ASTM F2340-2005(2010) Standard Specification for Developing and Validating Prediction Equation(s) or Model(s) Used in Connection with Livestock Meat and Poultry Evaluation Device(s.pdf

1、Designation: F2340 05 (Reapproved 2010)Standard Specification forDeveloping and Validating Prediction Equation(s) orModel(s) Used in Connection with Livestock, Meat, andPoultry Evaluation Device(s) or System(s) to DetermineValue1This standard is issued under the fixed designation F2340; the number i

2、mmediately following the designation indicates the year oforiginal adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 T

3、his specification covers methods to collect and analyzedata, document the results, and make predictions by anyobjective method for any characteristic used to determine valuein any species using livestock, meat, and poultry evaluationdevices or systems.1.2 This standard does not purport to address al

4、l of thesafety concerns, if any, associated with its use. It is theresponsibility of the user of this standard to establish appro-priate safety and health practices and determine the applica-bility of regulatory requirements prior to use.2. Referenced Documents2.1 ASTM Standards:2F2463 Terminology f

5、or Livestock, Meat, and PoultryEvaluation Systems3. Terminology3.1 For definitions of terms used in this specification, referto Terminology F2463.3.2 Definitions of Terms Specific to This Standard:3.2.1 accuracy, nstatement of the exactness with which ameasurement approaches the true measure for tha

6、t character-istic; accuracy is contrasted with precision, which is concernedwith the repeatability of the measurements. Therefore, with alarge bias, a measurement may be of high precision, but of lowaccuracy.3.2.2 calibration data set, ndata set used to develop theinitial prediction equations; same

7、as developmental or predic-tion data set.3.2.3 coeffcient of determination, npercentage of vari-ability in the response (dependent) variable that can beexplained by the prediction equation.R25 1(y 2 y!2(y 2 y !23.2.4 root mean square error for calibration, nsquare rootof the sum of squared residuals

8、 divided by nc(k + 1), wherencis the sample size for the calibration data set, and k is thenumber of explanatory variables in the prediction equation.(y 2 y!2nc2 k 1 1!3.2.5 root mean square error for validation, nsquare rootof the sum of squared residuals divided by ny, where nyis thesample size fo

9、r the validation data set.(y 2 y!2nv3.2.6 validation data set, nthe data set used to test thepredictive accuracy of the equations developed from thecalibration data set.3.2.7 value, commerce, nmeasure of economic worth incommerce.4. Significance and Use4.1 The procedures in this specification are to

10、 be used by allparties interested in predicting composition or quality, or both,for the purpose of establishing value based upon device orsystem measurements. Whenever new prediction equations areestablished, or when a change is experienced that could affectthe performance of existing equations, the

11、se procedures shallbe used.5. Procedure5.1 Experimental Design:5.1.1 Define the Population for Development of a PredictionEquation:1This specification is under the jurisdiction of ASTM Committee F10 onLivestock, Meat, and Poultry Evaluation Systems and is the direct responsibility ofSubcommittee F10

12、.40 on Predictive Accuracy.Current edition approved Sept. 1, 2010. Published December 2010. Originallyapproved in 2004. Last previous edition approved in 2005 as F2340 05. DOI:10.1520/F2340-05R10.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at

13、 serviceastm.org. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.5.1.1.1 To establish the predictive ability and va

14、lidity of anequation(s) using measures (independent variables) from anevaluation device or system, it is necessary to define thepopulation on which the prediction model is intended to beused.(1) The species on which measurements will be made mustbe defined.(2) The population for scope of use must be

15、 clearly defined.This may include, but is not limited to, factors such asgeographical location, gender, age, breed type, or any otherfactor that may affect the equation accuracy.(3) The characteristic to be predicted must be clearlydefined.5.1.2 Select a Sample Population for Development of aPredict

16、ion Equation:5.1.2.1 The sample size for the calibration data set must beat a minimum 10k, where k is the number of variables in theprediction equation, or 100 observations, whichever is greater.The sample size for the validation data set must be at least20 % of the size of the calibration validatio

17、n data set. Forexample, if the prediction equation has five explanatory vari-ables, the calibration data set will require a minimum of 100observations and the validation set must have at least 20observations. These are minimal requirements; larger samplesizes are encouraged, keeping in mind that the

18、 calibration dataset must be larger than the validation data set.5.1.2.2 The sample size must be large enough to be repre-sentative of the population; otherwise the resultant equationwill not be suitable for use in the population to which theequation will be applied. This may require a larger sample

19、 sizethan the minimal requirement in 5.1.2.1. When possible, it maybe useful to refer to existing data sets that describe a particularpopulation to ensure that the sample includes most of thevariation in the population. For example, if one were develop-ing an equation to predict yield grade in U.S.

20、fed beef packingplants, one would want to make sure that the samples used todevelop and validate the regression model encompassed mostof the normal variation in yield grade, yield grade factors, andfactors that might affect the accuracy of the model. In thisexample, the simple statistics of these ch

21、aracteristics in thecalibration data sets should be compared to the simple statisticsof these characteristics in references such as the National BeefQuality Audits. Users are encouraged to work with a statisti-cian.5.1.3 Develop an Experimental Process:5.1.3.1 A clearly defined process must be estab

22、lished anddocumented. That process, which includes consistent, repeat-able methods, should be used to obtain the measurementsunder the same conditions in which the device or system wouldbe expected to operate. In particular, the validity of theapproach and the repeatability of the procedure must bed

23、ocumented and demonstrated. For many of the commoncharacteristics to be predicted (such as percent lean), there area number of reference methods commonly accepted within thediscipline. Where accepted methods exist, they should be usedand cited. Where accepted methods do not exist, a sound,science-ba

24、sed process of method development should be fol-lowed. Consideration should be given to sources of variationfor the measurements and strategies to minimize any bias thatmay exist.5.1.4 Independent Third-Party Consultation:5.1.4.1 After the experimental process has been established(but before initiat

25、ion of the sampling), it is recommended thatthe users obtain an independent third-party consultation toreview the procedures for compliance with the guidelinesestablished in the previous sections. The consultation shouldfocus on areas such as the number of samples, the sampleselection protocol, and

26、the project procedures to ensure that theprocess will allow the users to determine effectively thepredictive ability and validity of the equation or model.5.1.5 Develop the Model or Equation:5.1.5.1 Collect data for the calibration (developmental) dataset and develop the model or equation. Report th

27、e value of thecoefficient of determination, R2, for the calibration data set.5.1.5.2 Describe the sample used to develop the model orequation. Calculate the simple statistics (standard deviation,mean, minimum, and maximum values) of the data set that wasused to develop the prediction model (calibrat

28、ion data setforexample, see Table 1).5.1.6 Validation of Prediction Models or Equation(s):5.1.6.1 ObjectiveTo demonstrate the validity of the initialprediction model or equation with a different sample.5.1.6.2 Select a sample for validation of a prediction equa-tion.Ageneral recommendation is for th

29、e size of the validationdata set to be 20 % of the size of the calibration data set.However, the sample must be large and variable enough to berepresentative of the population to which the equation or modelwill be applied (refer to the calibration data set statistics forguidance).5.1.6.3 Collect dat

30、a for the validation data set. In validationtrials, data used to determine predicted values must be col-lected under conditions where the devices or systems will beused or as close to on-line as possible. Prediction equationsmust not be applied to populations whose range of relevantTABLE 1 Simple St

31、atistics of Beef Carcass Characteristics for Calibration Data Set (n = 400)Data Set Characteristic Mean SD Minimum MaximumCalibration Hot carcass weight, kg 351 41 227 460Calibration Marbling scoreA505 106 250 1090Calibration Preliminary yield grade 3.07 0.58 2.1 5.5Calibration Adjusted preliminary

32、yield grade 3.29 0.62 2.0 5.6Calibration Adjustment of preliminary yield grade 0.22 0.22 -0.3 1.1Calibration Kidney, pelvic, and heart fat, % 2.08 0.69 0.0 4.5Calibration Longissimus area, cm290.2 11.3 53.5 135.5Calibration Yield grade 2.65 1.06 -0.5 6.3A200 = Practically Devoid0; 300 = Traces0; 400

33、 = Slight0; 500 = Small0; 600 = Modest0; 700 = Moderate0; 800 = Slightly Abundant0; 900 = Moderately Abundant0; 1000= Abundant0.F2340 05 (2010)2characteristics (independent and dependent variables or factorsthat affect the relationship between independent and dependentvariables) is practically diffe

34、rent from the range of thosecharacteristics in the calibration data set.5.1.6.4 Apply the model or equation from the calibrationdata set to the validation data set and evaluate the differencebetween the predicted and actual values. Report the coefficientof determination, R2, for the validation data

35、set.5.1.6.5 The model or equation would be deemed valid if theroot mean square error for validation is within 20 % of the rootmean square error for calibration. The model or equationwould be deemed invalid if the root mean square error fromvalidation is greater than the root mean square error fromca

36、libration by more than 20 % of the root mean square errorfrom calibration.5.1.6.6 Describe the sample used to validate the model orequation. Calculate the simple statistics (standard deviation,mean, minimum, and maximum values) of the data set. Thesestatistics define the bounds of the population to

37、which themodel or equation can be applied.5.2 Revalidation of Prediction Model(s) or Equation(s):5.2.1 Potential Factors Influencing the Decision to Revali-date:5.2.1.1 One factor is the range of the independent variablesor the prediction of the dependent variable used in thevalidation sample is pra

38、ctically different than the range in thecurrent population (9practically different9 can be defined as tenor more percent of the population measured fall outside therange of independent variables for the validation data set; userscarry the burden of proof to demonstrate no practical changeduring the

39、cumulative preceding 12 month period). Thisevaluation should be conducted at least annually and shouldencompass all relevant observations (observations in which thepredicted value influenced seller payment) in the previous 12month period.5.2.1.2 A second factor is a device or system modification/cha

40、nge that practically affects at least one independent variablethat causes a change that is greater than the establishedtolerance.5.2.1.3 A third factor is a process modification/change thatpractically affects at least one independent variable that causesa change that is greater than the established

41、tolerance.5.2.2 Determination of Appropriate Corrective Action:5.2.2.1 Compare differences in independent variable rangesor the predicted values to determine if a practical reason torevalidate exists.5.2.2.2 Upon identification of a practical reason to revali-date, operators must initiate revalidati

42、on promptly and notifyany appropriate government entity, if required.5.3 Documentation of Results:5.3.1 Records:5.3.1.1 Users must maintain written records of the data,procedures, and test results used to validate or revalidate aprediction equation; these records must be maintained so longas they ar

43、e the basis for the validation of a prediction equationor model in use.5.3.1.2 Parties using prediction equations or models todetermine value of livestock, meat, or poultry have a right toprotect trade secrets, subject to applicable laws.5.3.2 Representations:5.3.2.1 All representations or claims ab

44、out the performanceof a prediction equation or model must use terminology that isconsistent with this standard, supported by available data, andbased upon the procedures outlined above.6. Keywords6.1 coefficient of determination; model; prediction equa-tion; revalidation; root mean square error; sta

45、ndard deviation;validationASTM International takes no position respecting the validity of any patent rights asserted in connection with any item mentionedin this standard. Users of this standard are expressly advised that determination of the validity of any such patent rights, and the riskof infrin

46、gement of such rights, are entirely their own responsibility.This standard is subject to revision at any time by the responsible technical committee and must be reviewed every five years andif not revised, either reapproved or withdrawn. Your comments are invited either for revision of this standard

47、 or for additional standardsand should be addressed to ASTM International Headquarters. Your comments will receive careful consideration at a meeting of theresponsible technical committee, which you may attend. If you feel that your comments have not received a fair hearing you shouldmake your views

48、 known to the ASTM Committee on Standards, at the address shown below.This standard is copyrighted by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959,United States. Individual reprints (single or multiple copies) of this standard may be obtained by contacting ASTM at the aboveaddress or at 610-832-9585 (phone), 610-832-9555 (fax), or serviceastm.org (e-mail); or through the ASTM website(www.astm.org). Permission rights to photocopy the standard may also be secured from the ASTM website (www.astm.org/COPYRIGHT/).F2340 05 (2010)3