARE PROCESSING
IdentityE and gelatine database (BioArch)
ANALYTICAL SYSTEMS
(1) nanoACQUITY UPLC ® (2) XevoTM QTof MS
Nano-scale
separations
•Resolution
•Peak shape
•Number of
components
/ analytical
run
Full scan,
accurate
mass

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Meat Speciation Workflow
Sample Preparation
 Samples
– Protein tryptic digests;
o pork gelatine
o beef gelatine
o pork & beef gelatine mix
 Tryptic digest
– 250 μg of porcine and bovine gelatine were hydrolysed with 5 μg of
sequence grade trypsin for 16hr
 ADH addition
– Quench reaction with formic acid
– 10 fmol of yeast alcohol dehydrogenase (internal standard) added
tryptic digest
SAMPLE PREPARATION
(1) Tryptic digest (2) ADH addition

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UPLC Conditions
 System
– NanoACQUITY® UPLC
 Column:
– 75 µm x 15 cm BEH C18 column
 Gradient:
– 1 to 40% acetonitrile
– 90 min
 Flow rate:
– 300 nL/min
 Triplicate analysis
MS Conditions
 System
– Xevo QTof MS
 Mass range:
– m/z 50-1990
 Data Acquisition:
– MSE
 Collision energy:
– Low energy - 4 eV
– High energy - 12-35 eV
 Acquisition scan time:
– 0.9 s/function
Meat Speciation Workflow
UPLC-MS conditions
OPTIMISED ANALYTICAL PARAMETERS
(1) nanoACQUITY UPLC ® (2) XevoTM QTof MS

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Meat Speciation Workflow
MSE
 UPLC-MSE is a data independent parallel process that occurs in
the collision cell
 The instrument is operated in an alternative scanning mode
providing two MS scan functions for data acquisition in one
analytical run
– Function 1 = low collision energy (precursor ions) Function 2 =
high energy (fragment ions)
DATA ACQUISITION
Acquire data-independent MSE Data

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Meat Speciation Workflow
Software processing
SOFTWARE PROCESSING
PLGS and IdentityE
Positive matches referenced to
database library
Increasing confidence in assignment of peptides

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Results and Discussion

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nanoACQUITY replicate injections (n=3)
Beef gelatine
Repeatable results
Overlay low energy MSE chromatograms

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Meat Speciation Workflow
Software processing
High energy product ion data gives increased confidence in peptide sequence identification
Low energy data can be displayed as a mass
spectrum or a chromatogram
Markers originated from a SINGLE protein
Unique marker peptides sequences listed here

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Discovery & Identification
Total Marker Peptides
Aim to obtain a peptide marker that is unmodified (if possible)
 Over 60 collagen peptides were identified in samples of both bovine and
porcine gelatines
 IdentityE did not identify peptides from any other proteins
– Collagen
– Yeast ADH
 Indicates the samples were 100% gelatine
Multiple
forms of the
peptides
identified
Species peptide Peptide mass Type of peptide modification
Bovine
GYPGNPGPAGAAGAP 1235.58 Non-tryptic cleavage product
GYPGNPGPAGAAGAPGPQGAVGPAGK 2173.08 Unmodified peptide
GYPGNPGPAGAAGAPGPQGAVGPAGK 2189.07 Hydroxyl of single proline
GYPGNPGPAGAAGAPGPQGAVGPAGK 2205.07 Hydroxylation of prolines 3 and 15
GYPGNPGPAGAAGAPGPQGAVGPAGK 2221.06 Three hydroxyprolines
GYPGNPGPAGAAGAPGPQGAVGPAGKHGNR 2653.3 Missed tryptic cleavage hydroxylation of proline
29
GYPGNPGPAGAAGAPGPQGAVGPAGKHGNR 2669.29 Missed cleavage plus two proline hydroxylations
Porcine
IGQPGAVGPAGIR 1192.68 Deamidation + Q3
IGQPGAVGPAGIR 1193.66 Hydroxyl + DKNP 4
IGQPGAVGPAGIR 1208.67
IGQPGAVGPAGIR 1209.66 Deamidation +Q3; Hydroxyl+DKNP9

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Discovery & Identification
Unique Unmodified Marker Peptides
Bovine peptide marker
IGQPGAVAPAGIR
Porcine peptide marker
TGQPGAVAPAGIR
Comparison of high energy MSE fragment ion spectra
Differences
in b and y
ions formed

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Quantification
Porcine and bovine gelatine
 Use of ADH enabled quantification of proteins in sample
Removes need to use labelling systems for peptide and
protein quantification
Test mix was prepared
Addition of 15%(w/w) bovine gelatine in porcine gelatine
Results
Three bovine and porcine peptides were selected - relative
bovine gelatine content of ~ 16.8%

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Conclusions
 Highly processed food proteins, such as gelatine, that
are devoid of DNA signature can be speciated using
LC-MS
 Protein sequence database analysis identified peptide
sequences within the protein that are species specific
 Waters Xevo Qtof MS was able to identify these
sequences, even after significant modification of the
amino acids
 The interrogation of the total protein complement of
the sample also provided potential information on
non-gelatine proteins in the samples

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Further Work
Preliminary data suggests the method can
be used to quantify the species in mixtures
of gelatines
 Investigate the contribution that type III
collagen (from skin and connective tissue)
might make to the quantitative analysis
of gelatines

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Future Work
Method transfer to routine analysis using tandem quad
MS/MS

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Acknowledgments
Helen Grundy - Food and Environment
Research Agency, York, UK
BioArCh, University of York, UK
Thank you for your attention
Any Questions?