Comparación de modelos matemáticos : una aplicación en la evaluación de alimentos para animales

ABSTRACT: The digestibility and degradation rates of food can be estimated through the in vitro gas production technique. The gas curves generated can be described by diverse mathematical models (exponential, logistic, and empirical). The objective of this work was to present some mathematical model...

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Autores:
Posada Ochoa, Sandra Lucía
Rosero Noguera, Jaime Ricardo
Tipo de recurso:
Article of investigation
Fecha de publicación:
2007
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
spa
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/7830
Acceso en línea:
http://hdl.handle.net/10495/7830
Palabra clave:
Alimentos para animales
Digestibilidad in vitro
Modelos matemáticos
Producción de gas
Rights
openAccess
License
Atribución-NoComercial-CompartirIgual 2.5 Colombia (CC BY-NC-SA 2.5 CO)
Description
Summary:ABSTRACT: The digestibility and degradation rates of food can be estimated through the in vitro gas production technique. The gas curves generated can be described by diverse mathematical models (exponential, logistic, and empirical). The objective of this work was to present some mathematical models commonly used to describe gas production curves and to review some statistical tools useful to evaluate their adjustment capacity. Two models, either a logistic or an empirical proposed by Schofield et al, and France et al, respectively, were used to fit the profiles of gas production of six forage species. The selected criteria for evaluation of their adjustment capacity were: 1) square means error (CME), 2) Akaike (AIC) or 3) Bayesian(BIC) information criteria, 4) coefficient of determination (R2), 5) residual analysis, and 6) Durban-Watson dosim (DW). The best models for evaluation of gas production are those that present the best balance between data adjustment capacity and biological coherence, being necessary their evaluation under the most varied experimental conditions, in order to choose the best model for each specific situation.