final report - bipm...marco antonio Ávila calderon, alejandro pérez castorena centro nacional de...

CCQM-K79 Comparison of value-assigned CRMs and PT materials: Ethanol in Aqueous Matrix

Final Report

Measurement Laboratory Sebastian Hein, Rosemarie Philipp

Bundesanstalt für Materialforschung und -prüfung (BAM) Berlin, Germany

Coordinating Laboratory David L. Duewer, Hugo Gasca-Aragon, Katrice Lippa, Blaza Toman

National Institute of Standards and Technology (NIST) Gaithersburg, MD USA

With contributions from: Wolfram Bremser Bundesanstalt für Materialforschung und -prüfung (BAM); Berlin, Germany Marco Antonio Ávila Calderon, Alejandro Pérez Castorena Centro Nacional de Metrologia (CENAM); Querétaro, México Janaína Marques Rodrigues Caixeiro, Eliane Cristina Pires do Rego Inmetro - Instituto Nacional de Metrologia, Qualidade e Tecnologia Xerém, Rio de Janeiro Brazil Adriana Rosso, Mariana Ruiz de Arechavaleta Instituto Nacional de Tecnologia Industrial (INTI); Buenos Aires, Argentina Gavin O’Connor, Gill Holcombe, Stephen L.R. Ellison Laboratory of the Government Chemist (LGC); Teddington, Middlesex UK Jolene D. Splett National Institute of Standards and Technology (NIST); Boulder, CO USA Michele M. Schantz, Katherine E. Sharpless National Institute of Standards and Technology (NIST); Gaithersburg, MD USA Meg Croft National Measurement Institute (NMIA); Pymble, NSW Australia Marcellé Archer, Jayne De Vos National Metrology Institute of South Africa (NMISA); Lynnwood Ridge, South Africa Leonid A. Konopelko, Yury Kustikov D.I. Mendeleyev Institute for Metrology (VNIIM); St.Petersburg, Russian Federation

CCQM-K79 Final Report i

DISCLAIMER

Certain commercial materials, instruments, software, and equipment are identified in this report to specify the experimental procedure as completely as possible. In no case does such identification imply a recommendation or endorsement by the Bundesanstalt für Materialforschung und -prüfung nor the National Institute of Standards and Technology, nor does it imply that the material, instrument, software, or equipment is necessarily the best available for the purpose.

CCQM-K79 Final Report ii

ABSTRACT The 2010 CCQM-K79 “Comparison of value-assigned CRMs and PT materials: ethanol in aqueous media” is the second Key Comparison directly testing the chemical measurement services provided to customers by National Metrology Institutes (NMIs) and Designated Institutes (DIs). CCQM-K79 compared the assigned ethanol values of proficiency test (PT) and certified reference materials (CRMs) using measurements made on these materials under repeatability conditions. Nine NMIs submitted 27 CRM or value-assigned PT materials for evaluation. These materials represent many of the higher-order reference materials then available for this commercially and forensically important measurand. The assigned ethanol mass fraction in the materials ranged from 0.1 mg/kg to 334 mg/kg. All materials were stored and prepared according the specifications provided by each NMI. Samples were processed and analyzed under repeatability conditions by one analytical team using a gas chromatography with flame ionization detection (GC-FID) method of demonstrated trueness and precision. Given the number of materials and the time required for each analysis, the majority of the measurements were made in two measurement campaigns (“runs”). Due to a shipping delay from one NMI, an unanticipated third campaign was required. In all three campaigns, replicate analyses (three injections of one preparation separated in time) were made for one randomly selected unit of each of the 27 materials. Nine of the 27 materials were gravimetrically diluted before measurement to provide solutions with ethanol mass fraction in the established linear range of the GC-FID method. The repeatability measurement value for each analyzed solution was estimated as the mean of all replicate values. The within- and between-campaign variance components were estimated using one-way ANOVA. Markov Chain Monte Carlo Bayesian analysis was used to estimate 95 % level-of-confidence coverage intervals for the mean values. Uncertainty-weighted generalized distance regression was used to establish the Key Comparison Reference Function (KCRF) relating the assigned values to the repeatability measurements. On the basis of leave-one-out cross-validation, all of the assigned values for all 27 materials were deemed equivalent at the 95 % level of confidence. These materials were used to define the KCRF. Parametric Bootstrap Monte Carlo was used to estimate 95 % level-of-confidence coverage intervals for the degrees of equivalence of materials, d±U95(d), and of the participating NMIs, D±U95(D). Because of the very wide range of ethanol mass fraction in the materials, these degrees of equivalence are expressed in percent relative form: %d±U95(%d) and %D±U95(%D). The median of the absolute values of the %D for the participating NMIs is less than 0.05 % with a median U95(%D) of less than 1 %. These results demonstrate that the participating NMIs have the ability to correctly value-assign CRMs and proficiency test materials for ethanol in aqueous media and similar measurands.

CCQM-K79 Final Report iii

TABLE OF CONTENTS 0. INTRODUCTION ..................................................................................................................... 1

0.1 Historical Background ........................................................................................................ 1 0.2 Measurand Background ...................................................................................................... 2 0.3 Key Comparison Design Background ................................................................................ 2

1. STEP 1: DESIGN OF THE STUDY ......................................................................................... 3 1.1 Timeline .............................................................................................................................. 3 1.2 Participants .......................................................................................................................... 3 1.3 Materials ............................................................................................................................. 4

2. STEP 2: MEASURMENTS ....................................................................................................... 6 2.1 Measurement Design .......................................................................................................... 6 2.2 Analytical Method .............................................................................................................. 6 2.3 Chromatography ................................................................................................................. 7 2.4 Material Preparation............................................................................................................ 7

2.4.1 Internal standard and control solutions ........................................................................ 8 2.4.2 Study materials with assigned EtOH values <5 mg/g ................................................. 8 2.4.3 Study materials with assigned EtOH values >5 mg/g ................................................. 8

2.5 Measurement quantitation ................................................................................................... 8 2.6 Measurement Value, Variance Components, and Standard Uncertainty .......................... 11

2.6.1 Statistical Model ........................................................................................................ 11 2.6.2 Measurement values .................................................................................................. 11 2.6.3 Variance components ................................................................................................. 11

2.7 Large-Sample Standard Uncertainties, ( )iRu ′∞ ................................................................. 13 2.8 95 % Coverage Intervals, ( )iRU ′ ....................................................................................... 14 2.9 Summary values for the study materials ........................................................................... 15

3. STEP 3: DEFINE A CONSENSUS MODEL .......................................................................... 18 3.1 Key Comparison Reference Function, KCRF .................................................................. 18 3.2 Generalized Distance Regression analysis........................................................................ 19 3.3 Identifying strongly influential materials.......................................................................... 20 3.4 Identifying strongly consequential materials .................................................................... 21 3.5 KCRF parameters and predictions .................................................................................... 22

4. STEP 4: DEGREES OF EQUIVALENCE .............................................................................. 25 4.1 Degrees of Equivalence for Materials ............................................................................... 25 4.2 Degrees of Equivalence for Participants ........................................................................... 26

5. RECOMMENDATIONS FOR USE OF CCQM-K79 IN EVALUATING CIPM MRA CALIBRATION AND MEASUREMENT CAPABILITY (CMC) CLAIMS ............................. 28

5.1 How Far Does the Light Shine? ........................................................................................ 28 5.2 Demonstrated Competencies ............................................................................................ 28

6. REFERENCES ......................................................................................................................... 39

CCQM-K79 Final Report iv

LIST OF APPENDICES APPENDIX A: Call for Participants............................................................................................ A1 APPENDIX B: WinBUGS Code for Bayesian Estimation of 95 % Confidence Intervals .......... B1

LIST OF TABLES Table 1: Timeline ............................................................................................................................ 3 Table 2: Participating Institutions ................................................................................................... 3 Table 3: Materials ........................................................................................................................... 5 Table 4: Measurements for the Measured Solutions, Expressed in Arbitrary Units .................... 10 Table 5: Repeatability Measurement Uncertainties ...................................................................... 16 Table 6: Assigned and Measured Values for the Study Materials ................................................ 17 Table 7: GDR Model Parameter Estimates ................................................................................... 23 Table 8: KCRF Predicted Values and Responses ......................................................................... 24 Table 9: Degrees of Equivalence .................................................................................................. 26 Table 10a: Core Competencies Demonstrated in CCQM-K79 by BAM ..................................... 29 Table 10b: Core Competencies Demonstrated in CCQM-K79 by CENAM ................................ 28 Table 10c: Core Competencies Demonstrated in CCQM-K79 by INMETRO ............................ 29 Table 10d: Core Competencies Demonstrated in CCQM-K79 by INTI ...................................... 30 Table 10e: Core Competencies Demonstrated in CCQM-K79 by LGC....................................... 31 Table 10f: Core Competencies Demonstrated in CCQM-K79 by NIST ...................................... 32 Table 10g: Core Competencies Demonstrated in CCQM-K79 by NMIA .................................... 33 Table 10h: Core Competencies Demonstrated in CCQM-K79 by NMISA ................................. 34 Table 10i: Core Competencies Demonstrated in CCQM-K79 by VNIIM ................................... 35

LIST OF FIGURES Figure 1: Repeatability Measurement Design................................................................................. 6 Figure 2: Exemplar GC-FID Chromatogram .................................................................................. 7 Figure 3: Within-Campaign Imprecision as a Function of Mean Response ................................. 12 Figure 4: Between-Campaign Intermediate Imprecision as a Function of Mean Response ......... 13 Figure 5: Overview of the Relationship Between Assigned and Measured Values ..................... 18 Figure 6: High-Resolution Scattergrams ...................................................................................... 20 Figure 7: Identification of Strongly Influential Materials............................................................. 21 Figure 8: Identification of Strongly Consequential Materials ...................................................... 22 Figure 9: Degrees of Equivalence for Materials ........................................................................... 25 Figure 10: Degrees of Equivalence for Participants ..................................................................... 27

CCQM-K79 Final Report v

0. INTRODUCTION The Working Groups of the Consultative Committee for Amount of Substance – Metrology in Chemistry (CCQM) are responsible for selecting and overseeing the operation of Key Comparisons (KCs) that address chemical measurement-related issues important for international trade, environmental, health, and safety-related decision making. One objective of the Comité International des Poids et Mesures Mutual Recognition Arrangement (CIPM MRA) [1] is to establish the degree of equivalence of national measurement standards maintained by National Metrology Institutes (NMIs) and Designated Institutes (DIs). To more efficiently address this objective, in 2009 the CCQM Organic Analysis Working Group (OAWG) agreed to four basic study designs:

Track A: KCs that test core competencies for the delivery of measurement services to customers,

Track B: KCs that directly assess the equivalence of measurement services provided to customers,

Track C: KCs in emerging areas of global interest and importance with a potential parallel Pilot Study,

Track D: Capability assessment studies to allow assessment of measurement capabilities being established in a new area for NMIs and DIs.

0.1 Historical Background At the April 2009 CCQM OAWG Meeting (Sévres, France), an experimental design was proposed for OAWG KCs that directly test measurement services provided to customers through Certified Reference Materials (CRMs) and value-assigned Proficiency Testing (PT) materials. The premise of the Track B design is that these services can be demonstrated when several different value-assigned materials are available that deliver a nominally identical measurand (i.e., a given analyte in a sufficiently similar matrix with the assigned quantity value expressed in given units). This can be accomplished by comparing the assigned values with measurements made on the entire group of materials under repeatability conditions. Such comparisons directly reflect the measurement capabilities of the organizations that value-assigned the materials when not constrained by sample amount or reporting deadline. Track B comparisons also address the material homogeneity and stability assessment capabilities of the participating institutions and potentially their packaging, storage, and shipping capabilities. On the basis of the number of NMIs and DIs providing suitable materials, the number of available materials, and the intrinsic importance of the measurands, the OAWG authorized Track B KCs for ethanol (EtOH) in an aqueous matrix and creatinine in human serum. BAM and NIST were volunteered to co-coordinate the EtOH study, later designated “CCQM-K79 Comparison of value-assigned CRMs and PT materials: Ethanol in aqueous matrix,” with BAM to make the required repeatability-condition measurements and NIST to provide data analysis. Given the potential utility of such KCs, all OAWG member institutions that deliver measurement services via one or more appropriately value-assigned materials were asked to participate.

CCQM-K79 Final Report 1

0.2 Measurand Background Measurement of EtOH content in aqueous matrix is important in both commercial and forensic contexts. EtOH is a commodity that is traded worldwide in the form of beverages such as beer, wine, and spirits. The EtOH content of these materials is widely regulated and subject to excise duties. The content of EtOH in the body of consumers of these beverages affects both their coordination and judgment resulting in the widespread regulation of permissible “blood alcohol” and “breath alcohol” limits for the legal operation of vehicles and other equipment. Since the quality of quantitative EtOH measurements directly affects regulatory enforcement, both commercial and forensic applications require use of measurement systems calibrated and validated with aqueous EtOH standards. In 2001, the OAWG initiated the pilot study CCQM-P35 Determination of Ethanol in Aqueous Matrix. Based on the results of this pilot, CCQM-K27 Determination of Ethanol in Aqueous Matrix was initiated in 2002 [2]. Follow-on studies CCQM-K27.1 and CCQM-K27.2 were initiated in 2003 and 2006, respectively, to accommodate laboratories that had not been ready to benchmark their methods in the original CCQM-K27 study or that wished to benchmark a different method [3,4]. These studies demonstrated the equivalence of measurement results obtained using several measurement techniques within the range 0.2 mg/g to 120 mg/g.

0.3 Key Comparison Design Background Since different CRM and PT materials for the same nominal measurand will likely deliver different analyte quantities and may have somewhat different matrices, the evaluation of degrees of equivalence among the participants requires a quite different approach than that of studies where participants analyze nominally identical samples of the same material. The basic methodology for comparing two sets of measurement results (assigned values and repeatability measurements) for a given group of materials has been used with success in a number of studies conducted by the CCQM Gas Analysis Working Group (GAWG). The relatively unfamiliar experimental design and methodology considerations for Track B KCs are described at some length in the report “Comparison of value-assigned CRMs and PT materials: experimental design and data evaluation” [5]. The design and analysis considerations and tasks can be divided into the following four steps: Step 1) Design the study taking into account the number of candidate materials and their analyte

levels and matrices together with the analytical capabilities and available resources of the measurement laboratory.

Step 2) Inform the participants of the materials and material quantities required and a target date for supplying those materials to the measurement laboratory. Collect all materials at the measurement laboratory. Store the materials under the conditions specified by the participants until such time as measurements are made. Make the measurements under repeatability conditions. The measurement procedure needs to provide results that are a simple function of analyte level (linear in mass fraction is best) but does not need to be calibrated. Summarize the measurement results for each material as a value and an uncertainty on that value.

Step 3) Establish a consensus model that relates the assigned and measured values, using a technique that takes into account the uncertainties on both the assigned and measured values.


Step 4) Estimate the difference between the assigned and measured value for each material and the value predicted from the consensus model, taking into account the uncertainties on the definition of the model as well as those on the observed values. Convert these differences into degrees of equivalence for each of the study materials. If there are two or more study materials from a given participant, combine the degrees of equivalence for those materials into a degree of equivalence for the participant.

This report presents the results of CCQM-K79 in the context of this four-step structure.

1. STEP 1: DESIGN OF THE STUDY

1.1 Timeline

Table 1: Timeline Date Action

21 Apr 2009 OAWG authorized K79 study and approved basic protocol (Sèvres) 9 Oct 2009 Call for Participation emailed to OAWG members (see Appendix A)

11 Dec 2009 Most materials received at BAM, first measurement campaign 8 Jan 2010 Final materials received at BAM, second measurement campaign 25 Jan 2010 Third and final measurement campaign 4 Apr 2010 Preliminary results presented to OAWG (Sèvres)

20-Oct-2010 Draft A report distributed to OAWG 25-Oct-2011 Draft B report distributed to OAWG 31-Jan-2013 Final report delivered to OAWG Chair

1.2 Participants

Table 2: Participating Institutions Code Institution Country BAM Bundesanstalt für Materialforschung und -prüfung Germany

CENAM Centro Nacional de Metrología México

INMETRO Inmetro - Instituto Nacional de Metrologia, Qualidade e Tecnologia Brazil

INTI Instituto Nacional de Tecnología Industrial Argentina LGC Laboratory of the Government Chemist UK NIST National Institute of Standards and Technology USA NMIA National Measurement Institute Australia

NMISA National Metrology Institute of South Africa South Africa VNIIM D.I. Mendeleyev Institute for Metrology Russian Federation


1.3 Materials Given the established performance characteristics of the EtOH in aqueous matrix measurement system used by BAM, eligibility for inclusion in CCQM-K79 was limited to materials with EtOH mass fractions between 0.1 mg/g and 500 mg/g. Eligibility was further limited to materials 1) with certification values valid as of the 25 January 2010 measurement date and 2) that had assigned values either directly stated in units of mass fraction or that could be converted into units of mass fraction. All but one of the OAWG members known to support EtOH in aqueous matrix CRMs participated in CCQM-K79; this institution chose not to convert their volume/volume units of certification to the required mass fraction quantity. To limit the materials in CCQM-K79 to a number that could be measured under repeatability conditions, participating institutions were asked to submit no more than three of their materials. Participants supporting more than three materials were asked to submit three materials representative of low, medium, and high EtOH content within the 0.1 mg/g to 500 mg/g range. Exceptions to the limit of three were made for materials value-assigned by different methods or of modified matrix. Participants were asked to provide three units of each of the value-assigned material submitted. All materials were stored at the temperature specified in the provided instructions from the time of receipt to when the material was prepared for analysis. Participants with materials value-assigned in quantities other than mg/g were asked to provide the converted values and the 95 % level of confidence uncertainty on the converted values. Table 3 summarizes the certification information provided by the participants for the 27 materials submitted to CCQM-K79. In addition to identifying the certifying institution, the material, the assigned value “V,” and the uncertainty on the assigned value at a 95 % level of confidence “U95(V),” Table 3 lists information deemed useful for evaluating the materials’ suitability for inclusion in the KC, for storing and processing the materials, and for the design of the repeatability measurement campaigns. This auxiliary information includes additives to the aqueous matrix, the amount of material available per unit, any specified minimum sample quantity per analysis, the recommended storage temperature, and the expiration date for the certification. Most of this auxiliary information was available in the certification documents supplied by the participating institutions in response to the solicitation. When required information was not supplied in submitted documents, it was solicited via email. The repeatability measurements were not begun until all auxillary information was supplied and compiled and the accuracy of the compilation confirmed by the participating institutions. Table 3 also lists the basic analytical technique(s) used to value-assign the materials.


Table 3: Materials

Assigned Value, mg/g Auxiliary Information a NMI Material V U95(V) Additive mL Min °C Expires Method b

BAM BAM-K001 1.03133 0.00099 6000 Room temp 16-May-10 GP BAM BAM-K003 0.61117 0.00059 6000 Room temp 16-May-10 GP BAM BAM-K007 3.3983 0.0033 6000 Room temp 16-Mar-10 GP

CENAM DMR-382a 162.6 1.9 5 15-25 1-Mar-11 GC CENAM DMR-383a 289.8 3.0 5 15-25 1-Mar-11 GC

INMETRO MRC1.01 0.5094 0.0068 500 20-25 13-Nov-10 GP INMETRO MRC1.03 1.068 0.014 500 20-25 13-Nov-10 GP INMETRO MRC1.05 4.956 0.054 500 20-25 13-Nov-10 GP

INTI CIN13013 0.2407 0.0027 10 ≈ 25 7-Nov-10 GC INTI CIN13014 0.5975 0.0062 10 ≈ 25 7-Nov-10 GC INTI CIN13015 1.445 0.019 10 ≈ 25 7-Nov-10 GC LGC ERM401e 0.798 0.006 Hg(II)Cl2 25 20 1-9 Ship+1 y T LGC ERM403b 2.007 0.009 Hg(II)Cl2 25 10 1-9 Ship+1 y T LGC ERM404e 39.6 0.3 50 2 1-9 Ship+1 y Py LGC ERM406d 333.5 0.3 50 2 1-9 Ship+1 y Py LGC ERM409a 0.200 0.006 Hg(II)Cl2 50 30 1-9 Ship+1 y T NIST SRM-2892 0.3900 0.0046 1.2 10-30 31-Jan-14 GC, GP, T NIST SRM-2897 15.54 0.16 10 10-30 31-Jan-14 GC, GP, T NIST SRM-2899 252.1 2.2 10 10-30 31-Jan-14 GC, GP, T

NMIA TSL/426 1.2263 0.0074 Hg(II)Cl2 40 4-10 30-Jun-11 IDMS NMISA ORG001-1 0.0958 0.0038 25 0.5 4-10 3-Feb-10 GP, T NMISA ORG001-2 3.027 0.030 25 0.5 4-10 24-Feb-10 GP, T NMISA ORG001-3 104.3 1.3 25 0.5 4-10 28-Oct-10 GP, T NMISA ORG003-2 67.19 0.67 20 % glucose 25 1.6 4-10 12-May-10 GP VNIIM 07.22.001-a 0.1006 0.0005 20 0-10 24-May-10 GP VNIIM 07.22.001-b 3.04 0.015 20 0-10 24-May-10 GP VNIIM 07.22.001-c 6.01 0.03 20 0-10 24-May-10 GP

a Additive is any modification of the aqueous matrix, mL is the volume of material per unit, Min is the specified minimum volume in mL of material per analysis, °C is the specified storage temperature, Expires is the expiration date of the certification.

b Method is the certification method(s) used to value assign the material: GC = gas chromatography, GP = gravimetric preparation, IDMS = isotope dilution mass spectrometry, Py = pycnometry, T = dichromate titration.


2. STEP 2: MEASURMENTS

2.1 Measurement Design Participants provided the measurement laboratory with three units of each of their submitted materials, two to be analyzed and a spare in case of technical failure or to facilitate investigation of disputed results. Given the number of materials and the time required for each analysis, and a shipping delay with the materials from one NMI, the repeatability measurements were made in three measurement campaigns (“runs”). To check the most divergent measurements made in the first two campaigns and to provide context for the measurements of the shipping-delayed materials, the third units of nine materials were evaluated in the third campaign. In all three campaigns, replicate analyses (three injections) were made on one preparation of one unit of each material. This two-level nested design is summarized in Figure 1.

Figure 1: Repeatability Measurement Design

Due to the constraints of the analytical method, measurements on the CCQM-K79 materials were performed in order of either increasing or decreasing EtOH quantity amount. All measurements within each campaign were made under repeatability conditions. The campaigns were separated in time by two-to-three weeks. Measurements on gravimetrically prepared control solutions were alternated with the study samples to identify and enable compensation for any within-campaign drift and between-campaign bias. Control solutions were injected twice, sample solutions in triplicate. The same analysts prepared the samples and made the measurements in all three campaigns. The above design confounds between-unit and between-campaign sources of measurement imprecision. The measurements made for this study thus cannot be used to estimate between-unit inhomogeneity for any of the study materials.

2.2 Analytical Method All materials were analyzed at BAM using a gas chromatography with flame ionization detection (GC-FID) method of demonstrated trueness and precision [2]. Aliquots of all study materials were gravimetrically prepared with n-propanol (PrOH) internal standard. As part of sample preparation, aliquots of materials with assigned values >5 mg/g were gravimetrically diluted with water to enable measurement of EtOH levels within the method’s proven linear range. All prepared samples were injected directly onto a highly polar chromatographic column.

Materiali

Unit1 Campaign1

Inj1 Inj2 Inj3

Unit2 Campaign2

Inj1 Inj2 Inj3

Unit3 Campaign3

Inj1 Inj2 Inj3

Measurements in third campaign made only on the shipping-delayed materials and materials with >0.2 % relative difference between first two campaigns.


2.3 Chromatography Table 4 lists the critical parameters for the GC-FID analysis method. Peak integration was performed automatically using the instrument software. Figure 2 illustrates a representative chromatographic separation.

Table 4: GC-FID Parameters Parameter Value Instrument HP (now Agilent) 6890 GC with FID

Column stationary phase: nitroterephthalic acid modified polyethylene glycol (HP-FFAP), 50 m, 0.32 mm inner diameter, 0.53 µm film thickness

Injection 0.5 µl, autosampler, cool on column (70 °C) Carrier gas H2, constant pressure 52 kPa

Oven 8.5 min at 70 °C, 30 °C /min to 180 °C, 3 min at 180 °C FID 250 °C, H2 40 mL/min, synthetic air 450 mL/min, make-up gas N2 45 mL/min

Figure 2: Exemplar GC-FID Chromatogram

min2 3 4 5 6 7 8

pA

0

100

200

300

400

500

600

700

800

4.2

27

5.8

18

3 3.5 4 4.5 5 5.5 6 6.5

20

2.4 Material Preparation All materials were allowed to equilibrate to ambient temperature and were thoroughly mixed by shaking before processing.

A) Complete chromatogram B) Expanded scale of the same

chromatogram

Pink lines represent the automatic baseline assigned by the instrument software.

EtO

H

PrO

H

EtO

H

PrO

H

A

B

Det

ecto

r Res

pons

e, p

A

Time, min


2.4.1 Internal standard and control solutions The PrOH used to prepare the IS solution and the EtOH used to prepare the control solutions were obtained from commercial sources and assessed by GC-FID to assure the absence of interferences. Since the same control and IS solutions were to be used throughout the study, further knowledge of the purity of the source PrOH and EtOH materials was not required for evaluating the results of the repeatability measurements. However, to provide insurance against the unforeseen, the water content of the EtOH was determined by Karl Fischer and found to be 1.3 mg/g; combining this value with the GC-FID information, the EtOH content of the commercial material was estimated to be 998.7 mg/g. A 100 mg/g PrOH in water internal standard (IS) solution was prepared gravimetrically in a 100 mL volumetric flask using deionized water and commercial PrOH (LiChrosolv, Merck KGaA). This IS solution was stored at 4 °C in the dark. About 100 µL of this IS solution was added gravimetrically to each aliquot of control solution and study material analyzed. Eight EtOH in water control solutions were prepared gravimetrically using deionized water and commercial EtOH (Ethanol absolute CHROMASOLV, Sigma-Aldrich). The EtOH was delivered using a gas-tight syringe. These control solutions spanned the range 0.09 mg/g to 5.07 mg/g. A total of 5.4 L stock solution was prepared of each of the three solutions of less than 0.5 mg/g EtOH content; 1 L stock was prepared of each of the others. The control solutions were prepared in screw-capped bottles and stored at room temperature in the dark. 2.4.2 Study materials with assigned EtOH values <5 mg/g For materials delivered in individual units of 10 mL or more, a 10 mL sample aliquot was weighed into a 15 mL amber glass screw-capped vial. The IS (100 µL) was then added gravimetrically. Because NIST SRM 2892 was delivered in 1.2 mL ampoules, the contents of 4 ampoules were combined. The IS (50 µL) solution was gravimetrically added to the combined ≈5 g mass. 2.4.3 Study materials with assigned EtOH values >5 mg/g The nine materials with assigned values >5 mg/g were diluted with water during sample preparation. For each material, 10 mL deionized water was weighed into a 15 mL amber glass screw-capped vial. For the two materials of EtOH value between 5 mg/g and 20 mg/g, 200 µL of the material and 100 µL of the 100 mg/g n-propanol in water IS were added gravimetrically. For the seven materials of EtOH value > 20 mg/g, 100 µL of the material and 100 µL of the IS solution were added gravimetrically.

2.5 Measurement quantitation The column was conditioned at the start of each campaign by injecting a sample containing an ethanol mass fraction of 1 mg/g in water seven times. Blank samples of deionized water were analyzed to confirm the absence of syringe carry-over. In all three campaigns, linearity was excellent over the 0.09 mg/g to 5.07 mg/g range of the control solutions. However, the deviations of control solution measurements from the values predicted from linear regression displayed some structure. Since the materials and control solutions were analyzed in order of increasing or decreasing EtOH content, the measurement results for all study materials were calibrated to the bracketing control solutions rather than to regression models estimated using the entire suite of control solutions.


The GC-FID peak areas and gravimetrically determined masses were transformed into response measurements for the measured solutions, R, using :

( ) ( )−−−

−−−

+++

+++

=−

−−

=+

++

++−+

−+

+=

+===

=−=−−

=

−

+=

∑∑H2OEtOH,IS

EtOH,cal

H2OEtOH,IS

EtOH,cal2

1 ,PrOH

,EtOH2

1 ,PrOH

,EtOH

PrOH

EtOH

H2O,CRM,

IS,

;;21;

21

15000;;

mmmmm

xmmm

mmx

AA

yAA

y

xyxxyy

AA

mmm

R

j

j

j

j

l jl

jl

l jl

jl

ijijij

ijij,ijk

,ijk

ijkijk

ijkijk

γβαβ

βαγ

where i designates the study material, j designates the campaign, k designates the injection for the study materials, l designates the injection for control materials, AEtOH is the EtOH peak area, APrOH is the PrOH peak area, mcal is the mass of the control solution aliquot, mCRM is the mass of the study material aliquot, mEtOH is the mass of EtOH, mH2O is the mass of deionized water, and mIS is the mass of the IS solution. The superscript “-” designates the control solution having AEtOH just smaller than that of a given study material; the superscript “+” designates the control solution having AEtOH just larger than that of the given study material. The constant factor, γ, gives the calculated responses a convenient integer scale. Table 4 lists all of the measurements for the CCQM-K79 materials. These values are not corrected for the purity of the commercial EtOH material used to prepare the control solutions. The measurement responses are formally expressed in arbitrary units (a.u.). For the materials that were evaluated without dilution, the Rijk should be proportional to the EtOH content of the corresponding study materials. The EtOH content of the materials that were diluted before evaluation should be proportional to the product of the measured response and the dilution factor, DiFij, for the ith material in the jth campaign. Since at least some of the components of measurement variability are expected to be proportional to the EtOH content of the measured solutions rather than the study materials themselves, we have chosen to adjust for dilution in two stages. The first stage adjusts the measured values for the between-campaign differences in the dilution factors

,i

N

jijiiijijkijk NDiFDiFDiFDiFRR

i

c1

,c

; ∑=

==′

where Nc,i is the number of campaigns in which the ith material was analyzed and is here either 2 or 3. The second stage, performed after estimating the expected measurement response and its expanded uncertainty, iR′ and ( )iRU ′ , will transform the response for the EtOH content of the solutions to that for the EtOH content of the study materials (see Summary values for the study materials). Table 4 also lists these gravimetric dilution factors.


Table 4: Measurements for the Measured Solutions, Expressed in Arbitrary Units

Campaign1, 11-Dec-2009 Campaign2, 8-Jan-2010 Campaign3, 25-Jan-2010 DiF c NMI Material DiF a Inj1

b Inj2 b Inj3

b DiF a Inj1 b Inj2

b Inj3 b DiF a Inj1

b Inj2 b Inj3

b BAM BAM-K001 1 15465 15465 15471 1 15506 15408 15440 1 BAM BAM-K003 1 9159 9175 9168 1 9164 9161 9163 1 BAM BAM-K007 1 50742 50760 51082 1 50912 50949 50981 1

CENAM DMR-382a 103.44 23528 23503 23513 104.60 23100 23023 23128 101.32 24087 24065 24052 103.12 CENAM DMR-383a 103.33 42224 41905 41980 103.23 42183 42232 42138 104.47 41870 41920 41844 103.68

INMETRO MRC1.01 1 7627 7632 7635 1 7667 7588 7597 1 INMETRO MRC1.03 1 15993 16118 16071 1 15965 15959 15956 1 15949 15938 15934 1 INMETRO MRC1.05 1 74262 74251 74713 1 74393 74361 74363 1

INTI CIN13013 1 3610 3576 3584 1 3573 3602 3593 1 INTI CIN13014 1 8999 8932 8998 1 8954 8959 8972 1 INTI CIN13015 1 21807 21786 21744 1 21601 21600 21528 1 21749 21926 21637 1 LGC ERM401e 1 11993 11999 11995 1 12021 12033 12068 1 12008 11998 12018 1 LGC ERM403b 1 30400 30210 30351 1 30051 30094 30046 1 30212 30143 30170 1 LGC ERM404e 100.71 5841 5850 5851 102.93 5728 5725 5724 101.82 LGC ERM406d 105.52 47213 47251 47231 105.46 47363 47361 47404 105.06 47642 47848 47750 105.35 LGC ERM409a 1 3016 3023 3046 1 3038 3025 3009 1 NIST SRM-2892 1 5833 5862 5813 1 5860 5807 5807 1 NIST SRM-2897 52.04 4450 4473 4469 52.03 4488 4464 4437 52.04 NIST SRM-2899 104.66 36020 36080 36049 106.35 35438 35454 35701 105.51

NMIA TSL/426 1 18399 18423 18547 1 18344 18342 18371 1 18428 18456 18320 1 NMISA ORG001-1 1 1426 1433 1441 1 1434 1430 1445 1 NMISA ORG001-2 1 45351 45095 45375 1 45144 45171 45030 1 45367 45289 45120 1 NMISA ORG001-3 100.87 15513 15517 15529 103.10 15177 15162 15136 101.99 NMISA ORG003-2 91.85 10920 10910 10909 95.66 10451 10475 10464 93.76 VNIIM 07.22.001-a 1 1494 1495 1495 1 1490 1515 1505 1 VNIIM 07.22.001-b 1 45558 45615 45582 1 45681 45593 45661 1 VNIIM 07.22.001-c 51.74 1740 1741 1738 50.70 1785 1768 1770 51.22

a Dilution factor. The EtOH content of a study material is proportional to the product of the EtOH content of the solution and the DiF b Calibrated EtOH/PrOH peak area ratios, arbitrarily scaled to provide convenient integer magnitudes for all materials c Average dilution factor


2.6 Measurement Value, Variance Components, and Standard Uncertainty 2.6.1 Statistical Model The measurement design for CCQM-K79 addressed within-campaign repeatability and the confounded effect of between-campaign intermediate precision and between-unit bias. For this design, the least complicated model for describing measurements on a given material is:

( ) ( )2,c

2r ,0N~;,N~ iij,iijiijkR σγσγµ +′ [1]

where “~” means “is distributed as,” “N(…)” specifies a Gaussian (i.e., normal) distribution of given mean and variance, μi is the (unknown) true value of the measurand for the ith material, σr,i is the (unknown) true within-campaign measurement repeatability imprecision for that material and is assumed to be the same across all campaigns, γij are the (unknown) true between-campaign differences for that material, and σc,i is the (unknown) true between-campaign and/or between-unit measurement imprecision. This “between” imprecision combines effects from sample handling, changes in the measurement process between the campaigns that are not compensated for by (or are introduced by) the bracket-control, and any differences in EtOH amount between units of the same material. If there was evidence that the within-campaign measurement variance changes over campaigns (i.e., if there are several 2

,r ijσ instead of one 2r,iσ ), Equation 1 would need to be extended with

additional parameters. The robust Brown-Forsythe Test [6] was used to check for significantly different within-campaign variances. With approximately 95 % confidence, the within-campaign variance does not differ for any material. 2.6.2 Measurement values For completely balanced data such as that of CCQM-K79, the grand average is an appropriate estimate of µi [7]:

( )rc

c r

ˆ NNRR ,i

N

j

N

kijkii

,i

∑∑ ′=′=µ

where the “^” over the symbol distinguishes a data-informed estimate from the unknowable true value and Nr is the number of replicate injections per campaign and is here 3 for all materials. 2.6.3 Variance components Assuming constant within-campaign variance, the model motivating Equation 1 can be evaluated using one-way random effects analysis of variance (one-way ANOVA). For balanced nested designs like that of CCQM-K79, one-way ANOVA partitions the mean-square variance into two components, Mw and Mb. These terms can be used to estimate the within- and between-campaign variance components

( )

<

≥−==

wb

wbrwb,cw,r

if0

ifˆ;ˆ

MM

MMNMMM ii σσ .

The i,rσ̂ so estimated have Nc,i(Nr,i - 1) degrees of freedom, vr,i, and the i,cσ̂ have Nc,i - 1 degrees of freedom, vc,i.


The results displayed in Figure 3 suggest that the i,rσ̂ are proportional to iR′ and that neither dilution nor the number of campaigns has a systematic influence on the magnitude of this proportionality. Pooling the 27 relative imprecisions, ii R′,rσ̂ , the relative within-campaign repeatability imprecision for all materials is estimated to be 0.32 %.

Figure 3: Within-Campaign Imprecision as a Function of Mean Response Each symbol represents the estimated within-campaign measurement repeatability,

i,rσ̂ , for one material plotted against its mean measurement response,

iR′ . Open circles denote the materials that were evaluated without dilution, solid circles denote the materials that were diluted before evaluation, and the red open squares mark the materials that were evaluated in three campaigns. Vertical bars represent approximate 95 % confidence intervals. The thick line denotes 0.32 % relative imprecision.

The results displayed in Figure 4 suggest that, unlike the within-campaign imprecision, the between-campaign estimates are not well described as being proportional to iR′ . While dilution does not appear to systematically contribute to the variance, the largest relative standard deviations, ii R′,rσ̂ , are for solutions having EtOH levels from 0.8 mg/g to 5 mg/g. It is plausible that sample preparation is more challenging in this range; however, not all such solutions are associated with large ii R′,rσ̂ . Rather, the large ii R′,rσ̂ are all for the nine materials evaluated in three campaigns. Since these materials were selected for evaluation in the third campaign on the basis of the relative differences between the dilution-corrected measurements in the first two campaigns, the larger relative variance could reflect between-unit heterogeneity in EtOH amount or material-specific measurement artifacts. While a single variance function does not appear appropriate for characterizing the between-campaign measurement process variance, the ii R′,rσ̂ for the study materials evaluated in three campaigns can be used to estimate an approximate 95 % confidence upper bound on the between-campaign imprecision. Pooling the nine relative variances and expanding by the appropriate χ2

95th percentile, χ20.95,18 , suggests that the plausible relative between-campaign

imprecision is not greater than 0.44 %.

0.1

1

10

100

1000

1000 10000 100000Mean Measurement Response, a.u.

With

in-C

ampa

ign

Stan

dard

Dev

iatio

n, a

.u.

UndilutedDilutedThree campaigns

i,i R′= 0032.0ˆrσ


Figure 4: Between-Campaign Intermediate Imprecision as a Function of Mean Response

Each symbol represents the estimated between-campaign measurement repeatability,

i,cσ̂ , for one material plotted against its mean measurement response,

iR′ . Open diamonds denote the materials that were evaluated without dilution, solid diamonds denote the materials that were diluted before evaluation, and the red open squares mark the materials that were evaluated in three campaigns. The thick line denotes 0.44 % relative imprecision. Vertical bars on the symbols with

i,cσ̂ greater than 1 a.u. represent approximate 95 % confidence intervals. Symbols plotted without vertical bars just above the horizontal axis represent the materials that were estimated to have zero between-campaign variance.

2.7 Large-Sample Standard Uncertainties, ( )iRu ′∞

To yield representative results, the data analysis approach described in the next section requires that the uncertainties on the assigned values and repeatability measurements be qualitatively similar. As discussed in [5], the one-way ANOVA variance components can be used to estimate the standard combined uncertainty for each material, ( )iRu ′ . However, ( )ii RuR ′±′ 2 may not consistently provide the same coverage as is nominally provided by Vi±U95(Vi) especially when the ( )iRu ′ are estimated from relatively few measurements and the measurements for different materials are influenced by different sources of variance. Using the long-term frequency (frequentist) approach recommended in JCGM 100:2008 (GUM) [8], a ”large-sample” standard uncertainty can be estimated that does provide consistent coverage: first estimate a 95 % expanded uncertainty using Student’s t coverage factors

( ) ( )ivi RutRUi

′=′ ,05.095

where vi is the number of degrees of freedom associated with the ( )iRu ′ and then divide that expanded uncertainty by the conventional metrological large-sample coverage factor of 2 [9]

( ) ( ) 295 ii RURu ′=′∞ . [2]

Unfortunately, determining vi can be problematic [5]. For the CCQM-K79 materials, vi = 3Nc-1 when there is no significant between-campaign variance and Nc-1 when there is. The vi for different materials thus could range from 8 (t0.05,8 ≈ 2.31) to 2 (t0.05,2 ≈ 4.30) or from 5 (t0.05,5 ≈ 2.57) to 1 (t0.05,1 ≈ 12.7) depending on one’s assessment of “significance.”

0.1

1

10

100

1000

1000 10000 100000Mean Measurement Response, a.u.

Bet

wee

n-C

ampa

ign

Stan

dard

Dev

iatio

n, a

.u.

UndilutedDilutedThree campaigns

i,i R′= 0044.0ˆcσ


There is no established method for increasing the vi by incorporating prior knowledge about the CRM value-assigning process into these frequentist estimates. We therefore chose to estimate the ( )iRU ′95 with a Bayesian approach that yields a probability interval interpretable as an uncertainty interval as defined in JCGM 101:2008 (GUM-S1) [10].

2.8 95 % Coverage Intervals, ( )iRU ′

Under the Bayesian paradigm, parameters such as the measurand value and variance components have probability distributions that quantify our knowledge about them. The estimation process starts with quantification of prior knowledge about the parameters followed by specification of the statistical model that relates the parameters to the data. The statistical model is called a likelihood function and is the same as described in Equation 1. These components are combined via Bayes Theorem to obtain posterior distributions for the parameters. These distributions update our knowledge about the parameters based on the evidence provided by the data. For the CCQM-K79 data this analysis produces a probability distribution for each of the µi which encompasses all of the information and variability present in the data but is confined by bounds based on prior knowledge. Bayesian Markov Chain Monte Carlo (MCMC) methods implemented in programs such as the WinBUGS freeware [11] make computation of coverage intervals practical and relatively simple.

Even though we have middling to good prior information about the nature of the µi, i,rσ , and

i,cσ parameters, it is more conservative to use minimally-informative priors and to the extent possible let the data determine the posterior distribution of the measurand. For the µi we used a very broad prior

( )21000,0~ Normaliµ .

Model convergence issues did force initialization of the µi to have the same order-of-magnitude values as the iR′ . However, since only the variability of the µi is of interest, these initialization values are of no practical consequence – other than enabling the MCMC evaluation. To incorporate our prior knowledge that the relative within-campaign precision both should be and is observed to be the same for all materials, we used a relatively informative prior for the within-campaign variance:

( )1.0,1.0~; rrr GammaRi,i σσσ =

The values of the shape parameters on the gamma distribution are not critical; a grid of values on the range of [0.05, 10] gave very similar estimates. (Numeric issues were encountered with parameter values below this range while using larger values yielded unrealistically large estimates). More critically, MCMC methods require a minimum of three independent estimates to estimate a variance from just the data and a non-informative prior. Since many of the materials were measured in only two campaigns, it was necessary to use a somewhat informative prior. Assuming that all of the assigned uncertainties for all of the materials include an appropriate estimate of between-unit heterogeneity, we constrain the relative i,cσ to be no larger than the relative standard uncertainty on the assigned value:


( )

∞i

i

i,i V

VuUniform µσ ,0~c .

where (as discussed for Equation 2) u∞(Vi) = U95(Vi)/2. After a suitably large number of iterations (typically 10000 to 100000, requiring a few wall-clock minutes on a modest PC), 95 % confidence intervals on the repeatability measurements are readily estimated from the endpoints of the central 95 % of the MCMC estimates

( ) ( )975.0,PERCENTILE;025.0,PERCENTILE 5.97,5.2, iiii MCMCPMCMCP ==

where “MCMCi” is the vector of estimates of µi and “PERCENTILE(.,.)” is the function “return the pth percentile of the specified list of values.” At the cost of slightly expanding the coverage interval but ensuring that the interval is symmetric about the iR′ , the expanded uncertainties are defined from the longest half-intervals:

( ) ( )iiiii RPPRRU ′−−′=′ 5.97,5.2,95 ,MAX

where “MAX(.)” is the function “return the largest value of the arguments.”

Table 5 lists the estimated ( )iRU ′95 and the percent relative large-sample standard uncertainties derived from them, ( ) ii RRu ′′∞100 . The percent relative standard uncertainties used to constrain the between-campaign variance are also listed. Appendix C lists the WinBUGS code and input data used to calculate in the calculations.

2.9 Summary values for the study materials Table 6 summarizes the assigned and repeatability measurement values for the 27 materials. The measurement values for the solutions have been transformed into measurements for the study materials by multiplying by the average dilution factor:

( ) ( ) iijiiii DiFRuRuDiFRR ′=′′′=′′ ∞∞; .

Since any uncertainty in the gravimetrically-determined dilution factors contributes to the between-campaign variance, no additional dilution factor uncertainty is required. The study materials are listed in order of increasing EtOH content and for graphical convenience have been assigned a single-character code. For archival purposes, the iR ′′ are listed to at least five significant digits and the ( )iRu ′′∞ to two significant digits when less than 10.0 a.u. and three when greater.


Table 5: Repeatability Measurement Uncertainties

NMI Material V

mg/g ( ) ,%

VVu∞

R′

a.u. ( )RU ′95

a.u. ( ) ,%R

Ru′

′∞

BAM BAM-K001 1.03133 0.048 15459.2 40.7 0.132 BAM BAM-K003 0.61117 0.048 9165.0 23.5 0.128 BAM BAM-K007 3.3983 0.048 50904.3 132 0.130

CENAM DMR-382a 162.6 0.58 23549.7 129 0.274 CENAM DMR-383a 289.8 0.52 42031.8 208 0.247

INMETRO MRC1.01 0.5094 0.67 7624.3 42.3 0.277 INMETRO MRC1.03 1.068 0.66 15987.0 86.4 0.270 INMETRO MRC1.05 4.956 0.55 74390.5 694 0.466

INTI CIN13013 0.2407 0.56 3589.7 17.4 0.242 INTI CIN13014 0.5975 0.52 8969.0 41.9 0.234 INTI CIN13015 1.445 0.66 21708.7 129 0.297 LGC ERM401e 0.798 0.38 12014.8 37.2 0.155 LGC ERM403b 2.007 0.23 30186.3 85.2 0.141 LGC ERM404e 39.6 0.38 5785.5 22.1 0.191 LGC ERM406d 333.5 0.045 47450.6 100 0.105 LGC ERM409a 0.2 1.50 3026.2 32.5 0.537 NIST SRM-2892 0.39 0.59 5830.3 30.1 0.258 NIST SRM-2897 15.54 0.52 4463.0 20.5 0.230 NIST SRM-2899 252.1 0.44 35787.8 164 0.229

NMIA TSL/426 1.2263 0.30 18403.3 55.3 0.150 NMISA ORG001-1 0.0958 1.99 1434.8 20.5 0.714 NMISA ORG001-2 3.027 0.50 45215.8 192 0.212 NMISA ORG001-3 104.3 0.63 15336.7 80.8 0.263 NMISA ORG003-2 67.19 0.50 10683.2 48.7 0.228 VNIIM 07.22.001-a 0.1006 0.25 1499.0 5.24 0.175 VNIIM 07.22.001-b 3.04 0.25 45615.0 152 0.167 VNIIM 07.22.001-c 6.01 0.25 1756.3 5.63 0.160


Table 6: Assigned and Measured Values for the Study Materials

Assigned Value, mg/g Repeatability

Measurement, a.u NMI Material Code V u∞(V) R" u∞(R") NMISA ORG001-1 A 0.0958 0.0019 1434.8 10.3 VNIIM 07.22.001-a B 0.10060 0.00025 1499.0 2.6

LGC ERM409a C 0.2000 0.0030 3026.2 16.3 INTI CIN13013 D 0.2407 0.0014 3589.7 8.7 NIST SRM-2892 E 0.3900 0.0023 5830.3 15.0

INMETRO MRC1.01 F 0.5094 0.0034 7624.3 21.2 INTI CIN13014 G 0.5975 0.0031 8969.0 21.0

BAM BAM-K003 H 0.61117 0.00030 9165.0 11.8 LGC ERM401e I 0.7980 0.0030 12015 18.6

BAM BAM-K001 J 1.03133 0.00050 15459 20.4 INMETRO MRC1.03 K 1.0680 0.0070 15987 43.2

NMIA TSL/426 L 1.2263 0.0037 18403 27.6 INTI CIN13015 M 1.4450 0.0095 21709 64.5 LGC ERM403b N 2.0070 0.0045 30186 42.6

NMISA ORG001-2 O 3.027 0.015 45216 96.0 VNIIM 07.22.001-b P 3.0400 0.0076 45615 76.0

BAM BAM-K007 Q 3.3983 0.0016 50904 66.0 INMETRO MRC1.05 R 4.956 0.027 74391 347

VNIIM 07.22.001-c S 6.010 0.015 89958 144 NIST SRM-2897 T 15.540 0.080 232255 533 LGC ERM404e U 39.60 0.15 589080 1130

NMISA ORG003-2 V 67.19 0.34 1001657 2280 NMISA ORG001-3 W 104.29 0.63 1564190 4120

CENAM DMR-382a X 162.60 0.95 2428445 6650 NIST SRM-2899 Y 252.1 1.1 3775971 8650

CENAM DMR-383a Z 289.8 1.5 4357857 10800 LGC ERM406d α 333.50 0.15 4998921 5270


3. STEP 3: DEFINE A CONSENSUS MODEL

3.1 Key Comparison Reference Function, KCRF Since all of the measured solutions had EtOH content within the established linear range of the GC-FID method, a linear relationship is expected between the assigned and measured values. Figure 5 provides an overview of this relationship for the CCQM-K79 data, with the iR ′′ plotted as a function of the Vi relative to a red diagonal line that represents the expected relationship if the analytical method was unbiased and the purity of the calibrant EtOH was accurately determined:

( )( ) ( ) iii VVR 5.149809987.015000 ==′′

The closeness of the values to the line confirms that the observed relationship is linear.

Figure 5: Overview of the Relationship Between Assigned and Measured Values

Each symbol (the letters A to Z plus α) denotes the assigned, V, and repeatability measurement, R", values for one of the 27 materials; see Table 6 for the association between the code and the material. The uncertainties on the V and R" values are not visible at this graphical scale.

However, the line shown in Figure 5 may not be the best estimate of the relationship between the values. The consensus linear relationship between the values can be modeled as:

ΕVR ++=′′ βα [3]

where here α is the intercept, β is the slope, and Ε is the residual random error. In analogy to the “Key Comparison Reference Value (KCRV)” used with single-material comparisons, we term

CRM, mg/g

Scal

ed G

C-F

ID R

espo

nse,

a.u

.

0.1 1 10 100

1000

010

0000

1000

000

AB

CDE

FGHI JKLM

NOPQ

RS

T

UV

WX

YZα


this function the “Key Comparison Reference Function (KCRF)” for CCQM-K79. The issue is then how to appropriately parameterize this equation.

3.2 Generalized Distance Regression analysis Routine least squares regression is not appropriate for parameterizing Equation 3 since both the assigned values and the measurement results are associated with non-negligible uncertainty. We instead estimate the parameters by minimizing [5]:

( ) ( ) iii

ii

i

iii

N

ii VR

RuRR

VuVVm

ˆˆˆˆ;ˆˆ

;22

2

1

2 βαεε +=′′

′′′′−′′

+

−==Ε

∞∞=∑ [4]

where i indexes the materials, Nm is the number of materials, and iV̂ , iR̂ , α̂ , and β̂ are predicted estimates for the parameters. This is a well-established computational approach known by many names but that we term “Generalized Distance Regression (GDR)”. The uncertainty-scaled residuals, εi, have units of large-sample standard uncertainty and represent the “standard deviation” distance between the observed and predicted values. Since the

2iε are defined from two independent variables, they are χ2 distributed with two degrees of

freedom. The value that corresponds to 95th percentile for such distributions is about 5.99, thus the 95 % critical value for the εi is √5.99 = 2.45 rather than the √3.84 = 1.96 ≈ 2 appropriate for univariate systems that are χ2 distributed with one degree of freedom. GDR has been implemented in several accessible analysis system [5]. The GDR results in this report were obtained using the RegViz system developed at NIST [12] and validated against results provided by the web-accessible freeware FREML [13] and XLgenline [14] systems. While differing in many details, these systems yield the same parameter estimates for linear models when given the same data. The RegViz system incorporates a parametric bootstrap Monte Carlo (PBMC) technique that facilitates estimation of the variability for all quantities estimated in the GDR. The PBMC repeatedly replaces the entire set of ( ) ( ){ }iiii RuRVuV ′′±′′± ∞∞ , pairs used in the estimation process with pseudo-values randomly drawn from ( )( )ii VuV ∞,N and ( )( )ii RuR ′′′′ ∞,N distributions [15,16]. The parameters and associated quantities are stored and, once a suitably large number have been generated, approximate 95 % expanded uncertainty intervals, ±U95(.), are estimated from the empirical distributions. Since only the central 95 % of the distributions are of interest, relatively few (typically 1000) pseudo-sets are required for stable estimates. Figure 6 provides a high-resolution display of the CCQM-K79 data as a series of “thumbnail” scatterplots. Each thumbnail displays the GDR result for one of the study materials with the assigned-values plotted along the horizontal axis and the repeatability measurements along the vertical. The thumbnails are arranged in order of increasing assigned value. The red diagonal line represents the consensus GDR solution when all 27 materials are included in the GDR model. The green lines that bound the consensus solution represent the approximate asymptotic 95 % level of confidence region on that solution. The one-letter code for each material inside its ellipse marks the {Vi, Ri}. Each black ellipse encloses the central 95 % of the density for the bivariate normal distribution, ( ) ( )( )iiii RuRVuV ′′′′ ∞∞ ,,,N .


Figure 6: High-Resolution Scattergrams

Overlap of an ellipse with the consensus line indicates with about 95 % confidence that the

( ) ( ){ }iiii RuRVuV ′′±′′± ∞∞ , are consistent with the consensus estimate while ellipses that do not at least penetrate into the 95 % confidence interval indicate that the ( ) ( ){ }iiii RuRVuV ′′±′′± ∞∞ , are not fully consistent. The ellipses for all of the CCQM-K79 materials include the consensus GDR solution.

3.3 Identifying strongly influential materials As discussed in [5], the GDR solution can be strongly influenced by materials having small u∞(Vi) and/or u∞(Ri) such as 07.22.001-a (B), BAM-K003 (H), BAM-K001 (J), ERM403b (N), BAM-K007 (Q), 07.22.001-c (S), and ERM406d (α). However, the degree of influence depends not only on the magnitudes of the uncertainties but also on the material’s location and the distribution and uncertainty magnitudes of the other materials.

ORG001-1 07.22.001-a ERM409a CIN13013 SRM-2892 MRC1.01

CIN13014 BAM-K003 ERM401e BAM-K001 MRC1.03 TSL/426

CIN13015 ERM403b ORG001-2 07.22.001-b BAM-K007 MRC1.05

07.22.001-c SRM-2897 ERM404e ORG003-2 ORG001-3 DMR-382a

SRM-2899 DMR-383a ERM406d

A B C D E F

G H I J K L

M N O P Q R

S T U V W X

Y Z α

These thumbnails are scaled to all have the same relative uncertainty along both the assigned-value (horizontal) and repeatability measurement (vertical) axes. The scale is set by the largest relative uncertainty for either variable across the entire suite of materials.


Leave-one-out (LOO) validation [17] enables identifying which, if any, materials are sufficiently influential to distort the consensus estimation of the KCRF. A LOO analysis proceeds by excluding each material in turn from its own evaluation. For the CCQM-K79 materials, this involves 28 GDR analyses during each PBMC iteration: one solution with all 27 materials included in the model and 27 solutions each with one of the materials excluded.

Figure 7 compares the “exact” εi (that is, the results for the GDR solution of Equation 4 when all materials are included) with their LOO analogues. Results inside the red lines indicate materials that are consistent with the consensus GDR solution whether or not they are included in the GDR model. Results far from the diagonal line indicate materials whose presence in the GDR model strongly influences the consensus solution. Only 07.22.001-a (B) is strongly influential: it is consistent with the GDR solution when it is included but is not consistent when it is excluded.

Figure 7: Identification of Strongly Influential Materials

The labeled circles mark the medians of the εi estimated from the “exact” and LOO PBMC. The error bars span the central 95 % of the distributions. The diagonal line represents equality between the two estimates. The red lines enclose the 95 % level of confidence critical distance of 2.45 expected for materials consistent with the consensus GDR solution.

3.4 Identifying strongly consequential materials While the presence of a material in the GDR model may strongly influence the consensus solution, that influence may or may not have consequence for the other materials in the study. Figure 8 visualizes the consequences of including each CCQM-K79 material in the GDR model. Inclusion of a material in the GDR model has a negative consequence when its presence causes the εi of one or more other materials in a given PBMC iteration to change from being less than the critical distance of 2.45 to being greater than that value. Likewise, inclusion of the material has a positive consequence when its presence causes the εi of one or more other materials to change from being greater than 2.45 to being less than that value. While any significant distortion of the model is undesirable, a negative consequence for someone else’s material would damage the credibility of the study more than a negative consequence for

A

B

CDEFGH I

JKLM

NOPQRST

U

VWXYZα

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7"Exact" Uncertainty-Weighted Distance, ε i

LOO

Unc

erta

inty

-Wei

ghte

d D

ista

nce,

ε i


another of one’s own materials. Similarly, a positive consequence for another of one’s own materials would be more damaging then a positive consequence for some other participant’s material.

Figure 8: Identification of Strongly Consequential Materials

The labeled circles represent the negative and positive consequences estimated from 1000 PBMC iterations. The 95 % error bars for most of these estimates are covered by the circles. The red lines enclose materials whose presence in the GDR model are not strongly consequential for any other material.

The presence of 07.22.001-a in the GDR model, or of any other of the CQM-K79 materials, is not strongly consequential for any other material. As can be visualized in Figure 6, while the small relative uncertainty on the assigned value of the 07.22.001-a material “attracts” the GDR line, the much larger relative uncertainties on the assigned values for its near neighbors, ORG001-1 (A) and ERM409a (C), render them insensitive to the resulting small shifts in the GDR solution intercept.

3.5 KCRF parameters and predictions In addition to identifying materials that could distort the consensus GDR solution, LOO-PBMC enables a more robust estimate of the variability of the GDR parameters. The LOO estimates are influenced by biases (systematic differences in the GDR solutions with-and-without each material in the models) that are not present when all materials are included (“Leave-All-In” or “LAI”) in the model. Thus the LOO-PBMC parameter uncertainties are constrained to be somewhat larger than those determined with LAI-PBMC analysis – and regression theory. Table 7 lists the GDR consensus solution parameters for the model with all 27 materials and for the model with 07.22.001-a excluded. Excluding 07.22.001-a slightly increases the estimated value for the intercept, α̂ , and decreases that of the slope, β̂ . However, the values for both parameters are equivalent within their uncertainties. Both the modest increase in the LOO-based estimate of the standard uncertainty of the intercept, ( )α̂u , and the rather larger increase when

A

B

CDEFG

H

IJ

KLM NOPQ

RST UVWXYZ

α

0

10

20

30

40

50

0 10 20 30 40 50Negative Consequences, %

Posi

tive

Con

sequ

ence

s, %


07.22.001-a is always excluded reflect the strong attractive influence of this material on the intercept: when this material is included in the model, perturbing the values of all the other materials has much less effect on the GDR solution than when it is included.

Table 7: GDR Model Parameter Estimates

)ˆ(αu a )ˆ(βu b c)ˆ,ˆ( βαρ Data Set α̂ a LAId LOOe β̂ b LAId LOOe LAId LOOe All 27 -7.1 4.3 5.2 14992.3 9.0 9.4 -0.497 -0.504

Exclude 07.22.001-a 4.3 11.5 11.8 14984.5 11.1 11.4 -0.695 -0.694

a The intercept and its uncertainty estimates are expressed in arbitrary units. b The slope and its uncertainty estimates are expressed in arbitrary units per mg/g. c Correlation between the intercept and slope estimates. d Standard deviation of 1000 Leave-All-In PBMC parameter estimates where all eligible materials were included

in model. e Standard deviation of 27 (26 for the “Exclude 07.22.001-a” model) sets of 1000 Leave-One-Out PBMC

parameter estimates, where one eligible material is in turn left out of the model in each set.

Given the equivalence of the GDR parameters and the lack of significant consequence to other materials from the inclusion of 07.22.001-a, there is no reason not to estimate the KCRF using all 27 CCQM-K79 materials. The slightly larger LOO-based asymptotic standard uncertainty estimates on the GDR parameters provide more conservative coverage than do the LAI. The KCRF is thus estimated as:

( ) ( )VR ˆ4.93.149922.51.7ˆ ±+±−=′′ Table 8 lists estimates for the assigned values and repeatability measurements for this KCRF, along with their LOO-estimated asymptotic standard uncertainties.


Table 8: KCRF Predicted Values and Responses

Assigned Value, mg/g Repeatability

Measurement, a.u

NMI Material Code iV̂ ( )iVu ˆ iR̂ ( )iRu ˆ NMISA ORG001-1 A 0.09613 0.00071 1434.1 9.8 VNIIM 07.22.001-a B 0.10051 0.00026 1499.7 2.7

LGC ERM409a C 0.2021 0.0010 3022 15 INTI CIN13013 D 0.24003 0.00060 3591.4 8.2 NIST SRM-2892 E 0.38946 0.00097 5832 14

INMETRO MRC1.01 F 0.5091 0.0013 7625 19 INTI CIN13014 G 0.5985 0.0013 8966 19

BAM BAM-K003 H 0.61125 0.00028 9156.9 6.3 LGC ERM401e I 0.8013 0.0012 12006 17

BAM BAM-K001 J 1.03136 0.00048 15455.4 9.9 INMETRO MRC1.03 K 1.067 0.0026 15990 38

NMIA TSL/426 L 1.2276 0.0017 18398 24 INTI CIN13015 M 1.4479 0.0039 21700 58 LGC ERM403b N 2.0119 0.0025 30156 36

NMISA ORG001-2 O 3.0181 0.0059 45240 87 VNIIM 07.22.001-b P 3.0421 0.0045 45601 66

BAM BAM-K007 Q 3.398 0.0014 50937 33 INMETRO MRC1.05 R 4.96 0.017 74350 260

VNIIM 07.22.001-c S 6.0035 0.0085 90000 120 NIST SRM-2897 T 15.5 0.033 232370 480 LGC ERM404e U 39.355 0.071 590000 1000

NMISA ORG003-2 V 66.88 0.14 1002600 2000 NMISA ORG001-3 W 104.33 0.27 1564100 3900

CENAM DMR-382a X 162.09 0.40 2430100 5900 NIST SRM-2899 Y 251.91 0.51 3776700 7500

CENAM DMR-383a Z 290.51 0.66 4355400 9700 LGC ERM406d α 333.49 0.14 4999800 3300


4. STEP 4: DEGREES OF EQUIVALENCE

4.1 Degrees of Equivalence for Materials An appropriate definition for the degrees of equivalence for materials in studies such as CCQM-K79 is the percent relative signed orthogonal distance [5]:

( ) ( ) ( )( )( )( ) 2ˆˆ

ˆˆˆˆSIGN100%

22

βα

β

−′′+

′′−′′+−×−×=

ii

iiiiiii RV

RRVVVVd

where the α̂ and β̂ are the KCRF parameters of Equation 4 and are used to transform the repeatability measurement-related terms to have the same scale as the assigned values. The function SIGN(.) returns the sign (±1) of its argument and defines whether the observed {Vi, Ri} pair is “above” or “below” the KCRF. The di±U95(%di) can be estimated from the empirical distribution of the %di values calculated for each set of PBMC pseudo-values [5], using the LOO analysis to make the uncertainty estimates robust to each material’s “self-referential” influence. For simplicity and to ensure symmetric coverage intervals, we summarize the empirical distribution with the mean and standard deviation of the pseudo-values. Figure 8 displays the %di±U95(%di) in dot-and-bar format, with the horizontal axis used to display the Vi of each material. The red horizontal line denotes zero bias relative to the KCRF; materials with bars that cross this line have assigned values and uncertainties that are consistent with the consensus KCRF with about a 95 % level of confidence.

Figure 9: Degrees of Equivalence for Materials

43

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-2-3

-4

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Ethanol Mass Fraction, mg/g

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es o

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4.2 Degrees of Equivalence for Participants All but one of the participants in CCQM-K79 contributed more than one material to the study. For these participants, results for all of the materials that they contributed must be combined to provide the desired goal of the KC: the expected degrees of equivalence of the participating NMIs, %D. The %D for each participant can also be estimated from the PBMC pseudo-values as the mean and standard deviation of the pseudo-values for all of the materials contributed by that participant combined and treated as a single distribution [5]. Table 9 lists the %D±U95(%D) and the component %di±U95(%di) for each participant.

Table 9: Degrees of Equivalence %D, percent %di, percent

NMI Value u∞ U95 Material Code Value u∞ U95 Va %u(V)b BAM -0.02 0.16 0.31 BAM-K003 H -0.12 0.13 0.26 0.61 0.046

BAM-K001 J -0.02 0.13 0.27 1.0 0.047 BAM-K007 Q 0.08 0.13 0.26 3.4 0.044

CENAM 0.02 0.59 1.18 DMR-382a X 0.29 0.55 1.11 160 0.26 DMR-383a Z -0.24 0.49 0.98 290 0.23

INMETRO 0.02 0.59 1.19 MRC1.01 F 0.07 0.65 1.29 0.51 0.26 MRC1.03 K 0.08 0.60 1.21 1.1 0.26 MRC1.05 R -0.08 0.51 1.02 5.0 0.36

INTI -0.05 0.61 1.21 CIN13013 D 0.29 0.56 1.12 0.24 0.24 CIN13014 G -0.20 0.49 0.99 0.60 0.22 CIN13015 M -0.23 0.61 1.22 1.4 0.28

LGC -0.22 0.88 1.76 ERM409a C -1.07 1.41 2.83 0.20 0.52 ERM401e I -0.43 0.37 0.73 0.80 0.15 ERM403b N -0.27 0.20 0.41 2.0 0.13 ERM404e U 0.63 0.36 0.72 39 0.18 ERM406d α 0.03 0.12 0.24 330 0.042

NIST 0.17 0.49 0.98 SRM-2892 E 0.15 0.56 1.13 0.39 0.24 SRM-2897 T 0.27 0.48 0.96 16 0.21 SRM-2899 Y 0.09 0.40 0.80 250 0.20

NMIA -0.10 0.30 0.60 TSL/426 L -0.10 0.30 0.60 1.2 0.15 NMISA 0.12 1.07 2.15 ORG001-1 A -0.24 1.89 3.77 0.10 0.72

ORG001-2 O 0.31 0.47 0.94 3.0 0.20 ORG003-2 V 0.46 0.48 0.95 67 0.21 ORG001-3 W -0.05 0.54 1.08 100 0.25

VNIIM 0.24 0.48 0.96 07.22.001-a B 0.66 0.54 1.08 0.10 0.47 07.22.001-b P -0.07 0.23 0.47 3.0 0.14 07.22.001-c S 0.13 0.23 0.46 6.0 0.15

a Assigned value, rounded to two significant digits b Percent relative standard uncertainty on the assigned value, 100(U95(V)/2)/V, rounded to two significant digits


Figure 10 displays these summary values, again using the dot-and-bar format. The thick black bars and solid dots represent the %D±U95(%D, with the participants are arranged in alphabetical order. The thin blue lines and open dots to the right of the black bat represent the %di±U95(%di) of that participant’s materials.

Figure 10: Degrees of Equivalence for Participants

43

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-2-3

-4

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IIM

NM

ISA

NM

IA

NIS

T

LGC

INTI

INM

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5. RECOMMENDATIONS FOR USE OF CCQM-K79 IN EVALUATING CIPM MRA CALIBRATION AND MEASUREMENT CAPABILITY (CMC) CLAIMS

5.1 How Far Does the Light Shine? CCQM-K79 demonstrated that the participating NMIs have the capability to successfully assign EtOH values in aqueous media from 0.2 mg/g to more than 300 mg/g with a 95 % level of confidence relative uncertainty of ±1 % or less, regardless of the method of certification or whether Hg(II)Cl2 or up to 20 % glucose was added to the media. EtOH values between 0.01 mg/g and 0.2 mg/g can be assigned with a 95 % level of confidence relative uncertainty of ±2 % or less. The combined range covers most forensic and non-spirit beverage usage. The study results may be extended to the value-assignment of aqueous solutions of other analytes having volatility, reactivity, and stability properties similar to those of EtOH.

5.2 Demonstrated Competencies Table 10 documents the “Core Competencies” that each of the participating NMIs assert that they demonstrated by participation in CCQM-K79. The scope and nature of these measurement process competencies include competencies embedded in CRM and PT material value assignment and the delivery of these materials to customers.


Table 10a: Core Competencies Demonstrated in CCQM-K79 by BAM

Scope

Analyte Organic liquids with moderate to low polarity, relatively stable, moderately volatile, water soluble

Matrix Deionized water with up to 0.1g/kg Hg2Cl2 and/or up to 20 % glucose

Mass fraction 0.1 mg/g to 300 mg/g DemonstratedU95 capability 0.5 %

Calibration Competencies Competency Specifications / Comments

Identity verification GC-FID (retention time) Purity assessment GC-FID, Karl-Fischer titration

Calibrant preparation Gravimetric preparation of aqueous ethanol/n-propanol (internal standard) solutions

Sample Analysis Competencies Competency Specifications / Comments

Measurand identification GC-FID (retention time) Extraction Not applicable Cleanup Not applicable Transformation Not applicable

Method validation CCQM-K27 and in-house validation using gravimetrically prepared control samples and CRMs

Use of analytical techniques Gravimetry (dilution of samples), GC-FID

Quantification mode(s), data analysis, and uncertainty evaluation

Linear calibration curve and/or bracketing using peak area ratio ethanol/propanol vs. mass ratio ethanol/propanol; uncertainties combine purity of ethanol, uncertainties of weighing, and method precision

Material Delivery Competencies Competency Specifications / Comments

Homogeneity assessment Appropriate to uncertainty claimed in Scope Stability assessment Appropriate to uncertainty claimed in Scope Packaging Suitable for stability claimed in Scope Shipping Not applicable (measurement laboratory)


Table 10b: Core Competencies Demonstrated in CCQM-K79 by CENAM

Scope

Analyte

Organic liquids with moderate to low polarity, relatively stable, moderately volatile, water soluble, organic liquids with volatility, reactivity, and stability properties similar to those of ethanol in aqueous solutions like: methanol, ethyl acetate, 1-propanol, 1-butanol, isobutanol, isoamyl alcohol

Matrix Aqueous solutions

Mass fraction 100 mg/g to 300 mg/g DemonstratedU95 capability 1 to 2 %


Identity verification GC-FID (retention time) and GC/MS Purity assessment GC-FID, Karl-Fischer titration



Measurand identification GC/MS and GC-FID (retention time) Extraction Not Applicable Cleanup Not Applicable Transformation Not Applicable

Method validation In-house validation with control materials; comparison to gravimetry from preparation

Use of analytical techniques GC-FID


Calibration curve with 1-propanol as internal standard. Linear calibration curve using peak area ratio ethanol/1-propanol vs. mass ratio ethanol/1-propanol. Uncertainties of gravimetric preparation of CRM and ethanol purity, calibration curve variation, gravimetric preparation of calibration solution, repeatability of measurement, material heterogeneity.


Homogeneity assessment Appropriate to uncertainty claimed in Scope Stability assessment Appropriate to uncertainty claimed in Scope Packaging Suitable for minimum of 2 years Shipping Suitable for international delivery


Table 10c: Core Competencies Demonstrated in CCQM-K79 by INMETRO

Scope

Analyte

Organic liquids with volatility, reactivity, and stability properties similar to those of EtOH in aqueous solutions like: methanol, ethyl acetate, 1-propanol, isopropanol, 1-butanol, 2-butanol, isobutanol, isoamyl alcohol, ethyl carbamate and others.

Matrix Aqueous (Water, Wastewater, Alcoholic and non-alcoholic beverages, Urine, Serum and other water solutions)

Mass fraction 0.1 mg/g to 10 mg/g DemonstratedU95 capability 1 to 2 % of the certified values


Identity verification GC-FID (retention time) Purity assessment GC-FID, Karl-Fischer titration




Method validation CCQM-K27.2 and in-house validation using gravimetrically prepared control samples and CRMs; comparison to gravimetry from preparation.



Linear calibration curve using peak area ratio ethanol/propanol vs. mass ratio ethanol/propanol; uncertainties combine purity of ethanol, uncertainties of weighing, method repeatability, evaporation loss, material heterogeneity and material stability.


Homogeneity assessment Appropriate to uncertainty claimed in Scope Stability assessment Appropriate to uncertainty claimed in Scope Packaging Suitable for minimum of 1 year Shipping Suitable for international delivery


Table 10d: Core Competencies Demonstrated in CCQM-K79 by: INTI

Scope


Matrix Deionized water

Mass fraction 0.2 mg/g to 2 mg/g DemonstratedU95 capability 1 %


Identity verification GC-FID (retention time) Purity assessment GC-MS, Karl-Fischer titration

Calibrant preparation Gravimetric preparation of aqueous ethanol/methyl ethyl ketone (internal standard) solutions



Method validation In-house validation using gravimetrically prepared control materials and CRMs.



Linear calibration curve and/or bracketing using peak area ratio ethanol/ methyl ethyl ketone vs. mass ratio ethanol/ methyl ethyl ketone uncertainties combine weighing, method precision, material heterogeneity, between method bias (gravimetry vs GC-FID), and purities of starting materials


Homogeneity assessment Appropriate to uncertainty claimed in Scope Stability assessment Appropriate to uncertainty claimed in Scope Packaging Suitable for stability claimed in Scope Shipping Suitable for national/international delivery


Table 10e: Core Competencies Demonstrated in CCQM-K79 by: LGC Using Two Methods: a) Titration and b) Pycnometry

Scope

Analyte a) Alcohol (in a gravimetrically prepared solution of a defined alcohol of

known purity) b) Alcohol by volume via densitometry

Matrix a) purified water containing 0.1g/L mercury(II)chloride b) aqueous alcoholic solutions

Mass fraction a) 0.2 mg/g to 2 mg/g b) 30 mg/g to 400 mg/g

DemonstratedU95 capability

a) 0.4 % to 3 % b) 0.2 % to 1 %


Identity verification GC-FID, Density (for ethanol purity) Purity assessment GC-FID; Density, Karl- Fischer titration (for ethanol purity)

Calibrant preparation a) Gravimetric preparation of aqueous dichromate b) Volume and mass measurements of high purity water at a

specified temperature.


Measurand identification a) via purity determination performed in CRM production b) Not applicable

Extraction Not applicable Cleanup Not applicable

Transformation a) Chemical oxidation with potassium dichromate to acetic acid b) Not applicable

Method validation Comparison of method results with GC-C-IDMS method used in CCQM-K27. Comparison with gravimetric preparation where appropriate.

Use of analytical techniques Gravimetry, Titration, densitometry


a) Calculation of ethanol concentration via back titration. Uncertainty combined purity of reagents, method precision, homogeneity and stability.

b) Calculation of ethanol concentration and %ABV via the use of internationally recognized density tables.



Homogeneity assessment Predetermined number of samples taken randomly from CRM batch to assess homogeneity within the claimed uncertainty. (in accordance with Guide 34 and 35)

Stability assessment Stability assessed throughout lifetime of material (in accordance with Guide 34 and 35)

Packaging Suitable for international delivery and stability for life time of CRM

Shipping Suitable for international delivery


Table 10f: Core Competencies Demonstrated in CCQM-K79 by: NIST

Scope

Analyte Organic liquids with moderate to low polarity, relatively stable, and moderately volatile

Matrix Aqueous

Mass fraction 0.3 mg/g to 300 mg/g DemonstratedU95 capability 1 % of the certified values


Identity verification GC/MS Purity assessment GC-FID, Karl-Fischer titration, and DSC

Calibrant preparation Gravimetric preparation from ethanol of determined purity and water tested prior to use for presence of any interferences with internal standard or ethanol


Measurand identification GC/MS Extraction Not Applicable Cleanup Not Applicable Transformation Not Applicable

Method validation Control materials (SRMs); comparison to gravimetry from preparation

Use of analytical techniques GC-FID


Response factor calculations using multiple calibration solutions prepared to mimic the solution of interest; uncertainties combine instrumental imprecision, material heterogeneity, between method bias (gravimetry vs GC-FID), and purities of starting materials


Homogeneity assessment Appropriate to uncertainty claimed in Scope Stability assessment Appropriate to uncertainty claimed in Scope Packaging Suitable for minimum of 26 years Shipping Suitable for international delivery


Table 10g: Core Competencies Demonstrated in CCQM-K79 by: NMIA

Scope

Analyte Organic liquids with moderate to low polarity, relatively stable, and moderately volatile, water soluble

Matrix Aqueous solutions containing up to 0.1 g/kg Hg2Cl2

Mass fraction 0.5-5 mg/g DemonstratedU95 capability 0.5 % of the certified values


Identity verification GC/MS, Karl-Fisher titration

Purity assessment Gravimetric preparation using ethanol of assigned purity after testing for impurities, water and interferences.

Calibrant preparation GC/MS, Karl-Fisher titration


Measurand identification GC/MS Extraction Not applicable Cleanup Not applicable Transformation Not applicable

Method validation CCQM K27, Analysis of certified control materials (CRMs), precision, agreement of results for multiple characteristic ions. Agreement with gravimetric preparation.

Use of analytical techniques Gravimetry (dilution of samples), GCMS


Bracketed exact matching double IDMS using quadrupole GC/MS with a 13C2-labelled ethanol internal standard. Uncertainty budget included contributions from: calibration solution mass fraction, blend preparation masses, observed isotope amount ratio in blends, potential interferences/bias (different ion pairs), homogeneity and stability data.


Homogeneity assessment Appropriate to uncertainty claimed in scope Stability assessment Appropriate to uncertainty claimed in scope Packaging Suitable for stability claimed in scope Shipping Suitable for international delivery


Table 10h: Core Competencies Demonstrated in CCQM-K79 by: NMISA

Scope


Matrix Aqueous ethanol solutions containing up to 20 % glucose or up to 0.1g/kg Hg2Cl2

Mass fraction 0.1 to 105 mg/ g DemonstratedU95 capability 1 % to 4 % relative


Identity verification GC-FID (retention time) Purity assessment GC-FID, Karl-Fischer titration, dichromate titration

Calibrant preparation Gravimetric preparation of aqueous ethanol/t-butanol (internal standard) solutions


Measurand identification GC-FID (retention time), dichromate titration (chemical reaction equation)

Extraction None Cleanup None Transformation Oxidation of ethanol to acetic acid

Method validation In-house validation with CRMs, comparison to gravimetry from preparation, CCQM-K27.1 and CCQM-K27.2, CCQM-K79

Use of analytical techniques Gravimetry, GC-FID, oxidation of analyte, dichromate titration


Titrimetric primary method, GC bracketing quantification using t-butanol internal standard, uncertainties: combine purity of ethanol, uncertainties of weighing, method repeatability, between method bias (gravimetry vs. titrimetry), material heterogeneity and material stability.


Homogeneity assessment Predetermined number of samples taken from beginning, middle and end of CRM batch to assess homogeneity within the claimed uncertainty (by dichromate titration)

Stability assessment Stability assessed throughout lifetime at 10 monthly intervals by dichromate titration or GC-FID

Packaging Suitable for international delivery and stability for life time of CRM

Shipping Suitable for international delivery


Table 10i: Core Competencies Demonstrated in CCQM-K79 by: VNIIM

Scope

Analyte Organic liquids with moderate to low polarity, relatively stable, and moderately volatile, water soluble

Matrix Aqueous solutions

Mass fraction 0.10 mg/g to 6.0 mg/g DemonstratedU95 capability 0.5 % of the certified values


Identity verification GC-FID Purity assessment GC-FID, densitometer, Karl Fisher titration

Calibrant preparation Gravimetric preparation of aqueous solutions of ethanol/n-butanol (n-butanol as internal standard)


Measurand identification GC-FID Extraction Not Applicable Cleanup Not Applicable Transformation Not Applicable

Method validation CCQM-K27s; in-house validation using gravimetrically prepared control solutions

Use of analytical techniques Gravimetry, GC-FID


Linear calibration curve using peak area ratio ethanol/n-butanol against mass their ratio. Combined standard uncertainty comprises standard uncertainty from purity assessment, standard uncertainty from gravimetry, method repeatability


Homogeneity assessment According to Guide 35 Stability assessment According to Guide 35 Packaging Suitable for minimum 1 year Shipping Suitable for international delivery


6. REFERENCES

1 CIPM. Mutual recognition of national measurement standards and of calibration and measurement certificates issued by national metrology institutes. Comité international des poids et mesures. Paris, 14 October 1999. http://www.bipm.org/en/cipm-mra/documents/

2 Webb KS, Wolff Briche CSJ. CCQM-K27 (a,b) Key Comparison – Determination of Ethanol in Aqueous Matrix. 2003. http://kcdb.bipm.org/appendixB/appbresults/ccqm-k27.a/ccqm-k27.a_final_report.pdf

3 Schantz MM, Duewer DL, Parris RM, et al. CCQM-K27-Subsequent: Key Comparison (subsequent) for the Determination of Ethanol in Aqueous Matrix. 2005. http://kcdb.bipm.org/appendixB/appbresults/ccqm-k27.1/ccqm-k27.1_final_report.pdf

4 Schantz MM, Parris RM, May WE, et al. OAWG/07-08: CCQM-K27.2, Determination of Ethanol in Aqueous Media, Subsequent 2, Draft B report. 2006. http://www.bipm.org/wg/CCQM/OAWG/Restricted/April_2007/OAWG_0708.doc

5 Duewer DL, Gasca-Aragon H, Lippa KA, Toman B. Experimental design and data evaluation considerations for comparisons of reference materials. Accred Qual Assur 2012;17:567–588. DOI 10.1007/s00769-012-0920-4

6 Brown M., Forsythe AB. Robust Tests for Equality of Variance, J Amer Statist Assoc 1974;69:364-367. DOI: 10.2307/2285659

7 Searle SR, Casella G, McCulloch CE. Variance Components. John Wiley & Sons, Inc. Hoboken, NJ USA. 1992.

8 JCGM 100:2008. Guide to the Expression of Uncertainty in Measurement (GUM). BIPM, Sèvres, France. http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf

9 Ciarlini P, Cox MG, Pavese F, Regoliosi G. The use of a mixture of probability distributions in temperature interlaboratory comparisons. Metrologia 2004;41:116-121. DOI:10.1088/0026-1394/41/3/002

10 JCGM 101:2008. Evaluation of measurement data — Supplement 1 to the “Guide to the expression of uncertainty in measurement” — Propagation of distributions using a Monte Carlo method (GUM-S1). BIPM, Sèvres, France. http://www.bipm.org/utils/common/documents/jcgm/JCGM_101_2008_E.pdf.

11 The BUGS Project, WinBUGS 1.4.3, 2007. http://www.mrc-bsu.cam.ac.uk/bugs/ 12 Available on request from [email protected] 13 Analytical Methods Committee. Linear Functional Relationship Estimation by Maximum

Likelihood. http://www.rsc.org/Membership/Networking/InterestGroups/Analytical/AMC/Software/FREML.asp


http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf

14 National Physics Laboratory. XLGENLINE. http://www.eurometros.org/component_search.php?component_type=distributions

15 Bootstrapping (Statistics) http://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29 16 Duewer DL, Kowalski BR, Fasching JL. Improving the reliability of factor analysis of

chemical data by utilizing the measured analytical uncertainty. Anal Chem 1976;48:2002-2010. DOI: 10.1021/ac50007a048

17 Picard RR, Cook RD. Cross-validation of regression models. J Amer Statist Assoc 1984;79(387):575-583. DOI: 10.1080/01621459.1984.10478083. See also “Cross-validation (Statistics)” http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29#Leave-one-out_cross-validation


APPENDIX A

Call for Participants

The study proposal/project plan document that was attached to the following Call for Participants email is not included in this Appendix

From: Parris, Reenie M. Sent: Friday, October 09, 2009 10:41

To: List

Subject: CCQM OAWG Call for Participants for CCQM-K79: Comparison of Value-Assigned CRMs and Proficiency Testing Materials for Ethanol in Aqueous Matrix Dear OAWG Colleagues: On behalf of Willie E. May, CCQM OAWG Chair and the study coordinators, we are issuing the Call for Participants for CCQM-K79: Comparison of Value-Assigned CRMs and Proficiency Testing Materials for Ethanol in Aqueous Matrix.

CCQM OAWG Call for Participants for CCQM-K79: Comparison of Value-Assigned CRMs and Proficiency Testing Materials for Ethanol in Aqueous Matrix, 0.1 mg/g

to 500 mg/g All NMIs that deliver measurement services for ethanol in an aqueous matrix through one or more value-assigned CRMs or PT materials should participate in CCQM-K79. If your Institute cannot participate in K79 but has Calibration and Measurement Claims (CMCs) in the CIPM MRA Key Comparison Database for ethanol in an aqueous matrix where CRMs or PT value assignment is one of the delivery mechanisms, this may result in your CMCs for this measurand being deleted from the Database. Participation in CCQM-K79 is accomplished by providing the Measurement Laboratory with ethanol in an aqueous matrix CRM and/or PT materials that your institution has value-assigned, keeps in storage, and ships to customers. PT materials that are stored or distributed by another organization are not eligible for participation in K79. All of the comparison measurements will be made at the Measurement Laboratory under repeatability conditions. See the attached study proposal/project plan for details. Institutes that currently distribute from one to three different ethanol materials in water are asked to provide of all three materials. Institutes that distribute four or more materials in water are asked to provide three materials that are representative of their low, medium, and high ethanol level materials within the 0.1 mg/g to 500 mg/g range. Institutes that also distribute materials in any aqueous matrix other than water (e.g., sugar water, beer, wine, distillates, etc.) are asked to provide at least one and at most three material(s) of each such matrix, excluding materials that contain a significant level of n-propanol. Coordinating Laboratory: NIST Contact at the Coordinating Laboratory: [email protected]

CCQM-K79 Final Report, Appendix A A1 / 2

Measurement Laboratory: BAM Contact at the Measurement Laboratory: [email protected] CRM/PT information due to Coordinating Laboratory: ASAP CRM/PT materials due to Measurement Laboratory: 10-Nov-2009 Discussion of results April 2010 meeting. Because the analytical process to be used by the Measurement Laboratory requires a minimum of 2 mL of solution per measurement and the study design requires measurement of two independent samples of each material plus a “backup” sample in case of technical difficulties, three “analyzable units” (a combined solution volume of at least 2.4 mL) are required for each material. To participate, please: 1) E-mail [email protected], [email protected], and [email protected] stating your intent to participate and the name and contact information for your Institute. 2) E-mail [email protected] and [email protected] a copy of the CRM Certificate or PT material value-assignment reports, storage instructions, and handling information for each of the materials you believe suitable for inclusion in this study. 3) Upon notification that your materials are suitable for inclusion in CCQM-K79, ship three analyzable units (at least 2.4 mL total solution) of each material to: Dr. Rosemarie Philipp, AG "Organische Spurenanalytik" Bundesanstalt für Materialforschung und -prüfung (BAM) Richard-Willstätter-Str. 11 12489 Berlin, BRD Tel: +49-(0)30-8104-5893 Fax: +49-(0)30-8104-5990 [email protected] Please clearly indicate on the shipping label that these materials are for an interlaboratory study and provide any other information that you believe will facilitate shipping. 4) E-mail the carrier, tracking number, shipment date, and other shipping details to [email protected] as soon as possible after the materials are shipped. An acknowledgement of receipt of materials will be sent upon their arrival at the Measurement Laboratory. The study proposal/project plan is attached. If you have any comments or concerns regarding the design or proposed conduct of this KC, please contact [email protected] with a cc to [email protected]. For details concerning the gas chromatography – flame ionization detection (GC-FID) methodology used by the Measurement Laboratory, please contact [email protected]. Reenie Parris On behalf of Willie E. May, CCQM OAWG Chair

CCQM-K79 Final Report, Appendix A A2 / 2

mailto:[email protected]

APPENDIX B

WinBUGS Code for Bayesian Estimation of 95 % Confidence Intervals

### 2-level Bounded Nested ANOVA for CCQM-K79### # ******** Specified parameters ******* # n0 number of materials # n1 number units per material # n2 number injections per unit # RCRM[n0] vector of U(CRM)/(2*crm) # y[n0,n1,n2] matrix of measurements # # ******** Interesting Estimated parameters ******* # m0[n0] vector of material-response means # r1 scalar relative "injectability" imprecision # s0[n0] vector of between-unit SDs # # ******** Intermediate estimated parameters ******* # m1[n0,n1] matrix of unit-response means # sbnd[n0] vector of prior distribution on s0: RCRM*m0[n0] # t0[n0] vector of inverse between-variances: (1/s0)^2 # t1[n0] vector of inverse within-variances: (1/(r1*m0))^2 # Model{ r1 ~ dgamma(0.1,0.1) for( i0 in 1 : n0) { m0[i0] ~ dnorm(0,0.0000001) sbnd[i0] <- RCRM[i0]*m0[i0] s0[i0] ~ dunif(0.0,sbnd[i0]) t1[i0] <- 1/pow(r1*m0[i0],2) t0[i0] <- 1/pow(s0[i0],2) for( i1 in 1 : n1 ) {m1[i0,i1] ~ dnorm(m0[i0],t0[i0]) for( i2 in 1 : n2 ) {y[i0,i1,i2] ~ dnorm(m1[i0,i1],t1[i0])}}}} # K79 Data list(n0=27,n1=3,n2=3, RCRM=c( 4.80E-04,4.83E-04,4.80E-04,5.84E-03,5.18E-03,6.67E-03,6.55E-03, 5.45E-03,5.61E-03,5.19E-03,6.57E-03,3.76E-03,2.24E-03,3.79E-03, 4.50E-04,1.50E-02,5.90E-03,5.15E-03,4.36E-03,3.02E-03,2.00E-02, 5.01E-03,5.99E-03,5.00E-03,2.50E-03,2.50E-03,2.50E-03), y=structure(.Data=c( 15465,15465,15471,15506,15408,15440,NA,NA,NA, 9159,9175,9168,9164,9161,9163,NA,NA,NA, 50742,50760,51082,50912,50949,50981,NA,NA,NA, 23601,23576,23586,23431,23352,23459,23666,23644,23632, 42081,41764,41838,42002,42050,41957,42190,42240,42164, 7627,7632,7635,7667,7588,7597,NA,NA,NA, 15993,16118,16071,15965,15959,15956,15949,15938,15934, 74262,74251,74713,74393,74361,74363,NA,NA,NA,

CCQM-K79 Final Report, Appendix B B1 / 2

3610,3576,3584,3573,3602,3593,NA,NA,NA, 8999,8932,8998,8954,8959,8972,NA,NA,NA, 21807,21786,21744,21601,21600,21528,21749,21926,21637, 11993,11999,11995,12021,12033,12068,12008,11998,12018, 30400,30210,30351,30051,30094,30046,30212,30143,30170, 5777,5786,5787,5790,5787,5786,NA,NA,NA, 47290,47328,47308,47414,47412,47455,47512,47717,47619, 3016,3023,3046,3038,3025,3009,NA,NA,NA, 5833,5862,5813,5860,5807,5807,NA,NA,NA, 4450,4473,4469,4487,4463,4436,NA,NA,NA, 35732,35791,35760,35721,35737,35986,NA,NA,NA, 18399,18423,18547,18344,18342,18371,18428,18456,18320, 1426,1433,1441,1434,1430,1445,NA,NA,NA, 45351,45095,45375,45144,45171,45030,45367,45289,45120, 15343,15347,15359,15343,15327,15301,NA,NA,NA, 10698,10688,10687,10663,10687,10676,NA,NA,NA, NA,NA,NA,1494,1495,1495,1490,1515,1505, NA,NA,NA,45558,45615,45582,45681,45593,45661, NA,NA,NA,1757,1758,1755,1766,1750,1752),.Dim = c(27,3,3))) # K79 Initialization values list(m0=c( 1.5E+04,9.2E+03,5.1E+04,2.4E+04,4.2E+04,7.6E+03,1.6E+04,7.4E+04, 3.6E+03,9.0E+03,2.2E+04,1.2E+04,3.0E+04,5.8E+03,4.7E+04,3.0E+03, 5.8E+03,4.5E+03,3.6E+04,1.8E+04,1.4E+03,4.5E+04,1.5E+04,1.1E+04, 1.5E+03,4.6E+04,1.8E+03))

CCQM-K79 Final Report, Appendix B B2 / 2

final report - bipm...marco antonio Ávila calderon, alejandro pérez castorena centro nacional de...

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