# ASTM F2340-05(R2021) pdf free download

ASTM F2340-05(R2021) pdf free download.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

1. 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 appro- priate safety, health, and environmental practices and deter- mine the applicability ofregulatory limitations prior to use. 1.3 This international standard was developed in accor- dance with internationally recognized principles on standard- ization established in the Decision on Principles for the Development of International Standards, Guides and Recom- mendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

5. Procedure

5.1 Experimental Design: 5.1.1 Define the Population forDevelopment ofa Prediction Equation: 5.1.1.1 To establish the predictive ability and validity of an equation(s) using measures (independent variables) from an evaluation device or system, it is necessary to define the population on which the prediction model is intended to be used. (1) The species on which measurements will be made must be defined. (2) The population for scope ofuse must be clearly defined. This may include, but is not limited to, factors such as geographical location, gender, age, breed type, or any other factor that may affect the equation accuracy. (3) The characteristic to be predicted must be clearly defined. 5.1.2 Select a Sample Population for Development of a Prediction Equation: 5.1.2.1 The sample size for the calibration data set must be at a minimum 10k, where k is the number of variables in the prediction equation, or 100 observations, whichever is greater. The sample size for the validation data set must be at least 20 % of the size of the calibration validation data set. For example, if the prediction equation has five explanatory variables, the calibration data set will require a minimum of 100 observations and the validation set must have at least 20 observations. These are minimal requirements; larger sample sizes are encouraged, keeping in mind that the calibration data set 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 equation will not be suitable for use in the population to which the equation will be applied. This may require a larger sample size than the minimal requirement in 5.1.2.1. When possible, it may be useful to refer to existing data sets that describe a particular population to ensure that the sample includes most of the variation in the population. For example, if one were develop- ing an equation to predict yield grade in U.S. fed beef packing plants, one would want to make sure that the samples used to develop and validate the regression model encompassed most of the normal variation in yield grade, yield grade factors, and factors that might affect the accuracy of the model. In this example, the simple statistics of these characteristics in the calibration data sets should be compared to the simple statistics of these characteristics in references such as the National Beef Quality Audits. Users are encouraged to work with a statisti- cian.5.1.3 Develop an Experimental Process—A clearly defined process must be established and documented. That process, which includes consistent, repeatable methods, should be used to obtain the measurements under the same conditions in which the device or system would be expected to operate. In particular, the validity of the approach and the repeatability of the procedure must be documented and demonstrated. For many of the common characteristics to be predicted (such as percent lean), there are a number of reference methods com- monly accepted within the discipline. Where accepted methods exist, they should be used and cited.