EUROLAB
iso-21748-use-of-uncertainty-in-analytical-results
Pesticide Residue Analysis AOAC 2001.01 Multiresidue Pesticides in Fruits and VegetablesAOAC 2003.05 Pesticide Residue in Herbal ProductsAOAC 2005.06 Pesticide Residue Analysis in Baby FoodAOAC 2007.01 Pesticide Residue in Meat and PoultryAOAC 2007.02 Pesticides in Honey by QuEChERSAOAC 2007.07 Multi-Class Pesticide Residue TestingAOAC 2008.03 Pesticide Residue in Animal Feed AnalysisAOAC 2008.05 Pesticide Residue in Fruit JuicesAOAC 2009.01 Pesticide Residue in Spices TestingAOAC 2009.02 Pesticide Residue Testing in CerealsAOAC 2009.03 Determination of Pesticide Residues in CoffeeAOAC 2010.01 Pesticide Residue in Dairy ProductsAOAC 2010.02 Multiresidue Pesticide Testing in SpicesAOAC 2011.01 Multiresidue Pesticide Analysis in CommoditiesAOAC 2011.02 Multiresidue Pesticide Testing in VegetablesAOAC 2012.01 Pesticide Residue in Animal TissueAOAC 2013.05 Multiresidue Pesticide Testing by LC-MS/MSAOAC 2014.01 Multiresidue Pesticide Testing in CerealsAOAC 2015.01 QuEChERS Extraction for Pesticide TestingAOAC 2016.01 Multiresidue Pesticide Analysis by LC-MS/MSAOAC 991.13 Multiresidue Pesticide Detection in FoodAOAC 991.14 Multiresidue Pesticide Analysis in Cereal GrainsAOAC Official Method 2007.01 Pesticide Residue in Fruits TestingCEN EN 12393 Method for Pesticide Residue DeterminationCEN EN 14244 Liquid Chromatography for Pesticide ResiduesCEN EN 15635 Determination of Pesticides in CerealsCEN EN 15635 GC-MS/MS for Pesticide Residue AnalysisCEN EN 15635 Liquid Chromatography for Multi-Residue PesticidesCEN EN 15635 Standard Operating Procedures for Residue TestingCEN EN 15662 Multi-Residue Method for Pesticide DetectionCEN EN 15672 Multi-Residue Pesticide Analysis in FruitsCEN EN 15681 Pesticide Residue Analysis in Drinking WaterCEN EN 15681 Validation of Pesticide Residue MethodsCEN EN 15682 Pesticide Residue Analysis in VegetablesEPA Method 1698 Pesticide Residues in Water by LC-MS/MSEPA Method 1699 Glyphosate and AMPA Residue AnalysisEPA Method 3541 Soxhlet Extraction of PesticidesEPA Method 3545A Pesticide Extraction by Pressurized Fluid ExtractionEPA Method 3546 Microwave Extraction of PesticidesEPA Method 3550C Ultrasonic Extraction of PesticidesEPA Method 3620C Florisil Cleanup for Pesticide ResiduesEPA Method 3640 Solid Phase Extraction for Residue AnalysisEPA Method 3660 Solid Phase Extraction for Pesticide ResidueEPA Method 3660A Extraction of Pesticides from SoilEPA Method 3665 Matrix Solid Phase Dispersion for Pesticide AnalysisEPA Method 8080 Organochlorine Pesticide Residue AnalysisEPA Method 8081B Organochlorine Pesticides AnalysisEPA Method 8082 Polychlorinated Biphenyls and PesticidesEPA Method 8095 Organophosphorus Pesticide Residue AnalysisEPA Method 8141B Organochlorine Pesticide Analysis by GCEPA Method 8270D Semi-Volatile Organic Compounds by GC/MSEPA Method 8270E Semivolatile Organic Compounds AnalysisEPA Method 8275C Volatile Organic Compounds by GC/MSEPA Method 8275D Semivolatile Organics by GC/MSEPA Method 8310 Pyrethroids and Pesticide Residues by GCEPA Method 8315B Organophosphorus Pesticide Residue TestingEPA Method 8321B Pesticides and PCBs by GC/MS/MSEPA Method 8322 Pesticide Residues in Environmental SamplesISO 10381-6 Soil Sampling for Pesticide ResiduesISO 11843 Capability of Detection for Pesticide ResiduesISO 13485 Quality Management for Pesticide Testing LabsISO 13528 Statistical Methods for Proficiency Testing in Residue AnalysisISO 14869-1 Determination of Pesticides in WaterISO 16050 Sampling Procedures for Pesticide AnalysisISO 16050-1 QuEChERS Method Validation for Pesticide ResiduesISO 16140 Validation of Analytical Methods for Residue TestingISO 16141 Performance Criteria for Pesticide Residue AnalysisISO 17025 Laboratory Competence in Pesticide TestingISO 17034 Reference Material Production for Pesticide TestingISO 18593 Surface Sampling for Pesticide ResiduesISO 19036 Measurement Uncertainty in Pesticide AnalysisISO 21496 Pesticide Residue Analysis in Food ProductsISO 21748 Guidance on Uncertainty in Pesticide Residue AnalysisISO 21871 Analytical Methods for Pesticide ResiduesISO 24253-1 Measurement Uncertainty in Pesticide Residue TestingISO 5667-3 Water Sampling for Pesticide ResiduesISO 5725 Precision of Pesticide Residue MeasurementsISO 5725-1 Accuracy of Pesticide Residue MeasurementsISO 5725-2 Repeatability and Reproducibility for Pesticide TestsISO 9001 Quality Management Systems for Testing LaboratoriesUSDA Pesticide Data Program Residue Testing

Comprehensive Guide to ISO 21748: Use of Uncertainty in Analytical Results Testing Services

Provided by Eurolab

ISO 21748 is an international standard that outlines the principles for expressing the uncertainty of measurement results obtained from analytical laboratory testing. This standard is part of a broader series of ISO standards related to measurement and calibration, including ISO/IEC 17025, which specifies general requirements for the competence of testing and calibration laboratories.

Legal and Regulatory Framework

The use of analytical laboratory testing is governed by various laws and regulations at both national and international levels. These include:

  • European Union (EU) directives and regulations, such as the EUs Water Framework Directive

  • International Organization for Standardization (ISO) standards, including ISO 21748

  • National standards, like the American Society for Testing and Materials (ASTM) standards in the United States

    • International and National Standards

    The following international and national standards are relevant to analytical laboratory testing:

  • ISO 17025:2017 - General requirements for the competence of testing and calibration laboratories

  • ISO 9001:2015 - Quality management systems

  • ASTM E2500-18 - Standard practice for addressing measurement uncertainty in precision and methodology tests

  • EN ISO/IEC 17025:2018 - General requirements for the competence of testing and calibration laboratories

    • Standard Development Organizations

    Standard development organizations, such as ISO, ASTM, and EN, play a crucial role in shaping the standards that govern analytical laboratory testing. These organizations:

  • Develop and publish new standards

  • Review and update existing standards

  • Provide guidelines for standard implementation and use

    • Why Standards Matter

    Standards are essential for ensuring consistency, accuracy, and reliability in analytical laboratory testing. By following established standards, laboratories can:

  • Ensure compliance with regulatory requirements

  • Enhance product safety and quality

  • Improve communication and collaboration among stakeholders

    • ISO 21748 is essential for any laboratory that performs analytical testing, as it provides a framework for expressing measurement uncertainty. This standard:

    Business and Technical Reasons for Conducting ISO 21748 Testing

    There are several reasons why laboratories need to conduct ISO 21748 testing:

  • Compliance with regulations: Laboratories must demonstrate compliance with relevant laws and regulations, such as EU directives.

  • Quality assurance: ISO 21748 testing helps ensure the accuracy and reliability of measurement results.

  • Risk management: By understanding measurement uncertainty, laboratories can identify potential risks and take steps to mitigate them.

    • Consequences of Not Performing ISO 21748 Testing

    Failure to conduct ISO 21748 testing can have serious consequences:

  • Non-compliance with regulations: Laboratories may face fines, penalties, or even license revocation.

  • Reduced product quality: Inaccurate measurement results can compromise product safety and effectiveness.

  • Loss of customer confidence: Non-compliance with standards can damage a laboratorys reputation and relationships with customers.

    • Conducting ISO 21748 testing involves several steps:

    Testing Equipment and Instruments

    The following equipment and instruments are typically used for ISO 21748 testing:

  • Spectrophotometers: For measuring absorbance or transmittance

  • Chromatographs: For separating and detecting chemical compounds

  • Mass spectrometers: For identifying and quantifying molecules

    • Testing Environment Requirements

    The testing environment must meet specific requirements, including:

  • Temperature control: Maintaining a stable temperature between 20C and 25C.

  • Humidity control: Regulating humidity levels to prevent condensation or drying out of samples.

  • Pressure control: Ensuring that the testing equipment is operated within recommended pressure ranges.

    • Sample Preparation Procedures

    Sample preparation involves several steps:

    1. Sampling: Collecting a representative sample from the material being tested.

    2. Pre-treatment: Preparing the sample for analysis, such as grinding or dissolving it in a solvent.

    3. Storage: Storing the prepared sample to prevent contamination or degradation.

    Testing Parameters and Conditions

    The testing parameters and conditions will depend on the specific instrument being used:

  • Spectrophotometers: Measuring absorbance or transmittance at a specified wavelength.

  • Chromatographs: Separating and detecting chemical compounds using a mobile phase and stationary phase.

  • Mass spectrometers: Identifying and quantifying molecules based on their mass-to-charge ratio.

    • Measurement and Analysis Methods

    The measurement and analysis methods used will depend on the specific instrument being used:

  • Spectrophotometers: Measuring absorbance or transmittance using Beers law.

  • Chromatographs: Identifying and quantifying chemical compounds based on their retention time and peak area.

  • Mass spectrometers: Identifying and quantifying molecules based on their mass-to-charge ratio.

    • Expression of Measurement Uncertainty

    The measurement uncertainty is expressed as a standard deviation or a range, depending on the specific instrument being used:

  • Spectrophotometers: Expressing measurement uncertainty in terms of absorbance or transmittance.

  • Chromatographs: Expressing measurement uncertainty in terms of retention time and peak area.

  • Mass spectrometers: Expressing measurement uncertainty in terms of mass-to-charge ratio.

    • The measurement uncertainty can be expressed as a standard deviation or a range:

  • Standard deviation: A measure of the dispersion of individual measurement results around the mean value.

  • Range: An estimate of the maximum and minimum values within which the true value is likely to lie.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    The measurement uncertainty can be expressed as a single value or an interval:

  • Single value: Expressing the measurement uncertainty as a single number, such as 2.5.

  • Interval: Expressing the measurement uncertainty as a range, such as 1.0 to 3.5.

    • When expressing measurement uncertainty, laboratories must consider several factors:

  • Instrumental limitations: The inherent accuracy and precision of the instrument being used.

  • Sampling variability: The variation in sample preparation and testing conditions.

  • Analytical variability: The variation in measurement results due to differences in instrumental settings or operator technique.

    • To express measurement uncertainty, laboratories can use several methods:

    1. Type A evaluation: A statistical method for estimating measurement uncertainty based on a large number of replicate measurements.

    2. Type B evaluation: A non-statistical method for estimating measurement uncertainty based on expert judgment or historical data.

    It seems you provided the same information multiple times without any new additions to your question. Could you please provide more specific details about what you would like help with regarding Expression of Measurement Uncertainty? This will allow me to better assist you.

    Need help or have a question?
    Contact us for prompt assistance and solutions.

    CONTACT US +902127020000

    Latest News

    View all

    Eurolab Successfully Renews ISO 17025 Accreditation

    Read More

    Expanded Cosmetic Safety Testing Portfolio Now Available at Eurolab

    Read More

    Eurolab Launches Technical Training Series for Industry Professionals

    Read More

    JOIN US
    Want to make a difference?

    Careers