An attempt to predict conformation and fatness in bulls by means of artificial neural networks using weight, age and breed composition information
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Publication date
2015ISSN
1828-051X
Abstract
The present study aimed to predict conformation and fatness grades in bulls based on data available at slaughter (carcass weight, age and breed proportions) by means of counter-propagation artificial neural networks (ANN). For chemometric analysis, 5893 bull carcasses (n=2948 and n=2945 for calibration and testing of models, respectively) were randomly selected from the initial data set (n≈27000; one abattoir, one classifier, three years period). Different ANN models were developed for conformation and fatness by
varying the net size and the number of epochs. Tested net parameters did not have a notable effect on models’ quality. Respecting the tolerance of ±1 subclass between the actual and predicted value (as allowed by European Union legislation for on-spot checks), the matching between the classifier and ANN grading was 73.6 and 64.9% for conformation and fatness, respectively. Success rate of prediction was
positively related to the frequency of carcasses in the class.
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
663/664 - Food and nutrition. Enology. Oils. Fat
Pages
8
Publisher
Taylor & Francis Open Access
Is part of
Italian Journal of Animal Science
Citation
Čandek-Potokar, Marjeta, Maja Prevolnik, Martin Škrlep, Maria Font-i-Furnols, and Marjana Novič. 2015. "An Attempt to Predict Conformation and Fatness in Bulls by Means of Artificial Neural Networks Using Weight, Age and Breed Composition Information." Italian Journal of Animal Science 14, no. 1: 3198 https://www.tandfonline.com/doi/full/10.4081/ijas.2015.3198.
Grant agreement number
EC/COST-ACTIONS/FA1102/EU/Optimising and standardising non-destructive imaging and spectroscopic methods to improve the determination of body composition and meat quality in farm animals/FAIM
Program
Qualitat i Tecnologia Alimentària
This item appears in the following Collection(s)
- ARTICLES CIENTÍFICS [2503]
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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/