Identification of Meat Species in Processed Foods using Mass Spectrometry - Waters Corporation Food Research

©2015 Waters Corporation 1

Identification of Meat Species in

Processed Foods using

Mass Spectrometry


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


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


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…


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


Alternative strategy =

proteomic based analytical strategy to

identify peptide markers using HR-MS


Bottom-up proteomic experiment

1. Enzyme digestion

2. UPLC separation

Precursor ions

MSE product ions

3. MS analysis

4. Data interpretation


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


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


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


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


Meat Speciation Workflow Software processing

SOFTWARE PROCESSING PLGS and IdentityE

Positive matches referenced to database library

Increasing confidence in assignment of peptides


Results and Discussion


nanoACQUITY replicate injections (n=3) Beef gelatine

Repeatable results

Overlay low energy MSE chromatograms


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


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


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


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%


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


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


Future Work

Method transfer to routine analysis using tandem quad MS/MS


Acknowledgments

Helen Grundy - Food and Environment Research Agency, York, UK

BioArCh, University of York, UK

Thank you for your attention

Any Questions?

Identification of Meat Species in Processed Foods using Mass Spectrometry - Waters Corporation Food Research

Science

waters corporation

species of gelatine

bovine gelatine

porcine gelatine

meat products

gelatine research objectives

injection powders gelatine

identification of meat