celtic game live stream tonight free. NOTE The speci fi ed tolerance for a re ference materi a l can be based on con siderations for intended usc.">
The competence of each laboratory must be demonstrated by data that were not obtained on the material to be characterised [ 8 , subclause 7. With ISO , the characterisation approaches which can be used by conformity-assessed RMPs have been broadened. Other requirements are not new but phrased more explicitly, i.
Therefore, RMPs being in compliance with ISO Guide are likely to also be in compliance with ISO , provided that they intrinsically covered already the new requirements impartiality and risks and opportunities.
With the transformation of ISO Guide into ISO , harmonised requirements for the competence of RMPs and harmonised criteria for the assessment of the competence by third parties have been laid down. ISO Guide 34 General requirements for the competence of reference material producers. ISO, Geneva. Google Scholar. Accred Qual Assur — Whe re t he intended use pe rmits the use of part of a unit- for exa mple, a small porti on of a solid or liquid mate rial, or a sma ll region of the surface - it is also us ually necessary either to assess t he within-unit va ria bili ty of the mate ria l within-unit he te rogeneity or to provide in s tructions for use t ha t control the impact of within-unit heteroge neity.
T he assessme nt of homogeneity may include the use of prior e v ide nce i ncl uding prior expe r ime ntal evidence of the homogeneity of th e material, pe rforming an e xpe rime ntal homogeneity s tudy on the candid a te re fere nce materia l, or both. In most cases, a n e xpe rime nta l study is necessa ry. Exce ptions include, for e xa mple, batches of a highly homoge neous mate rial, s uch as a solution for which previous expe rime nta l s tudies h ave demons tra ted that packaging and st orage do not a ffect t he homogeneity; or.
U gives further deta ils about the circumstances and ty pes of material requiring experimental study. The results of an experimental homogeneity s tudy are usually a lso used for the calcula tion of one of the uncertainty components in the certification model see Clause The magnitude of this u nce rtai nty component ca n vary widely compared with other components of the uncertainty, de pending on the nature of the RM and of the certified property.
To undertake a homogeneity s tudy, a s ubset of units, typically 10 to 30, is chose n from the batch using a suitable sa mpling scheme, property values a re meas ured for each unit using a suitable measurement procedure a nd th e results are assessed using appropriate s tatistical methods to obtain information on, for exa mple, between-unit variabi lity and within-unit variability of the mate rial. Examples of calculations are provided in Annex C.
Hi storica lly, homoge neity studies have tes ted for sta tis tically significant between-unit differe nces compared with measurement precision in order to decide whether a mate rial is homogeneous or not. This approach is not taken in this document; rathe r, emphasis is placed on deciding whether the between-unit s tanda rd dev iation is sufficiently small for the intended end use.
Statistica l tests of s ignifica nce may, however, be of use in RM production, for example in order to decide whether further processing is required to reduce heterogeneity to insignifica nce compared w ith routine measurement precis ion.
Statis tical power calculations Although th e magnitude of between-un it differe nces can sometimes be small or even negligible after homoge nization, in other cases, between-unit differences can rema in larger than the uncertainty arising from characterization. RMs prepa red from s uch heterogeneous materials should t herefore be subjected to an ex perimental homogeneity study.
RMs prepa red as pu re compound s or solutions of pure compounds if cert ified for purity; not for impurities are ex pected to have a high degree of homogeneity. These materia ls ca n, however, also show some he t erogeneity, for example, due to a density gradie nt, localized contamination, evapora tion of solve nt during processing or fi lling, variations in residual solvent content, or metals conta in ing variable a mounts of occluded gases.
Furthe rm ore, cert ified values for such materia ls a re often expected to have very s mall uncertainties, making even a s ma ll a mount of heterogeneity potentia lly importa nt. Even in cases w he re the mate ria l is expected to be s ufficiently homogeneous for most intended uses, homogeneity should be verified. Verification may include a complete homogeneity study or other check for exa mpl e, a check on melting point cons iste ncy between units of a pure organic ma te ria l.
An ex perimental study of the homogeneity of a mate rial is not essential in the following cases :. Examples of materials that are sometimes produced in this way include ethanol calibration solutions or elemental calibration solutions prepared by mass and thoroughly mi xed to ensure that the mixture will be sufficiently homogeneous for the intended use. Where assurance of homogeneity relies on a validated production process, quality control procedures should be used to confirm consis tent operation of the production process.
Such procedures may include, for example, operation of a range control chart or standard deviation control chart for monitoring the range or s tandard deviation of a small number of units measured, or criteria for the range of values found in each characterization.
NOTE 1 6. It is essential that any subset of properties taken as representing homogeneity for a larger set of properties be appropriately selected on the basis of established chemical or physical relation ships.
For U example, an inter-ele me nt concomita nce in the mineral phases of an RM would s upport the assumption that the RM also has a s imilar degree of homogeneity for the non-selecte d ele ments. The evidence should be sufficient to show that the uncertainty associated with U. To achieve these objectives, the number of items should be s ufficie nt to give a reasonable estimate of the between-unit va ria nce, and sufficie nt items taken to give a clear view of a ny tre nds present.
Based on current practice, a n accepta ble estimate of the be tween-unit va ri a nce for the purposes of unce rtainty evaluation ca n be obtained with nine or more degrees of freedom.
For a s imple. Where a nested design is to be used for measurements, in which a subset of units is measured in each of several runs, additional units should be included in the s tudy in order to maintain the required degrees of freedom. Trends arising from processing often appear as an initial trend followed by stable output, as a trend developing late in the process, or as a combination of the two.
Where these features occur, it is often possible to provide a homogeneous material by discarding the affected units from the beginning or end of the run.
However, examining only 10 units might not provide sufficient information a bout the onset of trends near the ends of a lengthy processing run. Taken together with the degrees of u'l ' 0 freedom requirement above, this leads to a recommended minimum number of units Nmin for a '. This s tudy accordingly 10 req uires 15 units of material for the homogeneity study. Q' For reference materials certified for a qu alitative, or "nominal", property, the number of units chosen f for the homogeneity study should be set ba sed on sa mpling guidance for inspection by attributes as described in the ISO series[fi or s imilar guidance.
Sampling plans for inspection by attributes lead to very high inspection numbers if low proportions of defective units are to be detected by sampling alone.
Thi s is often unreali s tic for typical reference material batch sizes. If a certified qualitative property is individually verified for every unit of s uch a materia l, it is not necessary to perform a further test of the homogeneity of that property. For s uch s mall production batches, the minimum number of units specified in 7. Replication s hould be as high as practically feasible to provide the bes t ava ilable tes t powe r for the number of units used.
Power a na lys is 7. For example, vvith three units, four observation s per unit g ives. Statistical powe r a na lys is may be used to assist in choosing a su itable number of un its and replicates for the homogeneity study. Power analysis ai ms to control the probability of failing to detect a particular level of heterogeneity given a proposed s tatistical t est for significant hete rogeneity. Power analysis is a specialized topic but is increasingly available in software, some available without charge.
The most common example of power analysis in thi s context is the calculation of the numbers of test items and re plicate measurements on the ass umption that one-way analysis of variance see 7. Although statistica l powe r a na lysis ca n be useful in compari ng different proposed strategies, considerable care should be taken in its use. In particular, the choice of replicate numbers and unit numbers is very strongly dependent on the ass umed distribution of any hypothes ized between-sa mple difference.
For exa mple, ass uming a norm al di stribution for the true mea ns of different units, a common default in power calculatio n software, leads to a high proposed number of replicates and a sma ll nu mber of units. This is a poor choice if the most likely pattern of heterogeneity is a s mall proportion of discrepant units among a la rgely homogeneous population. In the absence of good information on the likely distribution of different true unit means, therefore, power analysis is most useful as an aid in choosing replicate numbe rs after the proposed numbe r of units has been decided.
The sa mpling scheme used to pick the units ite ms, bottles for a homogeneity study is typical ly one of s imple random sampling, st ra tified ra ndom sampling or syst emat ic sa mpling.
Random stratified sa mpling typica lly div ides the batch into a number o f seg me nts of equa l size usually by production or packagi ng seque nce or, some times, location and takes a n equal numbe r of units often one at random from each s uch segme nt.
Systematic sampling sets a n interval llsyst betwee n sampled units and a rando m start point between 1 a nd llsyst, and takes uni ts at Ra ndom samples s hould be defined using rando m num ber gene ration software or random number tables.
Manua l a rbitrary choice is not equivalent to ra ndom selection. Simple ra ndom sampling is most appropriate w he n there is no k nown ordering, or for small batches in w hi ch a la rge proportion of the units is used for homogeneity s tudy. Systematic sampling may be applied when there is little risk of overlooking repetitive effects or tre nd s in the batch. The sampling scheme s hould t ake into cons ide ration potentia l weak nesses in the method of preparing a nd storing sa mples including, for example, processing a nd fill trend s and possible settling on storage prior to subdivis ion , thus a llowing a critical examination of the prepared batch.
Units should normally be numbered before sampling, or numbe red in processing order after sa mpling, to permit subsequent trend analysis. Alternative sa mpling sche mes may be used where it can be shown that the resulting va riance estimates are not biased by the sampling scheme chosen.
The measure ment procedure chosen to make measurements for homogeneity studies s hould be chosen primarily for good precis ion during the expected duration of each measure me nt run that is, a good repeatability s tandard deviation,sr and, if units are to be randomized among several runs, good between The s tandard deviation for measurements should be small compared with the expected uncertainty for the value for each prope rty meas ured; ideally, the repeatability sta nda rd deviation for the homogeneity study procedure should be less than one third of the desired st a nda rd uncertainty.
That is, if the target meas ureme nt uncertainty for the property value is lltrg expressed as standard uncertainty , the repeatability standa rd deviation for the procedure should ideally comply wi th.
Stati stical power ca lculations see L Z ca n be of u se in choosing ' a suitable numb er of units and repli cates t o give a n experim ental desig n th at provides similar ass ura nce to that ' obtained when the precision requirements in EillJJn! NOTE 2 An additiona l co nsidera tion in the choice of a measurement procedure is the a mount a nd compl ex ity U of sampl e pre paration ne eded to ge t each specimen in to meas urable fo rm.
NOTE 2 Irres pective of the study design, the outcome is on ly mea ningful if the standa rd dev iation of measurement results over the study time scale, possibly in conjunction w ith the between-un it homogeneity, is sufficiently s mall. If For isochronous studies see 8. For studies which might be affected by run-to-run variations in measu remen t, such as the simple cla ssical design at a s ingle storage condition, mea sure me nt procedures s hould be selected primarily for good inte rme diate precision.
A range of designs for the experimental study of sta bility is g iven in. Alternative designs may be used where their effectiveness ca n be demonstra ted. In particu lar, uncertainties in predicted degradation depend both on t he s tudy dura tion and the number of replicates.
In deciding the number of re plicates, the number of diffe rent exposure times, a nd the numbe r and ra nge of different sets of exposure condit ions, co nsideration shou ld be given to: r the precis ion avai lable from the measurement procedure chosen for the study; very precise meas urement procedures require low levels of replication;.
Allow ing a minimum of two RM units for each combin ation of time and temp erat ure or other conditions provides fo r redunda ncy and helps to avoid miss ing sets of conditions;. Long monitoring intervals require either le ngthy s tability studies or highe r replication to provide reliable predictions of deg radation;. To provide some check on linearity, a min imum of t hree obse rvation times is essential in an isochronous study; fo r non-isochronou s s tudies, w he re run effects may be impor ta nt, a minimum of four points in tim e, wit h replication, is require d;.
Studies should include but need not be limited to exposure unde r the proposed condit ions of storage;. For mate rial s with littl e prior information available, the numbe r of units and re plicates used should, in addition to the conside rations above, be sufficient to provide s ma ll uncertainties fo r pre dicted degradation.
The preferred nature of replication depends on the principa l sources of varia tion, as follows. Whe re meas urement variation over time in a classica l stability study is important, the number of points in time should be increased. Th is is usually achieved by ensuring ra ndom a llocatio n of uni ts to treatments. NOTE 2 Exposure ti mes in a stabi lity s tudy a re not necessa rily e qually s paced. Data trea tment for s tability studies should take account of the particular study objective, the m experime nta l design used, and the sources of variation that might affect the r esu lts.
OJ For most basic st a bility s tudies, the objective is either to test for any important change over time in 1 s torage or to estima te the rate of change of property va lues over time. In studies that exam ine the effect of si ngle or multiple storage conditions. S above , data a nalysis is normally intended to provide a test for s ignifica nt e ffe ct s.
Some des ign s includ e elements of both of these objectives. The experime nta l design used will a ffect the da ta analys is options.
A des ign that follows a property value over time at a single set of s torage conditions is typica lly assessed using modelling such as linea r regression,. An accelerated s tudy, which follows a material over time at several storage conditions, can require treatment using a more general model tha t allows estimation of differe nt rates of change under different conditions for example, the temperature dependence of rates of degra dation.
The sources of random variation that affect the resu lt a lso affect data analys is. For t he simplest des ig n, in which only a s ingle source of random va ria tion is present, si mple linear regression as descri bed in fLS For studies involving measurements at different times, and in wh ich t he measurem ent system might show run-to-run variation in a ddition to w ithin-run variation, data a na lysis s hould be chosen to a llow for the additiona l source s of ra ndom variation.
T he following s ubcla uses describe the a na lysis of s ta bility study data. This s ubclause applies to a s ituation where one or more measurements a re taken at each of several points in time, and the ra ndom errors in each measurement are independent a nd share the sa me standard deviation a nd di s tribution that is, errors a re independent a nd ide ntically distributed.
NOTE 1 The ass umption of independence is not valid when the meas ureme nts are taken at d iffere nt times a nd th e inter mediate precision standard deviation of th e measurement system is greater than the r epeatability standard deviation.
See NOTE 2 If the precision cha nges sign ificantly from one time to a nother, the assu mption of identica l distribution is not va lid a nd this clause does not apply. Changes in precision ca n be detected by, fo r example, application of test s for homogeneity of variance or by inspection of r esidua l plots.
Fit a prelimina ry model a nd inspect the fit and model residuals, checking any assumptions made see 8. If the ass umptions apply, record the model para me ters us ually slope and intercept and their uncertainties and check the st a tis tical s ig nifica nce of a ny tre nd found see 8. NOTE Essentially a ll linear regression softwa re, including comme rcia l spreadsheet applications, ca rries out th ese calc ul ations automa tica lly.
ISO requires that soft ware, including any w ritten by the RM producer for this purpose, h e validated prior to us e. For stability assessment vvhere the underlying kinetic mecha n ism is unknow n a nd cha nges a re u expected to be s mall, a linea r a pproxima tion is us ually a suitable model. The s imple li near relationship is described in 8. In cases whe re a well-defi ned nonlinear mechanis m is the reason for the ins ta bility, the corresponding degradat ion model is to be prefe rred over the e mpirical linear model.
The mathe matics is somewhat more complex for models othe r than the straight line, but the evalu ation runs in the same fashion. EXAMPLE An RM containing a ra dioactive isotope is a n exa mple of a property wi th a well-defi ned k ine tic mechanism, in t his case a rndioactive decay, which ca n be predic ted by a we ll- know n but nonli near mode l.
The regression para me ters can be computed us ing t he procedures in.! The regress ion results should be inspected a nd the ass umptions checked as indicated in.!
Where serious departure from the assumptions is found, data analysis shou ld be discontinued and any anomalies should be resolved or alternative data treatment adopted. Outlying data points should be inspected, and if appropriate, removed. However, outliers can also be due to incorrect choice of degradation model or to RM units that have degraded individually; due care s hould accordingly be taken to cons ider possible causes before removing any observations.
Individual outlying observations among replicates on the same unit in a stability study are, if the material has already been shown to be homogeneous, likely to indicate measu rement failure. If Evidence of curvature may be addressed using a different model or since visible cha nge implies ro instability by concluding that the material is not sufficiently sta ble for the intended use.
Q If the trend is statistica lly significant, it is also useful to consider whether it is technically s ignificant; tha t is, whether it is sufficient to require a n increase of uncertainty in a certified value or to prevent ro certification. Trends should be considered technically significant if the predicted degradation over the L period of validity of the material is important compared with the standard uncertainty of the property Q value in question.
If a technically significant trend is observed, the provisions of M apply. When all units have been exposed for the intended time, the complete set of units is measured.
This sequence is illustrated schematically in Figu re 6. NOTE In an isochronous study, it is also poss ible to move material from reference condit ions to planned exposu re co nditions to ac hi eve the desired expos ure time. Isochronous: no reference units. A number of units co ' eight, here arc reserved for the study. At time A which may be a t a nom inal zero exposu re time , a set of units are moved to the reference co nditions. Finally, after all units have been ex posed for the desired t ime, a ll units are stored unde r refere nce L condit ions and are the n removed a nd measured simulta neously.
Note that measurements need not be conducted under th e refere nce conditions, provided that they a re carried out w ithin a short period o f time. All meas urements are made in a short period of time, us ually unde r repeatability conditions in a single run. This provides for the best available precision for the study a nd avoids the possibility that longer-term drift in th e measureme nt system might be mistaken for insta bility.
Completing a ll measurements in a single run reduces costs and si mpli fies the schedu ling of laboratory resources. Deferring measurement until the end of the study means that instability is no t identified until after completion of the study. Si mple interpretation of results in th e basic design of 8. If the material cha nges progressively und er the reference conditions for exa mple owing to progressive freezing effects , this ca n either be mistaken for cha nge at the planned storage cond ition s or ca n lead to an incorrect concl usion t hat the material is stable.
Additional ev id ence ca n therefore be needed for reliable inte rpre tation. The s tudy design assumes that there is no adverse effect on tra nsfe rring to, or from, the reference conditions. Not a ll mate rials ca n be placed in conditions that are more effective at pre venting degrada tion th a n the planned. Global distribution of reference materials.
Uncertainly of measurement - Part 3 : guide to the expression of uncertainly in measurement GUM : - Incertitude de mesure - Partie 3 : guide pour l'espression de l'incertitude de mesure GUM : Guidance for the in-house preparation of quality control materials QCMs.
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Brief guidance on the need for commutability assessment is given in the document, but no technical details are provided. A brief introduction for the characterisation of qualitative properties 9. This document is also not applicable to multivariate quantities, such as spectral data.
Check out our FAQs. Check for trend in production order. Check for outlier difference between replicates. Can use interlaboratory nested design. Report minimum sample amount uptake. Do not need to test all properties. Stability — General considerations. Can use information from previous batches of similar material. Should test all properties. Stability — Post-release monitoring. Frequent monitoring can be used when there is little classical or accelerated stability.
No monitoring needed if shelf life is short. Possible change between monitoring points. If there are replacement batches, there. Stability — Short term Stability. Short term Stability includes 2 factors. Should generally test all properties. Stability — Types of Long-term Studies. Classical, or real-time studies. L December World Health Organisation. Retrieved 21 September Categories : Reference standards Measurement Analytical chemistry.