Presentation on theme: "An Introduction to Quality Assurance in Analytical Science"— Presentation transcript:
1 An Introduction to Quality Assurance in Analytical Science Dr Irene Mueller-HarveyMr Richard BakerMr Brian Woodget
2 Part 2 - The Analytical Method Contents:VAM principles (slides 3&4)Fit for purpose (slide 5)Choice of method (slide 6)Method development and validation (slide 7)Method performance characteristics (slides )Method validation (slides )The presentation contains some animation which will be activatedautomatically (no more than a 2 second delay), by mouse click or by useof the ‘page down’ key on your keyboard.
3 VAM Principles (1)The DTI’s programme on Valid Analytical Measurement (VAM) is an integral part of the UK National Measurement System. The aim of the VAM programme is to help analytical laboratories to demonstrate the validity of their data and to facilitate mutual recognition of the results of analytical measurements. You can find out more about VAM by logging onto the web site [The VAM Bulletin is publishedhalf yearly and is sent free toall registered subscribers. Youmay subscribe via the website.
4 VAM Principles (2) There are six VAM principles: Analytical measurements should be madeto satisfy an agreed requirementAnalytical measurements should be madeusing methods and equipment which have beentested to ensure they are fit for their purposeAnalytical measurements madein one location should beconsistent with those elsewhereStaff making analytical measurementsshould be both qualified and competentto undertake the taskOrganisations makinganalytical measurementsshould have well definedquality control and qualityassurance proceduresThere should be a regular independent assessmentof the technical performance of the laboratory
5 Fit for PurposeYou have seen in part 1 of this presentation that it can be expensiveto generate good quality analytical data. The cost of producing datacan often be reduced by selecting analytical methods and technologiesthat produce data in accordance with the stated objectives forcarrying out the analysis or test. Consider two examples:A commercial limescale remover states that it contains between 5 to15% w/v of sulphamic acid. Therefore any analysis for quality controlpurposes needs only to show that the quantity present is indeed betweenthese two limits and does not need to be accurate to the nearest + 0.1%The EU current statutory limit for lead in potable water is 25 μg/l. Thus theuse of a method to perform the analysis, which only provides a ‘less than’value of 100 μg/l, is not suitable for this purpose
6 laboratory/situation. Choice of MethodIt is important to appreciate the difference between an ‘analytical method’ (combination of steps illustrated by the ‘analytical process’) and an ‘analytical technique’ (chemical or instrumental procedure by which analyticaldata is eventually obtained). In selecting a method we shall need to consider thefollowing parameters:sample type (matrix) and size (lot or a little);data required (qualitative/quantitative);expected level(s) of analyte(s);precision & accuracy expected;likely interferences;number and frequency of samples for analysis.Always use astandard method ifone is available asthis will save ondevelopment time.However the methodmust be checked toprove that it suitablefor yourlaboratory/situation.Modification maywell be required.
7 Method development and validation ‘Never attempt to re-invent the wheel!’Before embarking on the development of a new method, always research the chemical literature to see if a suitable one already exists. If a suitable one is found, it will still be necessary however to perform some method validation to prove that the method can be successfully adapted to your laboratory, equipment and personnel. More extensive validation is required for a brand new method. Methods in any field of analysis may be defined in terms ‘Method performance characteristics’ and it is these parameters plus a few others, that are quantified during a method validation exercise.
8 Method performance characteristics A method’s performance is defined by a number of importantindividual characteristics. There are:Sensitivity PrecisionAccuracy Limit of Detection (LoD)Limit of Determination BiasSelectivity Linear RangeDynamic range
9 Accuracy and precision The dictionary definition of both ‘accuracy’ and ‘precision’ are roughly the same, indicating that these words may be used synonymously. However in ‘Analytical Science’ they have two separate meanings, the difference between them is best illustrated by using target diagramsPoor precisionpoor accuracyGood precisionpoor accuracyGood mean accuracypoor precisionGood accuracygood precision
10 Accuracy and precision (2) You saw from the previous slide, a set of results can beeither accurate and/or precise or can be neither accuratenor precise. Thus accuracy may be defined as:The closeness of the mean value from a replicate set of results to thetrue or accepted valuePrecision may be defined as:The spread of results from a replicate set of measurementsThe difference between the true value and the meanmeasured value is termed bias. The spread of replicate data ismeasured in terms of standard deviation (s) or variance (s2)
11 Random and systematic errors There are 2 types of error for which allowance may be made:Random error Systematic errorRandom error arises from variationsin parameters which are outside thecontrol of the analyst, but whichinfluence the value of themeasurement being made. Becausethese errors are statistically random,the mean error should be zero ifsufficient measurements are made.Systematic error remains constant ormay vary in a predictable way over aseries of measurements and cannot bereduced by making replicatemeasurements. In theory, if known,this error can be allowed for.Eg: subtraction of blank valuesmeanmean
12 Bias and variance Bias = Mean value - true value = 10.06 - 10.00 A solution containing copper wasanalysed 10 times using atomicabsorption spectroscopy.The results obtained in ppm were:10.08, 9.80, 10.10, 10.21, 10.14,9.88, 10.02, 10.12, 10.11, 10.09We can now calculate the precisionof the data as standard deviationIf the true value is known to be10.00 ppm, we can also calculatethe biasBias = Mean value - true value== ppmStandard deviation (SD) = 0.12(4)Relative SD = 100 X SD/10.00 = 1.2%Conclusion - the method gives both good accuracy (low bias) andacceptable precision (RSD of 1.2%)
13 Sensitivity and selectivity Sensitivity is the change in measuredsignal for unit change in concentrationand can be obtained from thecalibration graphdydxSensitivity = dy/dxSelectivity is the ability of a methodto discriminate between the targetanalyte and other constituents of thesample. In many instances selectivityis achieved by high performanceseparation using chromatographic orelectrophoretic techniques.Hplc chromatogram
14 Limits of detection (LoD) and determination Example: In an analysis of traceCd by plasma emissionspectrometry the following datawere obtained:mean blank (Bl) signal 4SD of blank signal500 ppb CdLoD = Bl signal + 3(SD of Bl)= 4 + 3(12)= 40This equates to: [40/2000] X 500 ppb= 10 ppb CdThe limit of determination uses asimilar formula, replacing the3 SD’s by 10. This gives the limit ofdetermination as 31 ppb CdThese values refer to the statistical limits below which results of detection or accurate quantitative measurements (determination) should not be reported. The levels of both are dependent upon the variability of the signal when a blank containing none of the analyte is being measured. The signal generated under these conditions is mostly signal noise and is assumed to exhibit a normal distribution pattern. Both the blank signal and the standard deviation of the blank signal need to be measured. From this data we can calculate both limits.
15 Linear and Dynamic ranges These terms refer to the extent to which the method maybe used to produce accurate quantitative dataFrom the graph, it would appear that the data is linear to about 25 ppm and dynamic until about 75 ppm. After 75 ppm there is only minimal increase in signal for increased concentration.Expanding thelower section ofthe graph however,shows that non-linearity starts ataround 20 ppm.Top of linearrangeExtent of dynamic range
16 Method validationValidation, is the proof needed to ensure that an analyticalmethod can produce results which are reliable and reproducibleand which are fit for the purpose intended. The parameters that need to be demonstrated are those associated with the ‘Performance characteristics’ together with robustness, repeatability and reproducibility.Many analytical methods appearing in the literature have not beenthrough a thorough validation exercise and thus should be treated withcaution until full validation has been carried out. Validation of a newmethod (new to your laboratory), is a costly and time-consuming exercise,however the result of not carrying out method validation could result inlitigation, failure to get product approval, costly repeat analysis andloss of business and prestige.You can now consider in more detail how validation iscarried out
17 Method validation - linearity Check linearity between% of the expectedanalyte concentrationLinearityMost analytical methods are of a comparative type and thus requirecalibration against accurately known standards to generate quantitativedata. Where possible calibration data should show a linear relationshipbetween analyte concentration and measured signal, however it isacceptable under some circumstances, to use a non-linear relationshipup to the limit of the dynamic range.
18 Method validation - specificity Loss of specificity can be due to interferences and matrixeffects.All likely interferences should be investigated and their effects on analyteresponse determined over a range of concentrations. Measures can then beput into place to mask, eliminate or separate them from the analyte.Standard additionprocedures can be usedto identify matrix effectsnoyes
19 Method validation - precision You have seen already that,precision is measured interms of standard deviation (SD). Assuming that the variability of the measurements is totally random (obeys a normal distribution curve) thena formula derived from this distribution may be usedto calculate standard deviation.Estimation of true meanSD = [ Σ(xi - x)2/(n - 1)]1/2where:xi = individual data pointx = mean value of the datan = the total number of data pointsΣ = the sum ofIn practice around data points are used normally to calculate the SD, although statisticians would recommend 50
20 Method Validation - repeatability & reproducibility Methods need to be shown to be both repeatable andreproducible. A replicate set of data produced at a particulartime point by an operator working with a particular set of equipment in a given laboratory will verify repeatability. To show reproducibility, the method must produce similar results when any of these parameters are changed. The most likely changes are to time and operator.Two different operatorsanalysing milkusing different pieces ofequipment at differenttimes. The laboratory isthe same.
21 Method validation - reliability The reliability of a method can be tested in a number of waysSelection of reference materialsfrom LGCTest results from the newmethod against an existingmethod which is known tobe accurateThe best way of demonstratingaccuracy is to analyse a referencematerial or certified referencematerial (CRM) if one is available[see part 3 of this presentation]Add a known quantity of pure analyte(spike) to a real sample or real samplematrix and check that all of the addedsubstance can be measured(recovered)
22 Method validation - detection & quantitation limits A method is not acceptable for accurate detection orquantitation if the analyte level is likely be fall beneaththe limit(s) calculated based upon the blank signal andits standard deviation (Refer to the formula given in slide 14,in this part of the presentation). Analyte pre-concentrationthen becomes necessary.Mean sample signalmust be sufficientlylarger than the blankso that positivedetection or accuratequantitation ispossibleVariation insample signalVariation inblank signalMean samplesignalMean blanksignal
23 Method validation - robustness Robustness of an analytical method refers to it’s abilityto remain unaffected when subjected to small changesin method parameters.For exampleIn an hplc analysis the mobile phase is defined in terms of % organicmodifier, pH of the mobile phase, buffer composition, temperatureetc. A perfect mobile phase is one which allows small changes in thecomposition without affecting the selectivity or thequantitation of the method.Alter all major parameters in order to ascertain when the method ceasesto function in accordance with specifications
24 Method validation - establish stability In routine analysis where numerous samples and standardsare measured each day, it is essential to assess the stability ofthe prepared solutions. Stability of these solutions should betested by repeat analysis over at least a 48 hour period.Apparent onset ofsolution instabilitySignalTime
25 Method validation - additional reading An article entitled “A Practical Guide to Analytical Method Validation” was published in Analytical Chemistry in 1996 [ Anal. Chem. (68) 305A-309A]The article may be downloaded free from the acs web site: