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Anneli Kruve

Comparison of different methods aiming to account for/overcome matrixeffects in LC/ESI/MS on the example of pesticide analyses

Anneli Kruve, Ivo Leito Analytical Methods 2013, 5, 3035-3044

http://dx.doi.org/10.1039/C3AY26551J

22

Electrospray ionization

• ESI is used to connect LC and MS

• LC effluent is sprayed into small droplets

• Droplets devide into smaller droplets

• From the surface of small droplets ions can reach gas phase

HPLC effluent

Nebulizer

Waste

Drying gas N2

+

++

+

++ + + ++ + + +++

MS

Voltage ~3500 V

Nebulizer gas N2

3

Contents

• Matrix effects in LC-ESI-MS, their presence and evaluation

• Approaches for combating matrix effects– Extrapolative dilution

– Sample preparation

– Accounting for matrix effects

– ESI optimization to reduce matrix effects

• Conclusions3

44

Matrix effect

• Ionization efficiency in ESI depends on:– Solvent composition

– ESI parameters

– Compounds

co-eluting with

analyte

Are kept constant during analyses

Are not present in standards but are present in samples

Same amount of analyte gives different signal in sample and in standard

Matrix effect

12.0 12.5 13.0 13.5 14.0 14.5 15.0 Time [min]0

1

2

3

4

5x10Intens.

12.0 12.5 13.0 13.5 14.0 14.5 15.0 Time [min]0

1

2

3

4

5x10Intens.

5

Analyte in standard

Analyte in sample

How does matrix effect look like?

66

Combating matrix effect

• Reducing matrix effects– Sample preparation

– Dilution of the sample

– Instrumental parameters

• Taking matrix effect into account– Correcting results

– Uncertainty

77

Evaluation of matrix effect

• Is expressed as a ratio of analyte signal in sample and in standard: %ME

• %ME 100% - no matrix effect• %ME<100% - ionization supression• %ME>100% - ionization enhancement

%100%tandard

Sample⋅=

SPeakArea

PeakAreaME

%100%Standard

Sample⋅=

enGraphSlopCalibratio

enGraphSlopCalibratioME

88

Slopes vs Peak Areas

0.00E+00

2.00E+06

4.00E+06

6.00E+06

8.00E+06

1.00E+07

1.20E+07

1.40E+07

1.60E+07

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0

c (mg/kg)

Pea

k ar

ea

Standard

Wheat

Rye

Glyphosate calibration graph in cereals

In case of wheat calibration graph becomes nonlinear -%ME is not constant

In case of strong supression calibratin graph is linear

99

Matrix effect’s dependence on

analyte concentration

• %ME depends on the analyte concentration in the sample

• Risk of underestimated results at lower concentrations

• %ME can not be used for correction of the analysis results

0%

20%

40%

60%

80%

100%

120%

140%

160%

0 1 2 3 4 5

c, mg/kg

%M

E

Aldicarb

Methomyl

Thiabendazole

Garlic sample

1010

Sample dilution

• The amount of

co-eluting compounds is reduced

• Matrix effect is reduced

• Matrix effect may or may not be eliminated

0%

20%

40%

60%

80%

100%

120%

0.00 0.20 0.40 0.60 0.80 1.00 1.20

Dilution factor

%M

E

1111

No matrix effect

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0 0.2 0.4 0.6 0.8 1 1.2

Dilution factor

Cal

cula

ted

conc

entr

atio

n (m

g/kg

)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0 0.05 0.1 0.15 0.2 0.25 0.3

Dilution factor

Cal

cula

ted

conc

entr

atio

n (m

g/kg

)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0 0.2 0.4 0.6 0.8 1 1.2

Dilution factor

Cal

cula

ted

conc

entr

atio

n (m

g/kg

)

Dilution eliminates matrix effect

Dilution does not eliminate matrix effect

Analyte concentration is calculated as the average of all the measurements

Analyte concentration is the average of 3 most

diluted samples

Analyte concentration is estimated as the

intercept of the plot

A B C

1212

Validation

• 5 fruits and vegetables, spiked with 5 pesticides at 2 concentration levels– 11 observations of situation A– 6 observations of situation B– 33 observations of situation C

• According to En scores all of the calculated concentrations agreed with the spiked concentrations

1313

1414

Sample preparation

0%20%40%60%80%

100%120%140%160%180%

aldica

rb su

lpho

xide

aldica

rb su

lpho

ne

dem

eton-

S-met

hyl s

ulpho

xide

carb

endaz

imm

ethom

ylthi

aben

dazo

le

met

hiocar

b sulp

hoxid

e

methioc

arb su

lphon

eal

dicar

b

imaz

alil

phor

ate s

ulpho

xide

phor

ate

sulp

hone

methioc

arb

Luke QuEChERS MSPD

Luke and MSPD result in less matrix effect

1515

Thiodicarb

• In all samples ionization enhancement was observed

• Enhancement occured with all sample preparation methods

0%

100%

200%

300%

400%

500%

600%

700%

Pikniku

Suislepp

Talve

naudin

gKuld

rene

tt (Rakv

ere)

Kuldre

nett (T

artu)

Anton

ovka

(Tar

tu)

Melb

a (R

akve

re)

Telliss

aare

Valge k

laar (T

artu)

Valge k

laar (R

akve

re)

Blank s

olvent

%M

E

1616

“Seeing” matrix effect

• Next to aldicarb a peak elutes in the UV-chromatogram

• The shape of aldicarb peak is distorted

0

2

4

6

8

10

12

14

16

18

12.3 12.8 13.3 13.8 14.3

Retention time, min

UV

ab

sorb

ance

, m

AU

0

1000000

2000000

3000000

4000000

5000000

6000000

MS

sig

nal

, cp

s

Minimum in aldicarb peak in case of

QuEChERS extract

Interfring compound

1717

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

%ME

UV

pea

k ar

eaCorrelation between the UV peak

and matrix effect

• Measurements are carried out at different analyte concentrations!

1818

1919

Hypothesis

• If for aldicarb a compound causing matrix effect can be seen in UV, then for other analytes such compounds may exist also– Scanned mass spectra

• Background ions

2020

Background ions

• Are always there– Solvent impurities

– Plasticizers

• Originate from the sample– Co-extracted compounds

Intensity changes due to matrix effect

May cause matrix effect

2121

Scanned Spectra

• PCA was used to select background ions varying most from standards to samplesOnion samples

Garlic samples Standards

2222

Correction of analysis results

• Background ions intensities together with analyte peak area were used in PLS regression to calculate the analyte concentration

Test set

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

n=1 n=2 n=3 n=4 n=5 n=6

Number of linear combinations

Ave

rag

e er

ror

(mg

/kg

)

MethomylCarbendazimeThiabendazoleAldicarbImazalilMethiocarb

Training set

0.00

0.10

0.20

0.30

0.40

0.50

0.60

n=1 n=2 n=3 n=4 n=5 n=6

Number of linear combinations

Ave

rage

err

or (m

g/kg

)

MethomylCarbendazimeThiabendazoleAldicarbImazalilMethiocarb

2323

Results

Garlic Onion Garlic Garlic Standard Standard SolventAverage error

(mg/kg)

Methomyl Spiked 0.89 0.87 0.48 0.89 1.49 0.90 0.00

PLS 0.74 1.02 0.56 0.90 1.46 0.88 -0.13 0.10

Solvent calibration 0.74 1.01 0.38 0.74 1.42 0.87 -0.07 0.11

Carbendazim Spiked 0.25 0.25 0.14 0.25 0.42 0.25 0.00

PLS 0.17 0.23 0.15 0.20 0.44 0.34 -0.02 0.05

Solvent calibration 0.14 0.21 0.07 0.12 0.38 0.28 -0.02 0.07

Thiabendazole Spiked 1.14 1.11 0.61 1.14 1.91 1.14 0.00

PLS 0.84 1.07 0.92 0.78 2.14 1.41 -0.14 0.25

Solvent calibration 0.65 0.90 0.30 0.38 1.74 1.18 -0.09 0.38

Aldicarb Spiked 0.92 0.90 0.50 0.92 1.54 0.92 0.00

PLS 1.20 0.85 0.78 0.55 1.34 1.00 0.05 0.22

Solvent calibration 0.25 0.58 0.06 0.22 1.37 0.97 -0.11 0.43

Imazalil Spiked 1.13 1.11 0.61 1.13 1.89 1.13 0.00

PLS 1.25 0.83 1.19 0.91 1.89 1.29 0.09 0.27

Solvent calibration 0.32 0.60 0.19 0.38 1.78 1.12 -0.20 0.49

Methiocarb Spiked 0.99 0.97 0.54 0.99 1.66 1.00 0.00

PLS 0.88 1.09 0.86 0.60 1.31 0.95 -0.01 0.24

Solvent calibration -0.10 0.28 -0.11 -0.10 1.47 1.01 -0.12 0.69

2424

2525

Matrix effect as an uncertainty

source

• If low uncertainty is not needed in the analysis then matrix effect can be included as an uncertainty source

• Matrix effect graph approach

• Matrix-matched calibration

2626

Matrix effect graph

• Each calibration solution is prepared in a different matrix– Same commodity group

– Different commodity groups

2727

Single-matrix calibration

Same commodity-group calibration

Different commodity-group calibration

i10 ε+⋅+= ii CbbA

i10

iri Cbb ⋅+=

εε

Matrix effect graph for Methiocarb

0.000

0.200

0.400

0.600

0.800

1.000

1.200

rea

ltiv

e u

nsi

gn

ed r

esi

dua

ls

eggplant

beans

garlic

apple

lemon

rye

gooseberries

( )

21j

2rj

rRMS

−=

∑=

nu

n

ε

SamplerRMSSample)( AuAu ⋅=

2828

Validation

• 15 samples were spiked with 4 pesticides and the results were calculated

• According to En scores all of the calculated concentrations but one agreed with the spiked concentrations while using calculated in the same commodity group

• Using different commodity groups results in higher uncertainty – all results agreed with spiked concentrations

rRMSu

2929

3030

Is matrix effect dependent on

something else ... ?

• According to common understanding ... NO

• ESI parameters influence on matrix effect was studied– 3 different optimization stratagies were used

– Intensity optima and matrix effect optima do not coincide

• Matrix effect can be reduced with appropriate ESI/MS parameters

– ESI/MS parameters DO influncethe %ME

3131

Parameter optima for standards

and samples

3232

33

Summary

• Matrix effect depends on ...– ... analytes, matrices and concentrations

– ... sample preparation

• Extrapolative dilution

• Result correction via background ions

• Uncertainty calculation

• ESI/MS parameter optimization

Thank you!Ivo Koit Minu pere Riin Karin Karl Merit Triin Maris Anna Anna-Helena Olga Kaisa

Lauri GevenArtur Rain Elin Jaan Allan Lauri Signe Ivari Eva-Ingrid Erik Ester Marju

Siret Kaarel Asko HannoGert Ragne Vahur Kristo Kerli Tapio Risto