ASTM E1345-1998(2003) Standard Practice for Reducing the Effect of Variability of Color Measurement by Use of Multiple Measurements《用多次测量法降低颜色测量变异性的影响》.pdf

《ASTM E1345-1998(2003) Standard Practice for Reducing the Effect of Variability of Color Measurement by Use of Multiple Measurements《用多次测量法降低颜色测量变异性的影响》.pdf》由会员分享，可在线阅读，更多相关《ASTM E1345-1998(2003) Standard Practice for Reducing the Effect of Variability of Color Measurement by Use of Multiple Measurements《用多次测量法降低颜色测量变异性的影响》.pdf（5页珍藏版）》请在麦多课文档分享上搜索。

1、Designation: E 1345 98 (Reapproved 2003)Standard Practice forReducing the Effect of Variability of Color Measurement byUse of Multiple Measurements1This standard is issued under the fixed designation E 1345; the number immediately following the designation indicates the year oforiginal adoption or,

2、in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon (e) indicates an editorial change since the last revision or reapproval.INTRODUCTIONRecent improvements in the precision and bias of color-measuring instruments hav

3、e beenaccompanied by more widespread use of numerical color tolerances based on instrumental measure-ments. As tighter tolerances are specified, they begin to approach the limits of visual perception. Inmany cases, the instrument user has found it difficult to prepare and measure specimens with adeq

4、uaterepeatability. This practice provides procedures for reducing variability in the mean results of colormeasurement by the use of multiple measurements, and it indicates how many measurements arerequired for a specific reduction.1. Scope1.1 Reduction of the variability associated with averagecolor

5、 or color-difference measurements of object-color speci-mens is achieved by statistical analysis of the results ofmultiple measurements on a single specimen, or by measure-ment of multiple specimens, whichever is appropriate.1.2 This practice provides a means for the determination ofthe number of me

6、asurements required to reduce the variabilityto a predetermined fraction of the relevant color or color-difference tolerances.1.3 This practice is general in scope rather than specific asto instrument or material.2. Referenced Documents2.1 ASTM Standards:2D 2244 Practice for Calculation of Color Dif

7、ferences fromInstrumentally Measured Color CoordinatesD 3134 Practice for Establishing Retroreflectance Color andGloss TolerancesE 178 Practice for Dealing With Outlying ObservationsE 284 Terminology of AppearanceE 308 Practice for Computing the Colors of Objects byUsing the CIE SystemE 456 Terminol

8、ogy Relating to Quality and StatisticsE 1164 Practice for Obtaining Spectrophotometric Data forObject-Color Evaluation2.2 Other Standard:SAE J 1545 Recommended Practice for Instrumental ColorDifference Measurement for Exterior Finishes, Textilesand Colored Trim33. Terminology3.1 Definitions of appea

9、rance terms in Terminology E 284or statistical terms in Terminology E 456 are applicable to thispractice.3.2 Definitions of Terms Specific to This Standard:3.2.1 box and whisker plot, na nonparmetric data analysisdiagram that illustrates the 25, 50, and 75 % cumulativedistribution of values in a dat

10、a set (the box) and the expectedrange of values, defined by distance outside the box ends; seewhiskers, see Fig. 1.3.2.2 extreme value, na single reading, selected from aseries of readings, whose value is farther from the nearer boxend than 3.0 times the hinge length.3.2.2.1 DiscussionA box and whis

11、kers plot is normallyused to find outliers and extreme values. Such values should beeliminated from a series before calculating the series mean,standard deviation, and confidence intervals.3.2.3 hinges, nthe 25 and 75 % cumulative distributionpoints in a set of readings taken during a measurement.3.

12、2.3.1 DiscussionHinges represent the values in which25 % of the readings are less than the lower hinge and 75 % ofthe readings are less than the upper hinge. See also hingelength.1This practice is under the jurisdiction of ASTM Committee E12 on Color andAppearance and is the direct responsibility of

13、 Subcommittee E12.04 on Color andAppearance Analysis.Current edition approved Dec. 1, 2003. Published December 2003. Originallyapproved in 1990. Last previous edition approved in 1998 as E 1345 - 98.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service

14、 at serviceastm.org. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.3Available from Society of Automotive Engineers (SAE), 400 CommonwealthDr., Warrendale, PA 15096-0001.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box

15、 C700, West Conshohocken, PA 19428-2959, United States.3.2.3.2 DiscussionHinges are sometimes called the lower(Q1) and upper (Q1) quartile values.3.2.4 hinge length, H, nthe range of values between thelower and upper hinges.3.2.4.1 DiscussionThe hinge length is sometimes calledthe box width or the i

16、nterquartile range Q3to Q1.3.2.5 outlier, na single reading, selected from a series ofreadings, whose value is further from the nearer box end then1.5 times the hinge length; see 3.2.2.1.3.2.6 sampling number, N, nnumber of multiple measure-ments, or number of multiple specimens, required to reduce

17、thevariability of color or color-difference measurement to adesired level.3.2.7 standard deviation of color or color-difference mea-surement, sstandard deviation of the color scale or color-difference-scale value, xi, being considered:s 5 $(xi2 xavg!2%/n 2 1!#0.5(1)where:xavg=( xi)/n, andn = the num

18、ber of replicate measurements made.3.2.8 standard deviation of instrument, si, nstandard de-viation of a color-scale or color-difference-scale value due toinstrument variability alone:si5 $(xi2 xavg!2%/n 2 1!#0.5(2)3.2.9 standard error of the estimated mean, se, nstandarddeviation of color or color-

19、difference measurement divided bythe square root of the sampling number:se5 s/N0.5! (3)3.2.10 standard error goal, se,g, nlevel to which thestandard error of the estimated mean is to be reduced.3.2.11 tolerance, nthe upper tolerance limit minus thelower tolerance limit; the total allowable range of

20、the color-scale or color-difference-scale value considered.3.2.12 whiskers, nlines extending out from the box endsto the largest and smallest observations lying within 1.5 timesthe hinge length, measured from the box ends.4. Summary of Practice4.1 This practice assumes that, for the material underco

21、nsideration and a specified set of color scales, relevant coloror color-difference tolerances have been established (see Prac-tice D 3134).4.2 For convenience, the numerical example in the Appen-dix uses CIELAB LCH (lightness, chroma, hue) color differ-ence scales DL*, DC*ab, and DH*ab(see Practice

22、D 2244 andPractice E 308), but this is not meant to be restrictive.NOTE 1Some coordinates, such as CIE x, y, Y, do not follow thetheories of this standard due to excessive colinearity. While it has not beentested, this same colinearity problem may also be observed in 1960 u, vand 1976 u8,v8 coordina

23、tes. Table 1 provides a listing of the appropriateand inappropriate color coordinates for use with this practice.4.3 The successive steps in the procedure are as follows:4.3.1 Determine the standard deviation of instrument.4.3.1.1 Screen the measurement data for outliers and ex-treme values.4.3.2 De

24、termine the standard deviation of color or color-difference measurement.4.3.2.1 Screen the measurement data for outliers and ex-treme values.4.3.3 Determine the standard error of the estimated mean fora sampling number of one.4.3.4 Determine the final sampling number that reduces thestandard error o

25、f the estimated mean to less than the standarderror goal for each scale value.4.3.5 Determine the final standard error goal values.NOTE 2When the standard error of the estimated mean for a samplingnumber of one is larger than a specified fraction of the tolerance or aspecified multiple of the standa

26、rd deviation of instrument for any of thethree color-difference-scale values, a sampling number greater than one isrequired.4.4 Screening for and Elimination of Outliers and ExtremeValues in Measured Data:4.4.1 Box and whisker testThis test is best carried out bycomputer. Many programs for the box a

27、nd whisker techniqueare available.44See for example, Schaefer, R. L. and Anderson, R. B., The Student Edition ofMinitab, Addison-Wesley, New York, 1989.FIG. 1 Schematic Description of a Box and Whisker PlotTABLE 1 Appropriate and Inappropriate Color Coordinates forUse in This PracticeColor Coordinat

28、es Appropriate InappropriateCIE Yxy =CIE LCH =CIE LAB =CIE LUV =CIE Lu8v8 =E 1345 98 (2003)24.4.1.1 Order the readings from lowest to highest value. Thereading whose value is half way between the minimum andmaximum values is the median. Fig. 1 illustrates the followingsteps.4.4.1.2 The reading whose

29、 value is just less than 75 % of theother readings is the lower hinge. The readings whose value isjust higher than 75 % of the other readings is the upper hinge.The difference between these two is the hinge length H.4.4.1.3 If the smallest value of any reading is less than thelower hinge value minus

30、 1.5 times the hinge length, it may beconsidered an outlier. Likewise, if the largest value of anyreading is greater than the upper hinge value plus 1.5 times thehinge length, it may be considered an outlier.4.4.1.4 If the smallest (largest) value of any reading is less(greater) than the lower (uppe

31、r) hinge value minus (plus) 3.0times the hinge length, it may be considered an extreme value.4.4.2 Practice E 178 ProcedureThe test for outliers inPractice E 178 is constructed from the sample mean Xavg, andthe standard deviation s.4.4.2.1 Order the readings from lowest to highest value.4.4.2.2 Calc

32、ulate the following two statistics, T1for thelowest value, and Tnfor the highest value in a set of n orderedreadings as follows:Tl5xavg xl!s(4)Tn5xn xavg!S(5)4.4.2.3 Compare the values of Tl(Tn) to critical values inTable 2.IfTl(Tn) is larger than the critical value for n readingsat the 1 % level of

33、 significance. Reading 1 (n) may beconsidered an outlier.NOTE 3Table 2 contains critical values for series of up to 15 readingsand for 0.1 and 1 % significance levels. For other significance levels andlarger datasets, see Table 1 of Practice E 178.4.4.2.4 If Tl(Tn) is larger than the critical value

34、for nreadings at the 1 % level of significance, Readings 1 (n) may beconsidered an extreme value.4.4.3 If any outliers or extreme values were found, considercarefully whether they should be dropped or retained. Dropthose readings not considered to be part of the desired dataset,by whatever consisten

35、t criteria are accepted. See 5.3.4.4.4 Recalculate the mean, standard deviation and confi-dence limits of the remaining dataset.5. Significance and Use5.1 This practice should be used whenever measured color-scale or color-difference-scale values are to be compared to anestablished tolerance. In thi

36、s way it can be demonstratedquantitatively that the sampling and measurement proceduresare adequate to allow an unambiguous decision as to whetheror not the mean results are within tolerance.5.2 This practice is based on portions of SAE PracticeJ 1545, as it applies to painted or plastic automotive

37、parts. It isgenerally applicable to object colors in various materials.Textured materials, such as textiles, may require specialconsideration (see SAE Practice J 1545 and STP 15D Manualon Presentation of Data and Control Chart Analysis5).5.3 While Practice E 178 deals with outliers, it does notinclu

38、de definitions relating to the box and whisker technique.The definition of an outlier is operational and a little vaguebecause there is still considerable disagreement about whatconstitutes an outlier. In any normally distributed population,there will be members that range from minus to plus infinit

39、y.Theoretically, one should include any member of the popula-tion in any sample based on estimates of the populationparameters. Practically, including a member that is found farfrom the mean within a small sample, most members of whichare found near the mean, will introduce a systematic bias intothe

40、 estimate of the population parameters (mean, standarddeviation, standard error). Such a bias is in direct contrast withthe goal of this practice, namely, to reduce the effects ofvariability of measurement. For the purposes of this practice,no distinction is made between errors of sampling and mem-b

41、ers of the tails of the distribution. Practice E 178 has severalmethods and significance tables to attempt to differentiatebetween these two types of extreme values.6. Procedure6.1 Determine the standard deviation of instrument, si,bycarrying out the appropriate color measurement at least 10times (n

42、 = 10) when using a stable product standard as thespecimen, without removing or disturbing the specimen be-tween measurements. Calculate siby the use of Eq 2. Thisdetermination should be carried out for each color scale usedand for each product with a new color; however, siis unlikelyto change appre

43、ciably over relatively extended periods.6.1.1 Screen the measurement data for outliers and extremevalues following 4.4.1-4.4.4.6.2 Select maximum allowable values of the standard errorof the estimated mean, as a fraction of the tolerance and as amultiple of the standard deviation of instrument. In t

44、he absenceof specified values of these quantities, use those recommendedin SAE Practice J 1545: 0.1 times the tolerance and 2si. Thesevalues are used in the Appendix.NOTE 4This practice assumes that all measurements are subject to thecentral limit theorem of mathematical statistics, so that as the n

45、umber of5Available fromASTM International Headquarters 100 Barr Harbor Drive, WestConshohocken, PA 19428.TABLE 2 Official Values for T (One-Sided Test) for OutliersNumber ofObservationsnUpper 0.1 %SignificanceLevelUpper 1.0 %SignificanceLevel3 1.155 1.1554 1.499 1.4925 1.780 1.7496 2.011 1.9447 2.20

46、1 2.0978 2.358 2.2219 2.492 2.32310 2.606 2.41011 2.705 2.48512 2.791 2.55013 2.867 2.60714 2.935 2.65915 2.997 2.705E 1345 98 (2003)3replicate or repeat measurements becomes large, the distribution of valuesis described by the standard normal distribution. It has been shown,6,7however, that average

47、s of large numbers of measurements of a verificationstandard on a properly maintained spectrophotometer are not approxi-mated by the standard normal distribution. As a result, tests anchored to simay exhibit a significance or a power dependence different from thatwhich is expected.6.3 Determine the

48、standard deviation of color or color-difference measurement, s, by making the appropriate measure-ment at least 10 times (n = 10), as follows:6.3.1 To assess the variability within a single specimen,measure the same specimen at ten or more randomly selecteddifferent areas of the specimen.6.3.1.1 Scr

49、een the measurement data for outliers and ex-treme values following 4.4.1-4.4.4.6.3.2 To assess the variability among specimens, measure atleast ten replicate specimens.6.3.2.1 Screen the measurement data for outliers and ex-treme values following 4.4.1-4.4.4.6.4 Determine the standard error of the estimated mean, se,for a sampling number of one, using Eq 3. Note that for N =1,se= s. Use the larger of the values of s determined in 6.3.1 or6.3.2.6.5 Compare the value of seto 0.1 times the tolerance and to2sifor each of the three color or color-differe