ASTM F2340-04

Standard Specification for Developing and Validating Prediction Equation(s) or Model(s) Used in Connection with Livestock, Meat, and Poultry Evaluation Device(s) or System(s) to Determine Value

SCOPE
1.1 This specification covers methods to collect and analyze data, document the results, and make predictions by any objective method for any characteristic used to determine value in any species using livestock, meat, and poultry evaluation devices or systems.
This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory requirements prior to use.

General Information

• Status: Historical
• Publication Date: 30-Apr-2004
• ICS: 65.020.30 - Animal husbandry and breeding
• Technical Committee: F10 - Livestock, Meat, and Poultry Evaluation Systems
• Drafting Committee: F10.40 - Predictive Accuracy

Relations

• Replaced By: ASTM F2340-05 - Standard Specification for Developing and Validating Prediction Equation(s) or Model(s) Used in Connection with Livestock, Meat, and Poultry Evaluation Device(s) or System(s) to Determine Value
• Effective Date: 01-Dec-2005
• Referred By: ASTM F2463-15(2021) - Standard Terminology for Livestock, Meat, and Poultry Evaluation Systems
• Effective Date: 01-May-2004

Standards Content (Sample)

NOTICE: This standard has either been superseded and replaced by a new version or withdrawn.
Contact ASTM International (www.astm.org) for the latest information
Designation: F 2340 – 04
Standard Specification for
Developing and Validating Prediction Equation(s) or
Model(s) Used in Connection with Livestock, Meat, and
Poultry Evaluation Device(s) or System(s) to Determine
Value
This standard is issued under the fixed designation F2340; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision.Anumber in parentheses indicates the year of last reapproval.A
superscript epsilon (e) indicates an editorial change since the last revision or reapproval.
1. Scope n is the sample size for the calibration data set, and k is the
c
number of explanatory variables in the prediction equation.
1.1 Thisspecificationcoversmethodstocollectandanalyze
data, document the results, and make predictions by any
~y 2 yˆ!
(
objectivemethodforanycharacteristicusedtodeterminevalue Œ
n 2 ~k 11!
c
in any species using livestock, meat, and poultry evaluation
2.1.7 root mean square error for validation, n—square root
devices or systems.
of the sum of squared residuals divided by n , where n is the
y y
1.2 This standard does not purport to address all of the
sample size for the validation data set.
safety concerns, if any, associated with its use. It is the
responsibility of the user of this standard to establish appro-
~y 2 yˆ!
(
Œ
priate safety and health practices and determine the applica-
n
v
bility of regulatory requirements prior to use.
2.1.8 validation data set, n—the data set used to test the
predictive accuracy of the equations developed from the
2. Terminology
calibration data set.
2.1 Definitions:
2.1.9 value, commerce, n—measure of economic worth in
2.1.1 calibration data set, n—data set used to develop the
commerce.
initial prediction equations; same as developmental or predic-
tion data set.
3. Significance and Use
2.1.2 coeffıcient of determination, n—percentage of vari-
3.1 Theproceduresinthisspecificationaretobeusedbyall
ability in the response (dependent) variable that can be
parties interested in predicting composition or quality, or both,
explained by the prediction equation.
for the purpose of establishing value based upon device or
system measurements.Whenever new prediction equations are
~y 2 yˆ!
(
R 51–
established, or when a change is experienced that could affect
y 2 y
~ !
(
the performance of existing equations, these procedures shall
2.1.3 evaluation device, n—equipment designed to measure
be used.
composition,orqualityconstituentsusedtodeterminevalueof
live animals, carcasses, and individual cuts of meat.
4. Procedure
2.1.4 evaluation system, n—deviceorgroupofdevicesused
4.1 Experimental Design:
to measure and record composition, or quality constituents
4.1.1 Define the Population for Development of a Prediction
used to determine value of live animals, carcasses, and
Equation:
individual cuts of meat.
4.1.1.1 To establish the predictive ability and validity of an
2.1.5 independent third party, n—unbiased person or entity
equation(s) using measures (independent variables) from an
that is a knowledgeable expert for the specific activity.
evaluation device or system, it is necessary to define the
2.1.6 root mean square error for calibration, n—squareroot
population on which the prediction model is intended to be
of the sum of squared residuals divided by n −(k+1), where
c
used.
(1)Thespeciesonwhichmeasurementswillbemademust
be defined.
This specification is under the jurisdiction of ASTM Committee F10 on
Livestock, Meat, and Poultry Evaluation Systems and is the direct responsibility of
Subcommittee F10.40 on Predictive Accuracy.
Current edition approved May 1, 2004. Published May 2004.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.
F2340–04
(2)Thepopulationforscopeofusemustbeclearlydefined. documented and demonstrated. For many of the common
This may include, but is not limited to, factors such as characteristics to be predicted (such as percent lean), there are
geographical location, gender, age, breed type, or any other a number of reference methods commonly accepted within the
factor that may affect the equation accuracy. discipline. Where accepted methods exist, they should be used
(3) The characteristic to be predicted must be clearly and cited. Where accepted methods do not exist, a sound,
defined. science-based process of method development should be fol-
4.1.2 Select a Sample Population for Development of a lowed. Consideration should be given to sources of variation
Prediction Equation: for the measurements and strategies to minimize any bias that
4.1.2.1 Thesamplesizeforthecalibrationdatasetshouldbe may exist.
a minimum 10k, where k is the number of variables in the 4.1.4 Independent Third-Party Consultation:
prediction equation.The sample size for the validation data set
4.1.4.1 After the experimental process has been established
shouldbeatleast20%ofthesizeofthevalidationdataset.For (but before initiation of the sampling), it is recommended that
example, if the prediction equation has five explanatory vari-
the users obtain an independent third-party consultation to
ables, the calibration data set should have at least 50 observa- review the procedures for compliance with the guidelines
tions and the validation set should have at least ten observa-
established in the previous sections. The consultation should
tions.Theserecommendationsareminimal,largersamplesizes
focus on areas such as the number of samples, the sample
are encouraged, keeping in mind that the calibration data set
selectionprotocol,andtheprojectprocedurestoensurethatthe
should be larger than the validation data set.
process will allow the users to determine effectively the
predictive ability and validity of the equation or model.
NOTE 1—The committee may wish to set the minimum for the
4.1.5 Develop the Model or Equation:
calibration data set at 50 or 100 and use the 20% for the validation data
4.1.5.1 Collect data for the calibration (developmental) data
set. Looking at past studies may give you a better idea of where to set the
minimum for the calibration data set.
set and develop the model or equation. Report the value of the
coefficient of determination, R , for the calibration data set.
4.1.2.2 The sample size must be large enough to be repre-
4.1.5.2 Describe the sample used to develop the model or
sentative of the population; otherwise the resultant equation
equation. Calculate the simple statistics (standard deviation,
will not be suitable for use in the population to which the
mean,minimum,andmaximumvalues)ofthedatasetthatwas
equation will be applied.This may require a larger sample size
used to develop the prediction model (calibration data set—for
thantheminimalrequirementin4.1.2.1.Whenpossible,itmay
example, see Table 1).
be useful to refer to existing data sets that describe a particular
4.1.6 Validation of Prediction Models or Equation(s):
population to ensure that the sample includes most of the
4.1.6.1 Objective—To demonstrate the validity of the initial
variation in the population. For example, if one were develop-
predicti
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