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 %ME<100% - ionization supression %ME>100% - 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 Calculated concentration (mg/kg) Dilution factor Dilution factor Dilution factor Calculated concentration (mg/kg) No matrix effect 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 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 concentrations 12
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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 Tellissaare Valge klaar (Tartu) Valge klaar (Rakvere) Blank solvent 700% 600% 500% 400% 300% 200% 100% 0% 15 Melba (Rakvere) Kuldrenett (Tartu) Antonovka (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
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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 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 23
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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 concentrations 28
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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
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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