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Archive for the ‘Measurement uncertainty’ Category

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

IMG_6916

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

Bottling_edited

A linear regression approach to check bias between methods – Part II

 

Plot of reference and trial methods

A Worked Example

Suppose that we determined the amount of uranium contents in 14 stream water samples by a well-established laboratory method and a newly-developed hand-held rapid field method…..

A linear regression approach to bias between methods – Part II

A linear regression approach to check bias between methods – Part I

Plot of reference and trial methods

Linear regression is used to establish a relationship between two variables. In analytical chemistry, linear regression is commonly used in the construction of calibration curve for analytical instruments in, for example, gas and liquid chromatographic and many other spectrophotometric analyses….

A linear regression approach to bias between methods – Part I

 

 

Estimation of bias between two sampling methods

Cargo steel drums

Measurement uncertainty has two main contributors, namely sampling uncertainty and analytical uncertainty, but most laboratory analysts tend to equate analytical uncertainty as its measurement uncertainty based on the sample received.  This may be true when the target (population) lot sampled is homogeneous where every part of the target have an equal chance of being incorporated in the sample…..

Estimation of bias between 2 sampling methods