ASTM F2340-05(2010)

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

ABSTRACT
This specification covers the standard procedures used to collect and analyze data, document the results, and make predictions for any characteristic used to quantify the value of any livestock, meat, and poultry species as measured by appropriate evaluation devices or systems. The procedures described here shall be used particularly when new prediction equations or models are established, or when a change is experienced that could affect the performance of existing equations.
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.
1.2 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: 31-Aug-2010
• ICS: 65.020.30 - Animal husbandry and breeding
• Technical Committee: F10 - Livestock, Meat, and Poultry Evaluation Systems
• Drafting Committee: F10.40 - Predictive Accuracy

Relations

• Replaces: 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-Sep-2010
• Referred By: ASTM F2463-15(2021) - Standard Terminology for Livestock, Meat, and Poultry Evaluation Systems
• Effective Date: 01-Sep-2010

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NOTICE: This standard has either been superseded and replaced by a new version or withdrawn.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:F2340 −05(Reapproved 2010)
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 (´) indicates an editorial change since the last revision or reapproval.
1. Scope 3.2.3 coeffıcient of determination, n—percentageofvariabil-
ity in the response (dependent) variable that can be explained
1.1 Thisspecificationcoversmethodstocollectandanalyze
by the prediction equation.
data, document the results, and make predictions by any
objectivemethodforanycharacteristicusedtodeterminevalue
~y 2 yˆ!
(
R 51 2
in any species using livestock, meat, and poultry evaluation 2
y 2 y¯
~ !
(
devices or systems.
3.2.4 root mean square error for calibration, n—squareroot
1.2 This standard does not purport to address all of the
of the sum of squared residuals divided by n −(k+1), where
c
safety concerns, if any, associated with its use. It is the
n is the sample size for the calibration data set, and k is the
c
responsibility of the user of this standard to establish appro-
number of explanatory variables in the prediction equation.
priate safety and health practices and determine the applica-
bility of regulatory requirements prior to use. ~y 2 yˆ!
(
Œ
n 2 k11
~ !
c
2. Referenced Documents
3.2.5 root mean square error for validation, n—square root
2.1 ASTM Standards:
of the sum of squared residuals divided by n , where n is the
y y
F2463Terminology for Livestock, Meat, and Poultry Evalu-
sample size for the validation data set.
ation Systems
y 2 yˆ
~ !
(
3. Terminology
Œ
n
v
3.1 For definitions of terms used in this specification, refer
3.2.6 validation data set, n—the data set used to test the
to Terminology F2463.
predictive accuracy of the equations developed from the
3.2 Definitions of Terms Specific to This Standard:
calibration data set.
3.2.1 accuracy, n—statement of the exactness with which a
3.2.7 value, commerce, n—measure of economic worth in
measurement approaches the true measure for that character-
commerce.
istic;accuracyiscontrastedwithprecision,whichisconcerned
with the repeatability of the measurements. Therefore, with a 4. Significance and Use
largebias,ameasurementmaybeofhighprecision,butoflow
4.1 Theproceduresinthisspecificationaretobeusedbyall
accuracy.
parties interested in predicting composition or quality, or both,
3.2.2 calibration data set, n—data set used to develop the
for the purpose of establishing value based upon device or
initial prediction equations; same as developmental or predic-
system measurements.Whenever new prediction equations are
tion data set.
established, or when a change is experienced that could affect
the performance of existing equations, these procedures shall
This specification is under the jurisdiction of ASTM Committee F10 on be used.
Livestock, Meat, and Poultry Evaluation Systems and is the direct responsibility of
Subcommittee F10.40 on Predictive Accuracy.
5. Procedure
Current edition approved Sept. 1, 2010. Published December 2010. Originally
5.1 Experimental Design:
approved in 2004. Last previous edition approved in 2005 as F2340–05. DOI:
10.1520/F2340-05R10.
5.1.1 Define the Population for Development of a Prediction
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
Equation:
contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM
5.1.1.1 To establish the predictive ability and validity of an
Standards volume information, refer to the standard’s Document Summary page on
the ASTM website. equation(s) using measures (independent variables) from an
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
F2340−05 (2010)
evaluation device or system, it is necessary to define the underthesameconditionsinwhichthedeviceorsystemwould
population on which the prediction model is intended to be be expected to operate. In particular, the validity of the
used. approach and the repeatability of the procedure must be
(1)Thespeciesonwhichmeasurementswillbemademust documented and demonstrated. For many of the common
be defined. characteristics to be predicted (such as percent lean), there are
(2)Thepopulationforscopeofusemustbeclearlydefined. a number of reference methods commonly accepted within the
This may include, but is not limited to, factors such as discipline.Where accepted methods exist, they should be used
geographical location, gender, age, breed type, or any other and cited. Where accepted methods do not exist, a sound,
factor that may affect the equation accuracy. science-based process of method development should be fol-
(3)The characteristic to be predicted must be clearly lowed. Consideration should be given to sources of variation
defined. for the measurements and strategies to minimize any bias that
5.1.2 Select a Sample Population for Development of a may exist.
Prediction Equation: 5.1.4 Independent Third-Party Consultation:
5.1.2.1 The sample size for the calibration data set must be 5.1.4.1 After the experimental process has been established
at a minimum 10k, where k is the number of variables in the (but before initiation of the sampling), it is recommended that
prediction equation, or 100 observations, whichever is greater. the users obtain an independent third-party consultation to
The sample size for the validation data set must be at least review the procedures for compliance with the guidelines
20% of the size of the calibration validation data set. For established in the previous sections. The consultation should
example, if the prediction equation has five explanatory focus on areas such as the number of samples, the sample
variables, the calibration data set will require a minimum of selectionprotocol,andtheprojectprocedurestoensurethatthe
100 observations and the validation set must have at least 20 process will allow the users to determine effectively the
observations. These are minimal requirements; larger sample predictive ability and validity of the equation or model.
sizes are encouraged, keeping in mind that the calibration data 5.1.5 Develop the Model or Equation:
set must be larger than the validation data set. 5.1.5.1 Collectdataforthecalibration(developmental)data
5.1.2.2 The sample size must be large enough to be repre- set and develop the model or equation. Report the value of the
sentative of the population; otherwise the resultant equation coefficient of determination, R , for the calibration data set.
will not be suitable for use in the population to which the 5.1.5.2 Describe the sample used to develop the model or
equationwillbeapplied.Thismayrequirealargersamplesize equation. Calculate the simple statistics (standard deviation,
thantheminimalrequirementin5.1.2.1.Whenpossible,itmay mean,minimum,andmaximumvalues)ofthedatasetthatwas
be useful to refer to existing data sets that describe a particular used to develop the prediction model (calibration data set—for
population to ensure that the
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