Measurement trueness

When talking about quality of chemical measurements trueness, precision, accuracy and error are some of the more important keywords. Therefore a clear definition is necessary (see also chapter 11, slides 36 and 44). 

Note 2 RMs with or without assigned quantity values can be used for measurement precision control, whereas only RMs with assigned quantity values can be used for calibration or measurement trueness control. 

Example Human serum with assigned quantity value for the concentration of cholesterol and associated measurement uncertainty stated in an accompanying certificate, used as a calibrator or measurement trueness control material. 

NOTE 2 The term measurement accuracy should not be used for measurement trueness, and the term measurement precision should not be used for measurement accuracy, which, however, is related to both these concepts. 

According to Note 2 of the definition, the term accuracy is sometimes mixed up with measurement trueness and measurement precision. Therefore, we have a look at these two terms that are defined in ISO/IEC Guide 99 (2007), definitions 2.14 and 2.15. 

Qoseness of agreement between the average of an infinite number of replicate measured quantity values and a reference quantity value Note 1 Measurement trueness is not a quantity and thus cannot be expressed numerically, but measures for closeness of agreement are given in ISO 5725 (1994-2005). 

Note 2 Measurement trueness is inversely related to systematic measurement error, but is not related to random measurement error. 

Accuracy, Fig. 1 Different measurement series represented by mean value and plus/minus standard deviation. Best measurement accuracy, measurement series 1 best measurement trueness, measurement series 1 and 2 and best measurement precision, measurement series 3... 

Measurement series 1 and 2 show similar measurement trueness and measurement series 3 a worse measurement trueness. The averages, or the mean values, of measurement series 1 and 2 are closest to the tme quantity value, much closer than the mean value of measurement series 3. 

NOTE 3. Measurement accuracy should not be used for measurement trueness and vice versa. 

Measurement accuracy is the closeness of agreement between the measured quantity value and the true quantity value of a measurand. VIMS regards accuracy and measurement error as idealized quantities that we may not know exactly [14]. VIMS terminology uses measurement trueness, which is the closeness of agreement between the average of an infinite number of replicate measured quantity values and a reference quantity value, as measured using a CRM [13]. In the absence of a CRM that shares sufficient similarity to the protein of interest, any locally prepared material can only provide insight into precision, not trueness. 

Trueness. The level of agreement between an analytical value and an accepted reference value. Bias is the parameter that quantitatively measures trueness. A small bias indicates high trueness. 

Description Certified reference materials (CRMs) may be used as calibrators or measurement trueness control material. When used as calibrator, a CRM permits traceable and thus comparable measurement results. 

The methods It is advised that, prior to the certification measurements, the participants discuss their methods so that all participants have confidence in each others methods and there is a good level of agreement between laboratories. As it is preferred to certify on the basis of the agreement between different methods applied in different laboratories, a proposal should indude, where relevant and possible, a group of laboratories offering a range of widely different measurement methods. Each laboratory should use well established method(s), with which it can demonstrate adequate performance in terms of trueness and in terms of reproducibility. 

Verification implies that the laboratory investigates trueness and precision in particular. Elements which should be included in a full validation of an analytical method are specificity, calibration curve, precision between laboratories and/or precision within laboratories, trueness, measuring range, LOD, LOQ, robustness and sensitivity. The numbers of analyses required by the NMKL standard and the criteria for the adoption of quantitative methods are summarized in Table 10. 

For the characterization of the reliability of analytical measurements the terms precision, accuracy, and trueness have a definite meaning. 

The ISO recommendation [1993] should be followed and accuracy used only as a qualitative term. In case of quantitative characterization (by means of the bias), a problem may appear which is similar to that of precision, namely that a quality criterion is quantified by a measure that has a reverse attribute regarding the property which have to be characterized. If the basic idea of measures can be accepted, which is that a high quality becomes a high value and vice versa, bias is an unsuited measure of accuracy (and trueness). In this sense, accuracy could be defined by means of a measure proposed in the next paragraph. 

Precision, accuracy and trueness are important performance characteristics in analytical chemistry. Each of them is well-defined in a positive sense ( closeness of agreement... ). However, their quantifying is done by means of unfavourable measures, namely by error quantities like, e.g., standard deviation and bias, respectively, which indeed do quantify imprecision and... 

Accuracy and trueness have been defined above and it was mentioned that these terms base on qualitative concepts (ISO 3534-1 [1993]). If it is necessary to have quantitative information, the bias, which is a measure of inaccuracy, should not be used to quantify accuracy and trueness, respectively. Instead of this, the following measures might be applied... 

The example given in Table 7.2 is taken from a study to verify the trueness of clinical analyses (Streck [2004]). Recovery rates have been used as the criterion to accept a good agreement between the measured results and the reference values as it is frequently done by analysts. 

The situation becomes more complex when aspects of the trueness of analytical results are included in the assessment. Trueness of information cannot be considered neither by the classical Shannon model nor by Kullback s divergence measure if information. Instead, a model that takes account of three distributions, viz the uniform expectation range, po(x), the distribution of the measured values, p(x), and that of the true value, r(x), as shown in Fig. 9.5, must be applied. 

It goes without saying that you should make all measurements to the best of your ability. However, a value to the highest level of precision and trueness is not always required. The aim is that the result produced should be accurate enough to be of use to the customer, for the intended purpose (see Chapter 4). Customers may want the technical details of the method used but more often this will not be... 

Measurements are subject to systematic errors as well as the random errors covered in Section 4.3.2. Bias is the difference between the mean value of a large number of test results and an accepted reference value for the test material. The bias is a measure of trueness of the method. It can be expressed in a number of ways, i.e. simply as a difference or as a ratio of the observed value to the accepted value. This latter representation, when expressed as a percentage, is often termed recovery. This represents how much of the analyte of interest has been extracted from the matrix and measured. This is dealt with in Section 4.6.3. 

Accuracy (Trueness and Precision) of Measurement Methods and Results - Part 1. General Principles and Definitions , ISO 5725-1 1994, International Organization for Standardization (ISO), Geneva, Switzerland 1994. 

Bias is a measure of trueness . It tells us how close the mean of a set of measurement results is to an assumed true value. Precision, on the other hand, is a measure of the spread or dispersion of a set of results. Precision applies to a set of replicate measurements and tells us how the individual members of that set are distributed about the calculated mean value, regardless of where this mean value lies with respect to the true value. 

II the difference approach, which typically utilises 2-sided statistical tests (Hartmann et al., 1998), using either the null hypothesis (H0) or the alternative hypothesis (Hi). The evaluation of the method s bias (trueness) is determined by assessing the 95% confidence intervals (Cl) of the overall average bias compared to the 0% relative bias value (or 100% recovery). If the Cl brackets the 0% bias then the trueness that the method generates acceptable data is accepted, otherwise it is rejected. For precision measurements, if the Cl brackets the maximum RSDp at each concentration level of the validation standards then the method is acceptable. Typically, RSDn> is set at <3% (Bouabidi et al., 2010),... 

International Organization for Standardization (ISO), Statistical methods for quality control, Vol. 2, 4th Edition, Accuracy (trueness and precision) of measurement methods and results - Part 2 Basic method for the determination of repeatability and reproducibility of a standard measurement method, ISO 1994(E), 5725-2. 

Accuracy expresses the closeness of a result to the true value. Accuracy = trueness + precision. Under specific conditions it is quantified by the measurement uncertainty. Measurement uncertainty may vary under changing conditions and method validation determines the degree. 

Trueness The closeness of agreement between the average of a infinite nimber of replicate measure quantity values and a reference quantity value [VIM] Precision closeness of agreement between indications ormeasLFed quantity values obtained by replicate measurements on the same or similar objects under specified conditions [VIM]... 

A result could have good precision but a bad trueness . That is, all measurements could be repeatable, but the mean value could be far away from the trae value. This is not a good accmacy . Similarly, a result could have good trueness whilst precision could be bad. In this case the mean value may be close to the true value even though precision is low. This is again not a good accuracy . See also chapter 8 for further information. 

The recovery can be used as a measure of the trueness. Recoveries usually depend on sample matrix, sample preparation method and concentration present in the sample. The mean % recovery for a trace component (<... 

Precision gives information on random errors. The precision tells us how close together repeated measurements are to each other. So there is a clear distinction between precision and trueness. 

International organization for Standardization, Accuracy (trueness and precision) of measurement methods and results, ISO/DIS 5725-1 to 5725-3, Draft versions 1990/91. 

The purpose of an analytical method is the deliverance of a qualitative and/or quantitative result with an acceptable uncertainty level. Therefore, theoretically, validation boils down to measuring uncertainty . In practice, method validation is done by evaluating a series of method performance characteristics, such as precision, trueness, selectivity/specificity, linearity, operating range, recovery, LOD, limit of quantification (LOQ), sensitivity, ruggedness/robustness, and applicability. Calibration and traceability have been mentioned also as performance characteristics of a method [2, 4]. To these performance parameters, MU can be added, although MU is a key indicator for both fitness for purpose of a method and constant reliability of analytical results achieved in a laboratory (IQC). MU is a comprehensive parameter covering all sources of error and thus more than method validation alone. 

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