The Role of Proficiency Tests in the Estimation of Measurement Uncertainty of PCDD/PCDF and PCB Determination by Isotope Dilution Methods

Transcription

1 The Role of Proficiency Tests in the Estimation of Measurement Uncertainty of PCDD/PCDF and PCB Determination by Isotope Dilution Methods Stefano Raccanelli, Environmental Ethical Chemist, Italy

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3 Courtesy of G. Scortichini Measuring uncertainty For the official food and feed control for levels of PCDD/Fs and PCBs non-compliance is defined as exceedance of legal limits beyond reasonable doubt taking into account the measurement uncertainty.

4 Courtesy of G. Scortichini Measuring uncertainty As a consequence when checking for non-compliance in official control, the same results of different laboratories can cause different assessment of results due to application of different measurement uncertainties.

5 Measuring uncertainty (0) Empirical general approach Based on measurements, the Horwitz curve. Mentioned also by ISO. (1) Bottom-up approach Metrological approach based on the identification of the sources of uncertainty and the calculation of their standard uncertainties, then adequately combined. Suggested by ISO and adapted by EURACHEM to the analytical chemistry. (2) Top-down approach Global approach, which combines precision (within-laboratory reproducibility), trueness (bias) and proficiency tests (PTs) studies to calculate measurement uncertainty.

6 (0) Horwitz relation Empirical equation that relate the concentration of chemical to percentual variation coefficient (uncertainty) For concentrations below 120 ppb Thompson proposed

7 (1) Bottom-up approach Uncertainty sources identified repeatability/reproducibility (type A) calibration curve (type A) calibration curve drift (type B) volume (type B) standard purity (type B) sample weighing (type B) Type A evaluation (of uncertainty) Evaluation of uncertainty by the statistical analysis of series of observations. Type B evaluation (of uncertainty): Evaluation of uncertainty from probability density functions based on experience or other information.

8 Courtesy of G. Scortichini Quantifying uncertainty components (Type A) EXAMPLE: PCB-126 in beef fat Repeatability uncertainty Number of tests = 18 Mean recovery = % Standard deviation = 7.0% Relative standard deviation = 6.2% u ( x ) = r s i n i Relative uncertainty =

9 Courtesy of G. Scortichini Quantifying uncertainty components (Type A) EXAMPLE: PCB-126 in beef fat Calibration curve uncertainty Injected amount (pg) Signal ratio Intercept = Slope = R = Residual standard deviation = Relative uncertainty = s y / x = n i= 1 [ y y ] n i 2 i 2

10 Courtesy of G. Scortichini Quantifying uncertainty components (Type B) EXAMPLE: PCB-126 in beef fat Calibration curve drift uncertainty Maximum acceptable variation of RRF i from in-house method: u ( x ) d x 3 ± 20% i = Relative uncertainty = Maximum acceptable variation of RRF i from EPA method 1668B 2008 ± 30%

11 Courtesy of G. Scortichini Quantifying uncertainty components (Type B) EXAMPLE: PCB-126 in beef fat Spiking volume uncertainty Labelled compound solution (dilution 1): Relative uncertainty = u ( x ) v = x i 6 Labelled compound solution (dilution 2): Relative uncertainty = Labelled compound solution (spiking): Relative uncertainty = Relative uncertainty (sum) =

12 Quantifying uncertainty components (Type B) EXAMPLE: PCB-126 in beef fat Standard purity uncertainty Standard solution tolerance taken from analytical certificate: ± 5% u ( x ) p = x i 3 Relative uncertainty = Courtesy of G. Scortichini

13 Courtesy of G. Scortichini Quantifying uncertainty components (Type B) EXAMPLE: PCB-126 in beef fat Sample weighing uncertainty Maximum tolerance taken from calibration certificate of analytical balance: ± 0.3 mg u ( x ) w = x i 3 Relative uncertainty =

14 ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( = = w w u p p u v v u d d u c c u r r u y y u y u c c For models involving only a product or a quotient, the combined standard uncertainty is given by: Where etc., are the uncertainties of the parameters expressed as relative standard deviations. r r u ) ( Calculating the combined expanded uncertainty The expanded uncertainty is usually calculated using a coverage factor k = 2. ) y ( u k ) y ( U c =

15 Courtesy of G. Scortichini Uncertainty source Relative uncertainty Repeatability 0.062* Calibration curve 0.046* Calibration curve drift Volume Standard purity Sample weighing Quantifying TOTAL uncertainty EXAMPLE: PCB-126 in beef fat Relative combined uncertainty Relative expanded uncertainty (k=2) * In routine analysis u(x r ) and u(x c ) are related to the number of replicates

16 Courtesy of G. Scortichini Combined and expanded uncertainty PCB-126 in beef fat * In routine analysis u(x r ) and u(x c ) are related to the number of replicates Repeatability u ( x ) = u( x ) r sample r N N p r N p : number of replicates in precision study Calibration curve u ( x ) = u( x ) c sample c N N p r N r : number of replicates in routine analysis

17 How uncertainty estimated for each individual congener could be propagated to TEQ? Only one is statistically Four correct: approaches in fact can potentially the square be used root of the sum of squares uc ( (RSS) provides the most 29 realistic u ( TEQ estimates ) = ( TEF * u ) 1) The square root of the sum of squares (RSS): 2) The SUM: c i= 1 i ci TEQ) = 29 i= 1 ( TEF i * u ci ) 2 3) The average of (u ci *TEF i ) i=congener 4) The median of (u ci *TEF i ) i=congener

18 (2) Top-down approach Black-box approach Is this a simplified approach?

19 Uncertainty results from combination of two terms: a. Best estimate of global precision (s) (2) Top-down approach b. Best available estimate of global bias (E) and its uncertainty (u E ) uu cc = ff( ss aa ; EE ; uu (EE) bb )

20 a) Best estimate of global precision (s) Global precision is evaluated as the standard deviation of results of repeated measures on the same sample, in a period long enough to include all possible sources than can influence the results: - Different lot of reagents/solvents; - Different analyst; - Different environmental conditions, - Different instrumental calibrations; - Different instrument or after service..

21 Courtesy of F. Pecoraro a) Best estimate of global precision (s) Ripetibility intra-lab. (Rw) Riproducibility (R) Ripetibility (r) Same Laboratory Same reagent lot Same Laboratory same series Same reagent lot Same instrument Different series Same instrument Same Laboratory Different reagent lot Different series Same instrument Same Laboratory Different reagent lot Different series Different laboratory Different reagent lot Differnt instruments Different series Differnt instruments

22 b) Best estimate of global bias and uncertainty uu cc = ff( ss ; EE ; uu ( EE) bbbbbbbb cccccccccccccccccc The best estimate of global bias can be obtained in two ways: I) From CRM analysed several times in different days; ) II) From participation to Proficiency Tests (PT); The estimates can be provided in similar way, I will present the PT method (Northtest approach)

23 Courtesy of F. Pecoraro b) Best estimate from participation to PT: of global bias and its uncertainty uu cc = ff( ss RRRR ; EE ; uu (EE) ) Participating to one PT Participating to several PTs (minimum 6)

24 Courtesy of F. Pecoraro Calculating the combined expanded uncertainty uu cc = ss RRRR 2 + EE RRRRRR 2 + uu (EERRRRRR ) 2 Global precision Global bias Uncertainty of global bias UU = 2 uu cc

25 Pros&Cons of approaches for Measuring uncertainty (0) Empirical general approach Pros: immediate, no cost Cons: not verified, static, low reliability, possible overestimation (1) Bottom-up approach Pros: rigorous if correctly applied Cons: static (generally done only once), expensive, possible underestimation of error source (e.g. ripetibility) (2) Top-down approach Pros: dynamic (updated uncertainty at each PT participation), moderate costs for synergies with other Lab needs (accreditation). Cons: need for participation to some PTs, possible errors in estimation of assigned value and overestimation of assigned uncertainty

26 On-going top-down approach Made it a dynamic way to run your Laboratory! validation data (precision, accuracy) Quality control (QC) results from PTs (more than 1 per year) daily LOQ white matrix/solvent effect variation of response factors

27 the InterCIND + + = The interlaboratory circuit InterCinD is organized in agreement with international guidelines and is accreditated ISO Some features of InterCinD - the samples are NATURALLY CONTAMINATED MATRIXES (es. soil, sediments, ash, food, wastes ); - Matrixes are homogeneized and treated in agreement with the international guidelines for reference materials (ISO Guide 34:2009); - analytes: PCDD/F, PCB-DL, PCB-ICES6, PAH, PBDE, heavy metals; - Data are required in 3 replicates (evaluation of accuracy & precision).

28 Assessing Performance Overall objective Is to provide basis for the laboratories to assess their performances evaluated through the z-scores (accuracy) and dispersion of measures (precision). Accuracy: using z-scores z i, k, j = x i, k, j Precision: using relative range (r) r i, j max( x = i,1, j, x i,2, j, x i,3, j ) min( x x i, j i,1, j, x i,2, j s j, x x i,3, j j )

29 the participants 2013 (26 countries) 2014 (23 countries) Number of labs

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