Bear Basin in the Gallatin Mountain Range

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

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

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

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

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

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

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

What is Measurement Uncertainty ?

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

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

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

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

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

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

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

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

MT Approach Lasolacid Method MU SAMPNORESULTMATRIXCOLDATE AB Feed21-Mar-11 AB Feed22-Feb-11 AB Feed20-Jan-11 AB Feed06-Jan-11 AB Feed10-Dec-10 AB Feed18-Nov-10 AB Feed08-Apr-10 AB Feed24-Feb-10 AB Feed21-Jan-10 AB Feed13-Jan-10 AB Feed06-Jan-10 AA Feed03-Dec-09 AA Feed02-Dec-09 AA Feed09-Nov-09 AA Feed06-Mar-09 AA Feed19-Feb-09 AA Feed13-Feb-09 AA Feed17-Dec-08 AA Feed26-Nov-08 AA Feed18-Nov-08 AA Feed14-Nov-08 x (g/T)=28.92 SD(g/T)=1.527

MT Example

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

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 AVER split Split AVER split split AVER split 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%

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

MT Example: Lasolacid

MT Example

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

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 k (corr) n k (corr)

Lasalocid Measurement Uncertainty The data from LCS AAFCO Lasalocid was used to estimate MU U1: Sample prep 21 data points U2: Testing process 21 data points U c = (~ ) 1/2 = U = (2.0 x 11.31) = 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%

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

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

References International Standard, ISO/IEC 17025, Section 5.4 Test and calibration methods and method validation. Second edition , 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

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

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