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Decision rule and ISO/ IEC17025:2017

Notes on decision rule as per ISO/IEC 17025:2017 requirements

Introduction

The revised ISO/IEC 17025:2017 laboratory accreditation standard introduces a new concept, i.e., “risk-based thinking” which requires the operator of an accredited laboratory to plan and implement actions to address possible risks and opportunities associated with the laboratory activities, including issuance a statement of conformity to product specification or a compliance statement against regulatory limits.

The risk-based approach to management system implementation is one in which the breadth and depth of the implementation of particular clauses is varied to best suit the perceived risk involved for that particular laboratory activity.

Indeed, the laboratory is responsible for deciding which risks and opportunities need to be addressed. The aims as stated in the ISO standard clause 8.5.1 are:

  1. to give assurance that the management system achieves its intended results;
  2. to enhance opportunities to achieve the purpose and objectives of the laboratory;
  3. to prevent, or minimize, undesired impacts or interfering elements to cause failures in the laboratory activities, and
  • to achieve improvement of the activities.

The decision rule as required in ISO/IEC 17025:2017

On the subject of decision rule for conformity testing, the word of ‘risk’ can be found in the following relevant clauses of this international standard:

Clause 7.1.3

When the customer requests a statement of conformity to a specification or standard for the test or calibration (e.g. pass/fail, in-tolerance/out-of-tolerance), the specification or standard and the decision rule shall be clearly defined.  Unless inherent in the requested specification or standard, the decision rule selected shall be communicated to, and agreed with the customer.”

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 7.8.6.2

The laboratory shall report on the statement of conformity, such that the statement clearly identified:

  1.  to which results the statement of conformity applies;
  2. Which specifications, standards or part therefor are met or not met;
  3. The decision rule applied (unless it is inherent in the requested specification or standard).

From these specified requirements, it is obvious that clearly defined decision rules must be in place when the laboratory’s customer requests for inclusion of a statement of conformity on the specification in the test report after laboratory analysis.  Therefore, the tasks in front of the accredited laboratory operator are how the decision rules are going to be for a tested commodity or product, based on the laboratory’s own measurement uncertainty estimated, and how to communicate and convince the customers on its choice of reporting limits against the given specification or regulatory limits when issuing such conformity statement.

Examples on how to calculate combined standard uncertainty (edited)

Uncertainty calculation

It is very important for anyone interested in the evaluation of measurement uncertainty to fully understand the very basic principles in calculating the combined standard uncertainty.  Let’s look at some worked examples ….

Calculating standard uncertainties for each uncertainty contribution

In evaluating the combined uncertainty of a testing method from various sources of uncertainty, we need to ensure that we work on a platform of standard uncertainties expressed as standard deviations throughout, because in addition to the standard uncertainty (u) values obtained by our own evaluation (Type A uncertainty), we may also encounter the so-called Type B uncertainty contributions which are uncertainty (U) values given by a third party or from experience and other information in different forms.   Read on … How to calculate standard uncertainties for each source of uncertainty

 

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

 

Estimation of both sampling and measurement uncertainties by Excel ANOVA Data Analysis tool

Sampling and analysis

Estimation of sampling and analytical uncertainties using Excel Data Analysis toolpak

In the previous blog  https://consultglp.com/2018/08/22/a-worked-example-of-measurement-uncertainty-for-a-non-homogeneous-population/ ,  we used the basic ANOVA principles to analyze the total chromium Cr data for the estimation of measurement uncertainty covering both sampling and analytical uncertainties….

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…..

A simple example of sampling uncertainty evaluation

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A simple example of sampling uncertainty evaluation

In analytical chemistry, the test sample is usually only part of the system for which information is required. It is not possible to analyze numerous samples drawn from a population. Hence, one has to ensure a small number of samples taken are representative and assume that the results of the analysis can be taken as the answer for the whole…..

Improving uncertainty of linear calibration experiments

Standard error of cal curve

Improving uncertainty of linear calibration experiments

 

Confidence intervals- How many measurements should you take?

Laboratory 1

Confidence intervals – how many measurements to take

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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