ESI-LC-MS Matrix Effect Toolbox

Comparison of different methods aiming to account for/overcome matrix 

effects 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 

 

 

 

Anneli Kruve

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 

Nebulizer gas N 2 

+ 

+ 

+ 

HPLC effluent 

Nebulizer 

+ 

+ 

+ + + + + + + + + 

Voltage ~3500 V 

+ 

MS 

Drying gas N 2 

Waste 

2

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 

• Conclusions 

3

Matrix effect 

• Ionization efficiency in ESI depends on: 

– Solvent composition 

– ESI parameters 

– Compounds 

co-eluting with 

analyte 

Are not present 

in standards but 

are present in 

samples 

Are kept constant 

during analyses 

Same amount of 

analyte gives 

different signal 

in sample and in 

standard 

Matrix effect 

4

How does matrix effect look like? 

Intens. 

x10 5 

Analyte in standard 

4 

3 

2 

Analyte in sample 

1 

0 

12.0 12.5 13.0 13.5 14.0 14.5 15.0 Time [min] 

5

Combating matrix effect 

• Reducing matrix effects 

– Sample preparation 

– Dilution of the sample 

– Instrumental parameters 

• Taking matrix effect into account 

– Correcting results 

– Uncertainty 

6

Evaluation of matrix effect 

• Is expressed as a ratio of analyte signal in sample and in 

standard: %ME 

PeakArea 

% ME = 

Sample 

⋅100% 

PeakArea S tandard 

CalibrationGraphSlope 

% ME = 

CalibrationGraphSlope 

Sample 

Standard 

⋅100% 

• %ME 100% - no matrix effect 

• %ME100% - ionization enhancement 

7

Glyphosate calibration graph in 

cereals 

Slopes vs Peak Areas 

1.60E+07 

1.40E+07 

Standard 

Wheat 

Rye 

In case of wheat calibration 

graph becomes nonlinear - 

%ME is not constant 

1.20E+07 

In case of strong supression 

1.00E+07 

calibratin graph is linear 

Peak area 

8.00E+06 

6.00E+06 

4.00E+06 

2.00E+06 

0.00E+00 

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 

c (mg/kg) 

8

Matrix effect’s dependence on 

analyte concentration 

Garlic sample 

Aldicarb 

Methomyl 

Thiabendazole 

0 1 2 3 4 5 

c, mg/kg 

160% 

140% 

120% 

100% 

80% 

60% 

40% 

20% 

0% 

% M E 

• %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 

9

Sample dilution 

%ME 

120% 

100% 

80% 

60% 

40% 

20% 

0% 

0.00 0.20 0.40 0.60 0.80 1.00 1.20 

Dilution factor 

• The amount of 

co-eluting compounds 

is reduced 

• Matrix effect is 

reduced 

• Matrix effect may or 

may not be eliminated 

10

Calculated concentration (mg/kg) 

0.60 

6.00 

0.50 

0.45 

0.50 

A 5.00 

B 

0.40 

C 

0.40 

4.00 

0.35 

0.30 

0.30 

3.00 

0.25 

0.20 

0.20 

2.00 

0.15 

0.10 

0.10 

1.00 

0.05 

0.00 

0.00 

0.00 

0 0.2 0.4 0.6 0.8 1 1.2 

0 0.05 0.1 0.15 0.2 0.25 0.3 

0 0.2 0.4 0.6 0.8 1 1.2 






No matrix effect 

Dilution eliminates 

matrix effect 

Dilution does not 

eliminate matrix effect 

Analyte concentration is 

calculated as the average 

of all the measurements 


the average of 3 most 

diluted samples 


estimated as the 

intercept of the plot 

11

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 E n scores all of the calculated 

concentrations agreed with the spiked 


12

Sample preparation 

180% 

160% 

140% 

120% 

100% 

80% 

60% 

40% 

20% 

0% 

Luke QuEChERS MSPD 

14 

aldicarb sulphoxide 

aldicarb sulphone 

demeton-S-methyl sulphoxide 

carbendazim 

methomyl 

thiabendazole 

methiocarb sulphoxide 

methiocarb sulphone 

aldicarb 

imazalil 

phorate sulphoxide 

phorate sulphone 

methiocarb 

Luke and MSPD result in less matrix effect

Thiodicarb 

• In all samples 

ionization 

enhancement was 

observed 

• Enhancement occured 

with all sample 

preparation methods 

Valge klaar (Rakvere) 

Blank solvent 

700% 

600% 

500% 

400% 

300% 

200% 

100% 

0% 

15 

Valge klaar (Tartu) 

Tellissaare 

Melba (Rakvere) 

Antonovka (Tartu) 

Kuldrenett (Tartu) 

Kuldrenett (Rakvere) 

Talvenauding 

Suislepp 

Pikniku 

%ME

“Seeing” matrix effect 

UV absorbance, mAU 

18 

16 

14 

12 

10 

8 

6 

Interfring 

compound 

Minimum in aldicarb 

peak in case of 

QuEChERS extract 

6000000 

5000000 

4000000 

3000000 

2000000 

MS signal, cps 

• Next to aldicarb a 

peak elutes in the UVchromatogram 

• The shape of aldicarb 

peak is distorted 

4 

1000000 

2 

0 

12.3 12.8 13.3 13.8 14.3 

0 

Retention time, min 

16

Correlation between the UV peak 

and matrix effect 

U V pe a k a re a 

450000 

400000 

350000 

300000 

250000 

200000 

150000 

• Measurements are 

carried out at different 

analyte 

concentrations! 

100000 

50000 

0 

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

%ME 

17

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 

19

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 

20

Scanned Spectra 

Garlic samples 

Standards 

Onion samples 

• PCA was used to 

select background ions 

varying most from 

standards to samples 

21

Correction of analysis results 

Average error (mg/kg) 

Average error (mg/kg) 

0.60 

0.50 

0.40 

0.30 

0.20 

0.10 

0.00 

0.70 

0.60 

0.50 

0.40 

0.30 

0.20 

0.10 

0.00 

Methomyl 

Carbendazime 

Thiabendazole 

Aldicarb 

Imazalil 

Methiocarb 

Training set 

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

Number of linear combinations 

Test set 

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

Number of linear combinations 

Methomyl 

Carbendazime 

Thiabendazole 

Aldicarb 

Imazalil 

Methiocarb 

• Background ions 

intensities together 

with analyte peak area 

were used in PLS 

regression to calculate 

the analyte 

concentration 

22

Results 

Average error 

Garlic Onion Garlic Garlic Standard Standard Solvent (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 


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 


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 


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 


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 

23

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 

25

Matrix effect graph 

• Each calibration solution is prepared in a different 

matrix 

– Same commodity group 

– Different commodity groups 

26

Single-matrix 

calibration 

A 

i 

ε 

r 

i 

= b0 + b1 

⋅Ci 

+ ε 

i 

= 

b 

0 

ε 

i 

+ b 

1 

⋅C 

i 

Same commoditygroup 

calibration 

realtive unsigned residuals 

1.200 

1.000 

0.800 

0.600 

0.400 

0.200 

Matrix effect graph for Methiocarb 

eggplant 

beans 

garlic 

apple 

lemon 

rye 

gooseberries 

0.000 

Different commoditygroup 

calibration 

u 

r 

RMS 

= 

Sample) 

n 

∑ 

j= 

1 

( 

r 

ε ) 

j 

n − 2 

u( A = u ⋅ A 

r 

RMS 

2 

Sample 

27

Validation 

• 15 samples were spiked with 4 pesticides and the 

results were calculated 

• According to E n scores all of the calculated 

concentrations but one agreed with the spiked 

r 

concentrations while using u RMS 

calculated in the 

same commodity group 

• Using different commodity groups results in 

higher uncertainty – all results agreed with spiked 


28

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 influnce the %ME 

30

Parameter optima for standards 

and samples 

31

Summary 

• Matrix effect depends on ... 

– ... analytes, matrices and concentrations 

– ... sample preparation 

• Extrapolative dilution 

• Result correction via background ions 

• Uncertainty calculation 

• ESI/MS parameter optimization 

33

Thank you! 

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

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

Siret Kaarel Asko Hanno Gert Ragne Vahur Kristo Kerli Tapio Risto

ESI-LC-MS Matrix Effect Toolbox

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