Targeted and non-targeted detection of food detection of food

contaminants and adulterants

Dr Adrian Charltonadrian.charlton@fera.gsi.gov.uk

Thermo Summer Symposium, June 7th 2011, QE2 Centre, London.

Who we are

• Fera is an executive agency of the UK Department for Environment, Food and Rural Affairs.

• Approximately 1000 staff housed in purpose built facilities on the outskirts ofYork

• Our over arching purpose is to support and develop a sustainable food chain,a healthy natural environment, and to protect the global community frombiological and chemical risks.

Website: http://www.defra.gov.uk/fera

Targeted/Non-targeted analysis

• Food safety is largely monitored assuming that we know what to look for and need to measure it. The target list approach .

• This is ideal for routine monitoring purposes.• Inadequate for identifying emerging issues and this • Inadequate for identifying emerging issues and this

was particularly highlighted by recent melamine poisonings.

• Non-targeted profiling approaches are required to capture the most pertinent information in relation to chemical risks in foods.

• Understanding normal profiles is the key to success.

Targeted analysis of food

• Residues

• Environmental contaminants

• Natural toxicants

• Processing contaminants

• Additives

• Packaging chemicals

• Adulterants• Adulterants

• Melamine adulteration highlighted the need for holistic screening methodology.

• Rapid, generic NMR and HR-LC-MS workflows simultaneously detect low levels of food and beverage adulterants.

Non-targeted detection of adulterants

• A range of matrices including milk powder, chocolate and cocoa butter are being investigated.

Melamine

The ideal instrument?

Unprocessed sample with unknown contaminant Measure

< 60 mins< 60 mins

The answer! Only on CSI

Detection systems

Increasing Increasing

Non-targeted

FT-IR spectroscopy

Increasing specificity/sensitivity

Increasing coverage

Targeted

NMR spectroscopy

Mass spectrometry

HR-LC-MS

• High mass accuracy• High mass resolution• High-throughput screening• Compound ID confirmation• Compound ID confirmation• Unknown identification

• Accurate mass• Retention time• Fragmentation pattern• Isotopic fingerprint

Why is high resolution important?

“The minimum separation between two neighbouring masses to distinguish between ions of different m/z.”between ions of different m/z.”

E.g. Flumequine and Oxolinic acid

Low res MS: m/z [M+H] – 262High res MS: m/z [M+H] – Flumequine 262.0874

m/z [M+H] – Oxolinic acid 262.0710

Cryoplatform

Chiller

Autosampler Magnet

NMR spectroscopy

• High throughput• Unbiased• Unique “virtual” separation• Identification of unknowns

– Multinuclear chemical shifts Chiller

Cryoprobe

– Multinuclear chemical shifts – J-couplings– Peak intensities– NOE– Diffusion rate

Data mining

PC10.00

.01

.02

Computationally intensiveData handling andbioinformatics tools required

Multivariate Statistics

Observation

GM

Control

.004.02

-.01

.002.01

PC6PC2

0.0000.00-.002-.01

Artificial intelligence

Univariate Statistics

Spectral fingerprint

Compound Identification

Database Searching• Match criteria e.g.

• chemical shifts• accurate mass• accurate mass

Structure Elucidation• Heteronuclear NMR• LC-MS/MS• Other spectroscopies• Separation techniques

HR-LC-MS and NMR for the detection of adulterants in milk powder

Contaminants

Spiked into milk powder at 1, 10 and 100 ppm

LC-MS procedure

Extraction 20 min

Add 20 ml Acetonitrile/Water 70:30 v/v

Milk sample 2g

Data processing

UHPLC/Exactive MS analysis

Filtration 0.2 µm PTFE into LC vial

Centrifugation 14000 rpm 10 minutes

Extraction 20 min

NMR procedure

Solvent removal (N 2 stream)

Extract as for LC-MS

Data processing

NMR analysis

Dissolve (D 2O, CDCl3 or DMSO-d6)

Data Acquired

• LC-MS: All combinations using: • Columns: HILIC, RP C18• Ionisation: Positive,Negative• Ionisation: Positive,Negative• Source: ESI, APCI

• NMR• 1D 1H-NMR, HSQC, TOCSY

Chromaotgram of milk extract –Where is the information?RT: 0.39 - 60.03 SM: 7B

5

10

15

20

25

30

Rel

ativ

e A

bund

ance

44.4638.18 43.82

46.56

14.81 41.80 48.62

10.2641.27 48.98

13.829.82 33.20 49.7219.818.5637.765.47 16.73

50.4021.12 22.71 27.95 34.9031.41 51.0727.60

51.69 53.5755.96

NL: 1.58E8TIC F: FTMS {1,1} + p ESI Full ms [50.00-1000.00] MS Non_Target_Milk_PosC18ESI_250809Sample

TIC

5 10 15 20 25 30 35 40 45 50 55 60Time (min)

0

5

10

15

20

25

30

0

44.46

38.20

10.25 46.56

14.81 41.80

33.1446.81

48.8541.462.0350.18

9.30 37.76 50.8319.848.322.59 12.78 37.52 51.22

15.18 21.10 52.3622.62 30.2154.95

NL: 3.70E7Base Peak F: FTMS {1,1} + p ESI Full ms [50.00-1000.00] MS Non_Target_Milk_PosC18ESI_250809SampleBPC

RT: 8.71 - 60.03 SM: 7B

60

65

70

75

80

85

90

95

100

105

Rel

ative

Abu

ndan

ce

44.46

38.1843.82

46.56

14.8141.80 48.62

10.26

46.5814.81

38.23

41.80

Blank milk vs. spiked milk

10 15 20 25 30 35 40 45 50 55 60Time (min)

10

15

20

25

30

35

40

45

50

55

60

Rel

ative

Abu

ndan

ce

41.27 48.9840.53 49.2813.82

33.20 49.7219.81

50.1837.7616.73

50.4021.12

22.71 50.7927.9534.9022.95 31.41

51.07

51.2227.6026.12

51.6953.57

54.9555.96 58.20

10.29

37.5333.16

12.79

49.48

35.9625.0119.87

52.9826.32 27.8916.7122.58 28.1417.75

54.09

SieveTM – frame generation/data comparison

Blank sample

Alignment Framing ChemSpider Search

Real sample

Alignment Framing ChemSpider Search

Compensation of RT changes

Table of identified frames

Table of positive hits

Using Sieve to screen spiked milk samples – 10 mg/kg

Frame generation and data comparison

Difference in data found

ChemSpider searchChemSpider search

Hits reported

Confirmation using isotopic pattern

LC-MS summary

Compound Polarity Recognised by Sieve

1ppm 10ppm 100ppm

Fenthion Pos + + +

Sudan I Pos + + +

Sudan IV Pos + + +

Compound Polarity Recognised by Sieve

1ppm 10ppm 100ppm

Fenthion Any - - -

Sudan I Pos + + +

Sudan IV Pos + + +Sudan IV Pos + + +

Melamine Pos - - +

Urea Any - - -

Cyanuric acid Neg - + +

BPA Any - - -

Aldrin Any - - -

Sudan IV Pos + + +

Melamine Pos + + +

Urea Any - - -

Cyanuric Acid Neg + + +

BPA Any - - -

Aldrin Any - - -

SunFireTM C18 3.5 mm 2.1 X 150 mm ZIC® -HILIC 3.5 mm 2.1 X 100 mm

Data collated from APCI and ESI experiments

NMR SummaryCompound DMSO-d6 CDCl3 D2O

Fenthion ND 10 ppm 100 ppm

Sudan I 10 ppm 10 ppm ND

Sudan IV 10 ppm 100 ppm ND

Melamine 10 ppm ND ND

ND - Not detected

Urea 10 ppm ND ND

Cyanuric Acid* ND ND ND

BPA 10 ppm 10 ppm 10 ppm

Aldrin 10 ppm 10 ppm ND

*Parameters used suppressed CA signal in DMSO extra ct• In extract LOD estimated to be approximately 2 ppm w ith

2hrs data acquisition.

Conclusions• HR-LC-MS and NMR spectroscopy provide both

coverage and sensitivity.• Software tools successfully identify main sources

of variance.• Data fusion technologies and databases require

further development. • Limiting factor for MS is ionisation• Limiting factor for NMR is sensitivity• Generic extraction protocols need further work.

The Future – Data Fusion

Sudan IMcKenzie et al. (2010) Metabolomics. 6(4), 574-582

Summary

• Non-targeted profiling is becoming more feasible as instrument technologies advance.

• HR-LC-MS and NMR are the key tools.• Currently focused on emergency handling • Currently focused on emergency handling

and the rapid identification of unknowns.• Potential to hugely reduce the number of

routine analyses for food safety monitoring.• Also potential for on or at line monitoring.

– James Donarski (NMR)– Dom Roberts (LC-MS)– Robert Foster (LC-MS)– Michael Dickinson (LC-MS)– Michal Godula (Data mining and LC-MS)– John Godward (Software Development)– Mark Harrison (Analytical Chemistry)– Robert Stones (Bioinformatics)

Acknowledgements

– Robert Stones (Bioinformatics)– Julie Wilson (Maths and Statistics)– James McKenzie (Data Fusion)

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