implementing multi-residue methodologies for monitoring ...simplified sample extraction and...
TRANSCRIPT
©2015 Waters Corporation 1
Implementing multi-residue methodologies
for monitoring residues in foods using LC-MS
Dr Simon Hird Principal Scientist
Food & Environmental Business
©2015 Waters Corporation 2
Overview
Introduction
Targeted analysis
– Detection, quantification and identification
o Pesticides
– Screening (and confirmation)
o Vet drugs
Non-targeted analysis
– Screening, identification and quantification
o Pesticides
Conclusions
©2015 Waters Corporation 3
Current trends in residue testing
Multi-residue analyses determine as many residues as possible in the smallest number of analyses
– Reduced number of SOPs and associated costs
– Increased flexibility and optimal batch sizes
– Options to increase scope of current methodology
Combination of generic extraction with the use of high speed liquid chromatography coupled with tandem mass spectrometry
– Increase in the use of high resolution mass spectrometry (HRMS) as an alternative to MS/MS with some advantages
Achieves required LODs/LOQs even in complex matrices with simplified sample extraction and no/limited clean-up
Offers scope for rapid multi-component screening, quantification and identification
Targeted and “Non-targeted” analytical approaches
©2015 Waters Corporation 4
Targeted analysis
Based on establishing a method to determine a list of known
analytes using reference standards and typically methods are
validated prior to analysis of real samples
A list of selected compounds is prepared based on existing
knowledge/intelligence
The scope of a targeted approach, although extensive, will
always be limited to the chosen list
This list of compounds is continually changing and it is difficult
to ensure targeted methods cover all possible compounds of
interest
– Different regions and various laboratories have differing lists
Traditionally TQ using MRM (SRM) is the gold standard
– Often involves quantification
©2015 Waters Corporation 5
Results from official control of pesticide residues in the UK in 2014
In 2014, UK tested 3,615 samples for many targeted pesticides
Residues are detected
– Residues were detected in 44% of samples tested although only 2%
of samples exceeded MRLs
– Required to report MRL violations
– Residues < MRL reported for risk assessment purposes
Driver for analysis that meets performance criteria for
detection, quantification and identification in a single
analysis
No residues detected
Residue found at or below MRL
Residue found above MRL
©2015 Waters Corporation 6
Monitoring of pesticide residues
Increased frequency border controls – 669/2009 controls
– Specifies products/countries and frequency of testing
o Now specifies analysis of residues of at least those pesticides listed
in the EU coordinated control programme that can be analysed
with multi-residue methods based on GC-MS and LC-MS (pesticides
to be monitored in/on products of plant origin only)
o Additional pesticides specified as and when required
• e.g. Ethephon in table grapes from Peru
©2015 Waters Corporation 7
Multi-residue approach to pesticide residue analysis; novel in 2000
22 carbamates by extraction
with ethyl acetate, solvent
exchange and concentration,
LC-MS (SIM)
©2015 Waters Corporation 8
Multi-residue analysis; 400-600 compounds routine in 2015
UPLC-MS/MS APGC-MS/MS
©2015 Waters Corporation 9
Spike 10 mL of fruit juice with 80 pesticides @ at
0.01 mg/kg
Dilute x100 with water
Filter and inject; I-Class and Xevo
TQ-S
Multi-residue analysis without extraction: dilute and shoot of juice
©2015 Waters Corporation 10
Multi-residue analysis with extraction
Acetonitrile – QuEChERS (includes salting out)
Ethyl acetate – SweEt
Acetone – Mini-Luke or NL (extracted with
acetone followed of partition with dichlorometane / petroleum ether (1:1)
AOAC Official Method 2007.01 and CEN Method EN 15662
Shake
Shake and centrifuge
©2015 Waters Corporation 11
Multi residue analysis via QuEChERS
©2015 Waters Corporation 12
Selectivity of dSPE cleanup: different sorbents
©2015 Waters Corporation 13
Selectivity of dSPE cleanup: different sorbents
Freeze-out:
• Lipids and Waxes
• Sugars
Amino-Sorbents, Alumina:
• Acids (including fatty acids)
• Sugars
• Pigments (Anthocyanes, some Chlorophyll)
Carbon-based Sorbents:
• Carotinoids, Chlorophyll, Sterols
Reversed-Phase Sorbents:
• Lipids and Waxes
Risk: Losses of
acidic pesticides
Risk: Losses of
planar pesticides
Risk: Losses of
non-polar pesticides
Risk: Losses of
lipophilic pesticides
©2015 Waters Corporation 14
Overview of cleanup options (Michelangelo Anastassiades: EURL)
Weigh 10 g of Frozen Sample
Shake
Shake / Centrifuge
Add 10 mL Acetonitrile and ISTD-Solution
Add 4 g MgSO4, 1 g NaCl, Citrate Buffer Salts
Optional:
Add “Analyte Protectants”
Multiresidue Analysis
by GC-MS(/MS), LC-MS(/MS)…
Shake / Centrifuge
Acidify extract to pH ~5 to protect base-sensitive pesticides
D-SPE with MgSO4/PSA/ODS
Optional Analysis
of acidic pesticides (LC-MS/MS)
Optional Analysis :
of acid-sensitive pesticides
High content of lipids, waxes?
Freeze-out
D-SPE with MgSO4/PSA
D-SPE with MgSO4/PSA/GCB
High content ofplanar pigments?
Shake / Centrifuge
Shake / Centrifuge
Shake / Centrifuge
Decant or filter or centrifuge
Weigh 10 g of Frozen Sample
Shake
Shake / Centrifuge
Add 10 mL Acetonitrile and ISTD-Solution
Add 4 g MgSO4, 1 g NaCl, Citrate Buffer Salts
Optional:
Add “Analyte Protectants”
Multiresidue Analysis
by GC-MS(/MS), LC-MS(/MS)…
Shake / Centrifuge
Acidify extract to pH ~5 to protect base-sensitive pesticides
D-SPE with MgSO4/PSA/ODS
Optional Analysis
of acidic pesticides (LC-MS/MS)
Optional Analysis :
of acid-sensitive pesticides
High content of lipids, waxes?
Freeze-out
D-SPE with MgSO4/PSA
D-SPE with MgSO4/PSA/GCB
High content ofplanar pigments?
Shake / Centrifuge
Shake / Centrifuge
Shake / Centrifuge
Decant or filter or centrifuge
Choice also
dependant on the
selectivity and
sensitivity of the
LC-MS
©2015 Waters Corporation 15
Matrix effects
Matrix effects are known to occur frequently using electrospray
– Co-elution of matrix co-extractives with analytes
– Differences between commodities and even sample to sample
Matrix-matched calibration is commonly used to compensate for
matrix effects but also:
– Standard addition
– Internal standards (including isotopically labelled ones)
– Dilution of extracts
©2015 Waters Corporation 16
Pesticides in okra: UPLC-MS/MS Acquity H-Class and Xevo TQD
Okra: QuEChERS (AOAC), dSPE (C18,
PSA and GCB), evaporated to dryness
and reconstituted in mobile phase
Retention-time window based MRM
acquisition with autodwell
Compound name: Acetamiprid
Correlation coefficient: r = 0.998828, r̂ 2 = 0.997657
Calibration curve: 7.5159 * x + 1.16915
Response type: Internal Std ( Ref 233 ), Area * ( IS Conc. / IS Area )
Curve type: Linear, Origin: Exclude, Weighting: 1/x, Axis trans: None
ng/mL-0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0
Res
pons
e
-0
50
100
150
Quantification MRM
Qualification MRM
Calibrant @
0.001 mg/kg
RT 5.19
201 pesticides in okra @
0.01 mg/kg
Matrix-
matched
calibration
Acetamiprid
©2015 Waters Corporation 17
Pesticides in chilli: UPLC-MS/MS Acquity H-Class and Xevo TQ-S micro
Chilli: QuEChERS (CEN), no dSPE, direct
injection of MeCN extracts
Due to increased sensitivity
Wide retention-time window based SRM
acquisition (1 minute) for 424 pesticides
with autodwell
Due to increased scan speed
Matrix-matched calibration
Calibrant @ 0.001 mg/kg
RT 5.38
Quantification MRM
Qualification MRM
Compound name: MandipropamidCorrelation coefficient: r = 0.997983, r 2̂ = 0.995970Calibration curve: 1359.24 * x + 108.142Response type: External Std, AreaCurve type: Linear, Origin: Exclude, Weighting: 1/x, Axis trans: None
Conc-0 200 400 600 800 1000
Re
sp
on
se
-0
1000000
Conc
Re
sid
ua
l
-20.0
0.0
20.0
Selected pesticides
in chilli @ 0.01
mg/kg
Acetamiprid
©2015 Waters Corporation 18
Monitoring of vet drug residues
Unlike with pesticides, there are very few non-compliant samples reported in national surveillance across Europe
– In 2012, only 0.25 % of the 425,000 samples tested in Europe were non-compliant
Validated, low cost, high throughput, screening with generic, multi-residue conditions being used for operational efficiency with follow up confirmation
Validated for Decision Limit CCb
– Demonstrate minimal false detects and no false negatives
Calibrate using a positive control at Screening Target Conc.
– STC is the concentration at which a screening test categorises the sample as potentially non-compliant and triggers a confirmatory test
Separate, validated (Decision Limit CCa), confirmatory analysis, often carried out in duplicate, that meets criteria for quantification and identification
©2015 Waters Corporation 19
Monitoring of vet drug residues
Additional testing is carried out at border control on specific
imports of animal products entering to the European Union
Class-specific methods focusing on selected vet drugs
– e.g. currently chloramphenicol, tetracycline, oxytetracycline and
chlortetracycline and of metabolites of nitrofurans in aquaculture
products from India
©2015 Waters Corporation 20
67 compounds in many
(15000 p.a.) samples of
kidney and muscle
– Penicillins - e.g. amoxicillin
– Cephalosporins - e.g. Cefalexin
– Sulfonamide - e.g. sulfadiazine
– Macrolides - e.g. tylosin
– Tetracyclines - e.g. tetracycline
and epimer
– Quinolones - e.g. oxolinic acid
– Fluoroquinolones - e.g.
danofloxacin
– Aminoglycosides - e.g.
streptomycin (needs separate
analysis)
Screening for antibiotics: UPLC-MS/MS Acquity and Xevo TQ-S
©2015 Waters Corporation 21
Confirmation by repeat analysis by UPLC-MS/MS after the generic extraction
min1.350 1.400 1.450 1.500 1.550 1.600 1.650
%
0
100
F5:MRM of 3 channels,ES+
451.1 > 416
Run36793_a_026 Smooth(Mn,1x2)
7.532e+006Tetracycline-D6 Surrogate
1.39
min
%
0
100
F2:MRM of 3 channels,ES+
445 > 98.1
Run36793_a_026 Smooth(Mn,1x2)
1.267e+007Tetracycline
1.40
min
%
0
100
F2:MRM of 3 channels,ES+
445 > 154.1
Run36793_a_026 Smooth(Mn,1x2)
1.490e+007Tetracycline
Compound name: Tetracycline
Correlation coefficient: r = 0.998630, r^2 = 0.997262
Calibration curve: 0.392801 * x + -0.0295833
Response type: Internal Std ( Ref 8 ), Area * ( IS Conc. / IS Area )
Curve type: Linear, Origin: Exclude, Weighting: 1/x, Axis trans: None
x STC-0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00
Re
sp
on
se
-0.00
1.00
2.00
3.00
x STCR
esid
ua
l
-2.5
0.0
2.5
5.0
• Tetracycline in pig kidney
• Duplicate test portions: 4.9 and 5.1 µg/kg
• Sample ion ratios 0.86 cf. 0.88 in reference
©2015 Waters Corporation 22
Balance between time and robustness
Extracts of animal products contain fat and phospholipids which are not removed using dSPE using C18 in dSPE
– Soluble in solvents with high % MeCN so extracts remain clear
These are initially retained on the LC column but elute many injections later causing ion suppression
– Potential for non-detects and/or poor quantification
©2015 Waters Corporation 23
Vet drugs in milk
0
20
40
60
80
100
120
140
Cim
ate
rol
Cle
nbute
rol
Racto
pam
ine
Salb
uta
mol
Terb
uta
line
Tulo
bute
rol
Zilpate
rol
Clindam
ycin
Ery
thro
mycin
kitasam
ycin
Lin
com
ycin
Spiram
ycin
Tilm
icosin
Tylo
sin
Sulfachlo
rpyridazin
e
Sulfaclo
zin
e
Sulfadim
eth
oxin
e
Sulfaguanid
ine
Sulfam
era
zin
e
sulfam
ete
r
Sulfam
eth
azin
e
sulfam
eth
izole
Sulfam
eth
oxypyridazi…
sulfanilaceta
mid
e
sulfaphenazole
Sulfapyridin
e
sulfis
om
idin
e
Trim
eth
oprim
Cin
oxacin
Cip
rofloxacin
Danofloxacin
Diflo
xacin
Enoxacin
Enro
floxacin
Flu
mequin
e
Lom
efloxacin
Marb
ofloxacin
Nalidix
ic a
cid
Norf
loxacin
Ofloxacin
Orb
iflo
xacin
Oxolinic
acid
Pefloxacin
Sara
floxacin
Chlo
ram
phenic
ol
florf
enic
ol
Thia
mphenic
ol
penic
illin V
Beta
meth
asone
Cort
isone
Dexam
eth
asone
Hydro
cort
isone
Mepre
dnis
one
Meth
ylp
rednis
olo
ne
Pre
dnis
olo
ne
Triam
cin
olo
ne
Triam
cin
olo
ne
…
Cefo
taxim
e
Ceft
iofu
r
cephapirin
60 compounds in 9 drug classes in milk
Phospholipids
©2015 Waters Corporation 24
Identification criteria in Europe
Decision 2002/657/EC
– Animal tissues including veterinary drugs but often applied to other situations
– Weighting based upon the selectivity of the technique employed
o Identification points (IP) system
– Employs an ion ratio tolerance based upon ion intensity
– Does NOT offer an alternative of using mass accuracy from HRMS
– The minimum acceptable RT for the analyte(s) should be at least 2x the RT corresponding to the void volume of the column
– RT should correspond to that of a reference with a tolerance of ±2.5%
Relative intensity (% of
base peak)
LC-MS/MS (relative)
> 50 % 20 %
> 20 % to 50 % 25 %
> 10 % to 20 % 30 %
≤ 10 % 50 %
©2015 Waters Corporation 25
Identification criteria in Europe
Document No. SANCO/12571/2014
– Pesticide analysis
– No weighting based upon the selectivity of the technique
o No IP system
– No longer employs an ion ratio tolerance based upon ion intensity
o Tolerance for all LC-MS data is ±30% regardless
– Does offer an alternative of using mass accuracy from HRMS
o At least two diagnostic ions with mass accuracy of <5 ppm
– The minimum acceptable RT for the analyte(s) should be at least 2x
the RT corresponding to the void volume of the column
– RT should correspond to that of a reference with a tolerance of ±0.2
min
©2015 Waters Corporation 26
Limitation of targeted approach
Management of acquisition
– Need to program methods with RTs of analytes and specific transitions to monitor
– Will fail to detect other contaminants present in the sample
– Unable to go back and “mine” the data later
An alternative approach is to use LC-HRMS
– Full spectral acquisition with equivalent sensitivity to TQ
– High mass resolving power used for selectivity
– Database/library searching via mass measurements and isotope patterns of molecular species and fragment ions
o Potential to screen for more compounds compared to TQ
– If no hits, search elsewhere (e.g. Chemspider) for assignment of likely structures to the empirical formulae proposed by the software
– Use of ion mobility for added peak capacity and selectivity and CCS values to aid identification
©2015 Waters Corporation 27
MSE from a Xevo G2-XS
Alternate Low and
High Energy Scans
Low Energy Data
High Energy Data
Combined Data
©2015 Waters Corporation 28
Atrazine – QTof (HRMS)
Identification from more
sources of information
©2015 Waters Corporation 29
Processing MSE data using UNIFI
Raw data
Organise
Componentisation
Analyse
Report results
Peak
componentisation Only needs to be
done once
Performed in parallel
with data acquisition
Speedy
application
processing Through setting
various questions via
the application of
filters that are
combined into a
workflow
©2015 Waters Corporation 30
Workflow Filter
View
Report
Filter = Question imposed on data
View = What information do you need displayed to
answer the question?
Workflow Step = Filter + View, invoked by a single
click
Workflow = Series of workflow steps to review and
entire injection consistently, concisely and accurately.
Report = User reviewed result (answer)
Analysing the data in UNIFI
©2015 Waters Corporation 31
Workflow flexibility
Non-targeted Screening - Qualitative
Unknown Screening – Binary Compare
Non-targeted Screening Quan-Qual
Transformation products– Met ID
Same data, different questions
©2015 Waters Corporation 32
Review positively identified: injections and components
©2015 Waters Corporation 33
Score : 28/28
Some results for qualitative screening of strawberry pomace
©2015 Waters Corporation 34
Ion mobility for non-targeted screening on VION IMS QTof
Drift time
m/z
Drift time
m/z
Mobility
separation
Co-eluting precursor ions
Drift time aligned
precursors and products
©2015 Waters Corporation 35
The enhancements ion mobility offers
Provides selectivity orthogonal to UPLC and MS
– Separation based on shape, charge and mass
– Potential for separation of isomers
– Generates extra peak capacity
Generates cleaner mass spectra
– Facilitates more reliable interpretation
©2015 Waters Corporation 36
MSE mass spectra with no ion mobility
Fragments are Retention time aligned
©2015 Waters Corporation 37
HDMSE with ion mobility providing spectral clean-up all the time
Fragments are Mobility Aligned And Retention time aligned
©2015 Waters Corporation 38
Use of collision cross section (CCS) information reduces errors
in compound identification
Drift time is a measure of an ion’s mobility
– Related to the shape of the molecule in the gas phase
Applying a calibration gives us values for CCS
– Measurements are reproducible and don’t vary with matrix
o More robust a parameter than retention time
– Reduces false detects/positives and non-detects/false negatives
during non-targeted screening
The enhancements ion mobility offers
©2015 Waters Corporation 39
Results summary for EU RL proficiency sample FV-13
29 Observed Using a Screen with 1
min RT Window, and 20 ppm Mass Accuracy
8 Expected to be detected
©2015 Waters Corporation 40
Results summary for EU RL proficiency sample FV-13
9 Observed Using a Screen with 1 min
RT Window, 10 ppm Mass Accuracy and Fragment Presence
8 Expected to be detected
©2015 Waters Corporation 41
Results summary for EU RL proficiency sample FV-13
8 Observed Using a Screen with 1 min
RT Window, 10 ppm Mass Accuracy, Fragment Presence and 2% CCS
Tolerance
8 Expected to be detected
False +ve
False –ve based upon
mass accuracy alone
©2015 Waters Corporation 42
Conclusions
Improved performance of multi-residue methods has increased the scope of analysis with targeted analyses in routine use
Can be used to fulfil criteria for different types of analysis
There has been a significant increase in reports of the use of non-targeted screening methods (by specialist laboratories?)
– Mostly non-targeted high mass resolution mass spectrometry acquisitions supported by targeted data processing
– Mass accuracy alone is insufficient for preventing false detects
– Adding other mass spectrometric parameters helps but can lead to increase in the number of non-detects
Ion mobility increases peak capacity and provides spectral cleanup
– CCS provides an additional useful analytical measurement to aid with screening and identification
The next challenge is to continue to make this truly routine