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Uncertainty of measurement – “Bottom-up” vs “Top-down”

At the recently concluded Eurachem/PUC training workshop on “Accreditation of analytical, microbiological and medical laboratories – ISO/IEC 17025:2017 and ISO 15189:2012”, the following important pointers were noted during the presentation of Dr Steve Ellison of LGC UK on the to subject : Measurement Uncertainty – “Bottom-up” vs “Top-down”:

  1. Measurement uncertainty assessed in analytical chemistry is either through the use of the Law of Propagation of Uncertainty from uncertainty budgets (or inputs or contributors) as per GUM (bottom-up) method, or adopting the method performance (or validation) data (top-down);
  2. Using the GUM approach with a mathematical model, the laboratory is to assess and sufficiently quantify significant uncertainty contributors in the test procedures.  This can be done by (a) using descriptive statistical data through repeated experiments (Type A), or (b) any other means, such as certificates of analysis by a third party, theory or professional judgement (Type B);
  3. It has been stated that testing laboratories tend to underestimate measurement uncertainty using the GUM method in almost measurement fields, as one cannot comprehensively identify and quantify all important uncertainty inputs;
  4. Use of any one of the top-down approaches with the use of validation data is a better bet in the evaluation of measurement uncertainty because the actual dispersion of test results in extended experiments can be observed; the major uncertainty source data can come from (a) long term precision (intermediate reproducibility), (b) bias uncertainty based on reliable certified reference materials, and (c ) any other additional important effects which are not part of the method’s mathematical equation;
  5. By definition, uncertainty is a range which includes the true value.  Therefore, any significant bias should not be ignored.
  6. Empirical methods are operationally defined.  In the top-down approach, relevant  reference material should be used to estimate laboratory bias as an input of uncertainty.  In this case, only matrix bias is to be taken care of and method bias is not relevant.
  7. Eurachem opines that the bottom-up GUM method is appropriate for metrology laboratories, whilst the top-down approaches are best for testing laboratories.

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Basic statistics, Measurement uncertainty, Top-Down Approach

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Can we estimate uncertainty by replicates?

The method traditionally practiced by most test laboratories in the estimation of measurement uncertainty is by the ISO GUM (ISO/IEC Guide 98-3) approach, which is quite tedious and time consuming to study and gather uncertainty contributions from each and every step of the test method.  An alternative way of looking at uncertainty is to attempt to study the overall performance of the analytical procedure by involving replication of the whole procedure to give a direct estimate of the uncertainty for the final test result. This is the so-called ‘top-down’ approach.

We may use the data from inter-laboratory study, in-house validation or ongoing quality control. This approach is particularly appropriate where individual effects are poorly understood in terms of their quantitative theoretical models which are capable of predicting the behavior of analytical results for particular sample types.  By this approach, it is suffice to consider reproducibility from inter-laboratory data or long-term within-laboratory precision as recommended by ISO 21748, ISO 11352 and ASTM D 6299.

However, one must be aware of that by repeatedly analyzing a given sample over several times will not be a good estimate of the uncertainty unless the following conditions are fulfilled:

  1. There must be no perceptible bias or systematic error in the procedure.  That is to say that the difference between the expected results and the true or reference value must be negligible in relation to twice of the standard deviation with 95% confidence. This condition is usually (but not always) fulfilled in analytical chemistry.
  • The replication has to explore all of the possible variations in the execution of the method by engaging different analysts on different days using different equipment on a similar sample. If not, at least all of the variations of important magnitude are considered. Such condition may not be easily met by replication under repeatability conditions (i.e. repeated testing within laboratory), because such variations would be laboratory-specific to a great extent.

The conclusion is that replicated data by a single analyst on same equipment over a short period of time are not sufficient for uncertainty estimation. If the top-down approach is to be followed, we must obtain a good estimate of the long-term precision of the analytical method.  This can be done for example, by studying the precision for a typical test method used as a QC material over a reasonable period of time. We may also use a published reproducibility standard deviation for the method in use, provided we document proof that we are able to follow the procedure closely and competently.

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Basic statistics, Measurement uncertainty, Top-Down Approach

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Basic discussion on measurement uncertainty evaluation

MU with error

Currently many measurement uncertainty (MU) courses and workshops for test laboratories in this region are run by metrology experts instead of practicing chemists. Some laboratory analysts and quality control personnel have found the outcome after attending the two- or three-day presentations rather disillusion, leaving the classroom with their minds even more uncertain. This is because they cannot see how to apply in their routine works as there are no practical worked examples demonstrated to satisfy their needs…..  Read on  Measurement uncertainty – the very basic

 

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Basic statistics, Measurement uncertainty, Top-Down Approach

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A worked example of measurement uncertainty for a non-homogeneous population

Sampling and analysis

A worked example of MU estimation on a non-homogeneous population

For sampling a non-homogeneous target population such as grain cargo, grainy materials or soil, random positions may be selected and split duplicate samples are taken with duplicate laboratory analysis carried out on each sample received.  This approach will be able to address both sampling and analytical uncertainties at the same time…..

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Basic statistics, Measurement uncertainty, Top-Down Approach

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The concept of measurement uncertainty – a new perspective

Since the publication of the newly revised ISO/IEC 17025:2017, measurement uncertainty evaluation has expanded its coverage to include sampling uncertainty as well because ISO has recognized that sampling uncertainty can be a serious factor in the final test result obtained from a given sample ……

The concept of measurement uncertainty – a new pespective

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Basic statistics, Measurement uncertainty, Top-Down Approach

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ISO FDIS 17025:2017 – Impacts on accredited laboratories

ISO FDIS 17025:2017 – Impacts on accredited laboratories

As noted in my previous article on the final draft international standard FDIS 17025:2017 (https://consultglp.com/2017/10/30/iso-fdis-170252017-sampling-sampling-uncertainty/ ), the current ISO/IEC 17025:2005 version widely used by accredited laboratories around the world will soon be replaced by this new standard, expected to be published very soon.

The ILAC (International Laboratory Accreditation Cooperation), a formal cooperation to promote establishing an international arrangement between member accreditation bodies based on peer evaluation and mutual acceptance with a view to develop and harmonize laboratory and inspection body accreditation practices, has recommended a 3-year transition to fully implement this new standard from the date of its publication. At the end of the transition period, laboratories not accredited to the ISO/IEC 17025:2017 will not be allowed to issue endorsed test or calibration reports and will not be recognized under the ILAC MRA terms.

Today, there are over 90 member accreditation bodies from over 80 economies have signed the ILAC Mutual Recognition Arrangement (ILAC MRA). This new ISO standard therefore has a tremendous impact on all accredited calibration and testing laboratories of which their national accreditation bodies are signatory members of the ILAC MRA.

Each national accreditation body is expected to work out its own transition plan with actions to be taken to help the laboratories under its charge to smoothly migrate to the new practices. These actions might include, but not limit to, effective communication, scheduled seminars/training courses for laboratory managers and technical assessors, and mapping out a time table and policies to achieve the ultimate goal.

In the nutshell, the new standard has standardized and aligned its structure and contents with other recently revised ISO standards, and the ISO 9001:2015, in particular. It reinforces a process-based model and focuses on outcomes rather than prescriptive requirements such as the absence of familiar terms like quality manual, quality manager, etc. and giving less description on other documentation. It will allow more flexibility for laboratory operation as long as the laboratory’s technical competence can be assessed and recognized by the standard.

The following notes highlight major significant changes in the new revision as compared with those in the 2005 version:

  1. Standard format

Many requirements under the 2005 version remain unchanged but appear in different places of the document, under headlines like general requirements (Clause 4), structural requirements (Clause 5), resource requirements (Clause 6), process requirements (Clause 7) and management system requirements (Clause 8). Also, there are certain language updates to reflect the current standard practices and technologies.

  1. Laboratory activities

Under Clause 3 on Terms and Definitions, the term “laboratory activities” in sub-clause 3.6 has included “sampling, associated with subsequent testing or calibration” in addition to the existing “testing” and “calibration” activities. This is a major scope expansion of the laboratory activity for accreditation and will be a challenge for most testing laboratories which are engaged in field sampling. Sub-sampling of test sample in a laboratory prior to analysis is considered to be part of the test procedure.

  1. Risk-based thinking

The revision has incorporated a new “risk-based thinking” which requires the laboratory to plan and implement actions to address possible risks and opportunities associated with the laboratory activities. The laboratory is responsible for deciding which risks and opportunities need to be addressed. The aims are to:

a) give assurance that the management system achieves its intended results;

b) enhance opportunities to achieve the purpose and objectives of the laboratory;

c) prevent, or minimize, undesired elements

The word of ‘risk’ can be found in the following requirements:

Clause 4.1.4:   Identifying risk to impartiality

Clause 7.8.6.1:  “When a statement of conformity to a specification or standard is provided, the laboratory shall document the decision rule employed, taking into account the level of risk (such as false accept and false reject and statistical assumptions) associated with the decision rule employed and apply the decision rule.”

Clause 7.10:  Actions taken for nonconforming work based upon risk levels established by the laboratory

Clause 8.5:   Actions to address risks and opportunities

Clause 8.7:   Updated risk and opportunities when corrective action is taken

Clause 8.9:   Management review agenda to include results of risk identification

  1. Impartiality

The new standard has stressed on the laboratory’s impartiality.  Under Clause 4 General Requirements, the Sub-clause 4.1 requires the laboratory to identify risks to its impartiality on an on-going basis and if a risk to impartiality is identified, the laboratory shall be able to demonstrate how it will eliminate or minimize such risk.

  1. Decision rule

The term “decision rule” is new to this ISO standard. It first appears in Clause 3.7 under Terms and Definitions which states that “rule that describes how measurement uncertainty is accounted for when stating conformity with a specified requirement”. This is in relation to Sub-clause 7.8.6 on providing “Reporting statements of conformity”.

Before the laboratory provides any statement of conformity to a specification, it is required  to first assess the level of risk (such as false acceptance, false rejection and statistical assumptions) involved in the decision rule employed which has to be documented.  See Sub-clause 7.8.6.1.

  1. External provided products and services

The new standard combines current Sub-contracting, Supplies and External Services which affect laboratory’s activities under a new headline with requirements, controls and communication guidance given under Sub-clause 6.6

  1. Evaluation of measurement uncertainty (MU)

Clause 7.6.1 requires laboratories to identify the contributions to measurement uncertainty. When evaluating MU, all contributions which are of significance, including those arising from sampling, shall be taken into account using appropriate methods of analysis.

The standard in its Clause 7.6.3 Note 3 states that “For further information, see ISO/IEC Guide 98-3, ISO 5725 and ISO 21748”.  It is inferred that the laboratory has a choice in the MU evaluation methods, i.e. using either the conventional GUM (bottom up) or the holistic method performance (top down) approaches can be applied in the MU evaluation.

  1.   Options in management system requirements

The new standard allows the laboratory to implement a management system in accordance with Option A or Option B after meeting the requirements of Sub-clauses 4 to 7.

Option A asks the laboratory to address all its sub-clauses 8.2 to 8.9 as the minimum requirements, and Option B is for a laboratory which has already established and is maintaining a management system in accordance with the requirements of ISO 9001, and which is capable of supporting and demonstrating the consistent fulfillment of the requirement of Clauses 4 to 7, whilst fulfilling at least the intent of the management system requirements specified in Sub-clauses 8.2 to 8.9.

In conclusion, in addition to aligning with the other current international standards in its structural forms and wordings, the new version of ISO 17025 to be implemented introduces new laboratory activity scope on sampling and new requirements such as risks and opportunities, decision rule, sampling uncertainty and two management system options.

Laboratories will need to acquire new skills in carrying out risk assessment, making decision rule, evaluating sampling uncertainty and learning how to incorporate this uncertainty into the overall measurement uncertainty evaluation.

Now, accredited laboratories await for their respective national accreditation body to provide new laboratory accreditation guidelines and directives in this significant migration of ISO/IEC 17025 standards from the existing 2005 version to the latest one during this 3-year transition period.

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Basic statistics, Measurement uncertainty, Top-Down Approach

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ISO FDIS 17025:2017, sampling & sampling uncertainty

17025 Process

The international standards for accrediting laboratory’s technical competence has evolved over the past 30 over years, started from ISO Guide 25: 1982 to ISO Guide 25:1990, to ISO 17025:1999, to ISO 17025:2005 and now to the final draft international standard FDIS 17025:2017, which is due to be published before the end of this year to replace the 2005 version. We do not anticipate much changes to the contents other than any editorial amendments.

The new draft standard aims to align its structure and contents with other recently revised ISO standards, and the ISO 9001:2015 in particular. It is reinforcing a process-based model and focuses on outcomes rather than prescriptive requirements such as eliminating familiar terms like quality manual, quality manager, etc. and giving less description on other documentation. It attempts to introduce more flexibility for laboratory operation.

Although many requirements remain unchanged but appear in different places of the document, it has added some new concepts such as:

–  focusing on risk-based thinking and acting,

–  decision rule for measurement uncertainty to be accountable for when stating

conformity with a specification,

–  sampling as another laboratory activity apart from testing, and calibration, and,

–  sampling uncertainty to be a significant contributing factor for the evaluation of

measurement uncertainty.

The purpose of introducing sampling as another activity is understandable, as we know that the reliability of test results is hanged on how representative the sample drawn from the field is. The saying “The test result is no better than the sample that it is based upon” is very true indeed.

If an accredited laboratory’s routine activity is also involved in the field sampling before carrying out laboratory analysis on the sample(s) drawn, the laboratory must show evidence of a robust sampling plan to start with, and to evaluate the associated sampling uncertainty.

It is reckoned however that in the process of carrying out analysis, the laboratory has to carry out sub-sampling of the sample received and this is to be part of the SOP which must devote a section on how to sub-sample it. If the sample received is not homogeneous, a consideration of sampling uncertainty is to be taken into account.

Although FDIS states that when evaluating the measurement uncertainty (MU), all components which are of significance in the given situation shall be identified and taken into account using appropriate methods of analysis, its Clause 7.6.3 Note 2 further states that “for a particular method where the measurement uncertainty of the results has been established and verified, there is no need to evaluate measurement uncertainty for each result if it can demonstrate that the identified critical influencing factors are under control”.

To me, it means that all identified critical uncertainty influencing factors must be continually monitored. This will have a pressured work load for the laboratory concerned to keep track with many contributing components over time if the GUM method is used to evaluate its MU.

The main advantage of the top down MU evaluation approach based on holistic method performance using the daily routine quality control data, such as intermediate precision and bias estimation is also appreciated as stated in Clause 7.6.3.  Its Note 3 refers to the ISO 21748 which uses accuracy, precision and trueness as the budgets for evaluation of MU, as an information reference.

Secondly, this clause in the FDIS suggests that once you have established an uncertainty of a result by the test method, you can estimate the MU of all test results in a predefined range through the use of relative uncertainty calculation.

 

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Basic statistics, Measurement uncertainty, Top-Down Approach

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Which top down MU method is good for you?

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Which alternative top down MU method will work best for you?

There are several alternative top down methods available for the estimation of measurement uncertainty (MU) in chemical and microbiological laboratories. One or two will work best under your laboratory conditions.

All accredited laboratories would have implemented a robust laboratory quality management system in accordance to the ISO/IEC 17025 standards. In this scenario, you would have carried out certain method validation and verification on your in-house test methods and standard/official methods, respectively. You would also have consistently been running laboratory control samples (LCS’s) as and when you conduct a batch of sample analysis to monitor the accuracy of your test results. Similarly, you should also have been participating some proficiency testing (PT) programs at regular intervals as required by the national accreditation body.

With this in mind, I recommend the following top down approaches which will be suitable for your MU evaluation:

  1. For established standard test methods (like ISO, AOCS, BS, EN, ASTM, etc.) that you have been running routinely with readily available QC data, the use of data repeatability, reproducibility and trueness estimates will be fine for estimating the MU, such as following the ISO 21748:2010;
  2. If you have been using stable laboratory control samples (LCS’s) to monitor your test accuracy regularly, you can consider plotting the LCS data on a Shewhart control chart against time and apply the variance of the data moving average as the estimation of its standard uncertainty (see ASTM D6299-08). Certain pre-requisites however, do apply here, such as statistically confirming your QC data are completely random and independent by the Anderson-Darling (AD) or Shapiro-Wilk tests for normality, and visually checking the data trend of the Shewhart chart to follow a set of chart rules laid down by the standards;
  3. If your laboratory is not able to take part in any PT program because there is no such program to check your test parameters, you can use your within-lab reproducibility (or intermediate precision) data and the result bias estimates which are available in your method validation process. The relevant reference of this approach is ISO 11352:2012;
  4. For certain types of chemical tests involving a series of certified reference materials as calibration standards (e.g. the determination of total sulphur in petroleum product by X-ray fluorescence spectrometric method – ASTM D4294 – which is a direct read-out from the energy-dispersive X-ray fluorescence sulphur meter with 4-point or 5-point calibration using different sulphur reference standards), the laboratory concerned can plot a linear calibration curve involving the actual test results of these reference standards against the assigned reference values to estimate the uncertainty. See reference ISO 11095:1996(2012).
  5. The GUM component-by-component method is not suitable for the MU estimation of microbiological count experiment because the distribution of such data is not strictly normal whilst GUM has made this assumption. In fact, the Poisson probability distribution is better in this situation. The MU estimation for microbiological counting therefore is based on the holistic performance of the test method and we need to make logarithmic transformation of the within-lab reproducibility data before evaluating its variances. Various established documents are available for reference, such as ISO 10936:2006, BS 8496:2007, A2LA G108, NMKL Procedure No. 8, etc.

In my opinion, the ISO 11095 method in (d) is the most difficult one for a laboratory analyst who has acquired only elementary statistical skills, because of its application of more complicated statistical tools in estimating the constant and proportional variances. The other approaches are quite straight forward and will be easily appreciated by the analysts with average statistical knowledge.

 

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List of published documents on top down MU methods

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A list of published international documents in relation to the use of top down MU approaches (not exhaustive)

  • ISO 21748:2010 Guidance for the use of repeatability, reproducibility and trueness estimates in measurement uncertainty estimation
  • ISO 11095:1996 (2012) Linear calibration using reference materials
  • ISO 11352:2012 Water quality — Estimation of measurement uncertainty based on validation and quality control data
  • ISO 19036:2006 Microbiology of food and animal feeding stuffs — Guidelines  for the estimation of measurement uncertainty for quantitative determinations Amd1:2009: Measurement uncertainty for low counts
  • ISO 29201:2012 Water Quality – The variability of test results and the uncertainty of measurement of microbiological enumeration methods
  • ISO Guide 98-3/Suppl. 1 Uncertainty of measurement Part 3: Guide to the expression of uncertainty of measurement  Supplement 1 : Propagation of distributions using a Monte Carlo method
  • BS 8496:2007   Water quality. Enumeration of micro-organisms in water samples

  • ASTM D2554-07 Estimating and monitoring the uncertainty of test results of a test method in a single laboratory using a control sample program
  • ASTM D6299-08 Applying statistical quality assurance and control charting techniques to evaluate analytical measurement system performance
  • ASTM E2093-05 Optimizing controlling and reporting test method uncertainty from multiple workstations in the same laboratory organization
  • EuroLab Technical Report No. 1/2006 Guide to the Evaluation of Measurement Uncertainty for Quantitative Test Results
  • NORDIC Technical Report TR 537 Edition 3.1 Handbook for Calculation of Measurement Uncertainty in Environmental Laboratories
  • A2LA G108 – Guidelines for Estimating Uncertainty for Microbiological Counting Methods
  • NMKL Procedure No. 8 (2002) Measurement of uncertainty in microbiological examination of foods.
  • CNAS-GL34:2013 基于质控数据环境检测测量不确定度评定指南  Guidance for measurement uncertainty evaluation based on quality control data in environmental testing

 

 

 

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Basic statistics, Measurement uncertainty, Top-Down Approach

A chat group set up at www.meetup.com website

Yeoh GH of GLP Consulting has at the http://www.meetup.com website set up a chat group, namely Interest Group in Measurement Uncertainty for Test Labs for better interaction with the laboratory and QA personnel in this region.

The very first Meetup session is scheduled on August 30 in Singapore with venue to be confirmed later. All are welcome to join this interest group at https://www.meetup.com/Interest-Group-in-Measurement-Uncertainty-for-Test-Labs/  even though you might not be in Singapore as we shall post summaries of discussions and useful pointers after the meetup.

Meetup 1

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Measurement uncertainty, Top-Down Approach