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Targeted and non-targeted detection of food detection of food
contaminants and adulterants
Dr Adrian [email protected]
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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