1. Bear Basin in the Gallatin Mountain Range

2. MONTANA: MEASUREMENT UNCERTAINTY Lab Accreditation Meeting San Diego, March 12 th

3. MT Department of Agriculture Lab Analytical Chemistry Lab 8.625 FTE Montana State University Bozeman, MT ~2,500 samples per year

4. MT Department of Agriculture Lab ~1/2 Feed /Fertilizer samples ~1/2 Pesticide samples ISO 17025 Accreditation November 2014 2 Feed, 1 Fertilizer Method 0.5 FTE QAO position

5. Outline What is Measurement Uncertainty (MU) ? ISO Requirements for MU MT Approach to MU-how we did it!

6. What is Measurement Uncertainty (MU)? Uncertainty associated with measurements Doubt about the result of any measurement Defines possible range/spread of results Tells something about the quality

7. What is Measurement Uncertainty ? Variation in results when they are repeated May be due to natural variations Take a number of readings Average to get to “true” value

8. What is Measurement Uncertainty ? How widely spread are the values Standard deviation-quantify spread Judge quality of measurement from the spread %RSD: Relative Standard Deviation %RSD=Divide STDDEV by AVER and X 100

9. What is Measurement Uncertainty ?

10. ISO MU Requirements Reporting does not give wrong impression Result incomplete without a statement of uncertainty Needs to be understandable and relevant to the user REQUIRED: Procedure to estimate uncertainty

11. ISO MU Requirements Need to look at MU mathematically Need to put a number to the uncertainty You do not have to hire a statistician Reasonable estimation based on method

12. MT Approach Sent Staff to MU training Become familiar with basic steps Become familiar with uncertainty language Goal: Reasonable Estimation Use a practical user friendly approach

13. MT Approach Established rules for calculating uncertainty Follow conventional methods Express uncertainty as consistently as possible Express uncertainty values properly Others need to understand what we have done

14. MT Approach 1: Identify Approach 2: Identify sources of uncertainty 3: Estimate size of uncertainty from each source=%RSD 4: Combine individual uncertainties =overall figure 5: Expand Uncertainty 6: Create Uncertainty Budget

15. Identify Approach Two approaches to estimating uncertainty Type A: use statistics from repeated findings Type B: Non states, other means Use Type A—why? A2LA classifies test methods for determining MU Method based on published consensus methods

16. Type A Use Laboratory Control sample (LCS) Appropriate matrix and concentration LCS has been through all method steps Apply Statistics to estimate MU

18. Identify Sources of Uncertainty 1- Analytical method 2- Sample PREP Both use type A approach

20. MT Approach Lasolacid Method MU SAMPNORESULTMATRIXCOLDATE AB1071726.93Feed21-Mar-11 AB1040328.11Feed22-Feb-11 AB1016527.50Feed20-Jan-11 AB1004727.75Feed06-Jan-11 AB0424530.43Feed10-Dec-10 AB0402627.16Feed18-Nov-10 AB0103631.54Feed08-Apr-10 AB0055429.30Feed24-Feb-10 AB0015929.59Feed21-Jan-10 AB0009028.54Feed13-Jan-10 AB0003626.44Feed06-Jan-10 AA9389931.65Feed03-Dec-09 AA9389030.90Feed02-Dec-09 AA9365230.42Feed09-Nov-09 AA9061227.81Feed06-Mar-09 AA9046828.43Feed19-Feb-09 AA9043727.91Feed13-Feb-09 AA8411628.01Feed17-Dec-08 AA8389928.97Feed26-Nov-08 AA8377329.68Feed18-Nov-08 AA8374130.33Feed14-Nov-08 x (g/T)=28.92 SD(g/T)=1.527

21. MT Example

23. Sample PREP MU EXAMPLE Design set of experiments 3 Different samples Each samples: mass reduction to 4 splits Each split: 3 instrument measurements

24. MT Approach EXAMPLE Sample PREP MU Hypothetical data Repeat for 3 samples AVER SD splits 3 samples Calc: %RSD of AVER SD %RSD= MU Sample PREP Sample 1SD method Split 1 25.00 22.00 23.00 AVER split 123.331.53 Split 2 19.00 16.00 AVER split 218.001.73 split 3 18.00 17.00 15.00 AVER split 316.671.53 AVER SD method1.60 AVER splits19.33 SD method/splits3.53 AVER SD method1.60 SD splits1.93 %RSD9.97 MU sample PREP EXAMPLE9.97%

25. MT Approach Unfamiliar Concepts/Terms Combine Uncertainties Expand Uncertainty Uncertainty Budget

26. MT Example: Lasolacid

27. MT Example

28. Coverage factor K Numerical factor used as a multiplier To expand uncertainty Include more results at greater confidence Typically in the range 2 to 3

29. Degree of Freedom Degree of freedom: number of measurements - 1 For >20 values, k = 2.0, degree of freedom = ∞ Tabulated values of k shown below: n -112345678910 k (corr) 12.7 1 4.303.182.782.572.452.372.312.262.23 n -111121314151617181920 k (corr) 2.202.182.162.152.132.122.112.102.09

30. Lasalocid Measurement Uncertainty The data from LCS AAFCO Lasalocid 2007-30 was used to estimate MU U1: Sample prep 21 data points U2: Testing process 21 data points U c = (~10.0 2 + 5.28 2 ) 1/2 = 11.31 U = (2.0 x 11.31) = 22.62 SymbolSource of Uncertainty Value (%) DistrbDivUncert (1σ) Degree Freedm U1U1 Sample Preparation~10.0N1 ∞ U2U2 Testing process5.28N1 ∞ UcUc Combined Uncertainty11.31 % UExpanded Uncertainty (k=2.0)22.62%

31. Summary of Determination Combined Uncertainty : 11.31% Expand Uncertainty: 11.31 X 2.0 = 22.62% Average: 28.92 g/T 22.62% of 28.92g/T= ± 6.54 g/T 95% confident result will be 22.38-35.46 g/T YAHOO!!

32. Conclusions Deriving measurement uncertainty is so doable Standard protocol for determining uncertainty This is NOT a stumbling block for accreditation Practical approach—usable and manageable

33. References International Standard, ISO/IEC 17025, Section 5.4 Test and calibration methods and method validation. Second edition 2005-05-15, Reference number ISO/IEC 17025:2005(E). Montana Department of Agriculture Quality Management System for ISO accredited methods A2LA Introduction to Measurement Uncertainty-Training Course 2013 A Beginners guide to Uncertainty in Measurement, Stephanie Bell, NPL, Issue 2

34. “ A person who never made a mistake never tried anything new.” Albert Einstein

35. MIDWEST AOAC meeting June 8-10 th 2015 Bozeman, MT SYMPOSIA: Pesticides at work in the Agriculture LAB State AG FEED LABS going ISO-Say what? Veterinary Toxicology and Mycotoxins NFTA technical presentations Food Safety: Chemistry and Microbiology Applications ICP workshop LIMS issues SPECIAL EVENTS: Vendor expo and presentations Tuesday evening social: Mountain Chalet surrounded by snowcapped mountains Casper-Helfe Memorial Golf Outing