Utilize este identificador para referenciar este registo: http://hdl.handle.net/10198/2923
Título: Subcutaneous fat depth magnitude influences its measurement errors: a simulation study
Autor: Cadavez, Vasco
Rufino, José
Fonseca, António
Palavras-chave: Carcass
Errors
lambs
Lean
Meat
Data: 2010
Editora: EUROSIS-ETI. Escola Superior Agrária de Bragança, CIMO
Citação: Cadavez, Vasco; Amaro, Rufino; Fonseca, António (2010) - Subcutaneous fat depth magnitude influences its measurement errors: a simulation study. In FOODSIM'2010 In 6th International Conference on Simulation and Modelling in the Food and Bio-Industry. Bragança: CIMO. p. 118-121. ISBN 9789077381561.
Resumo: The objectives of this study were to evaluate the impact of proportional and absolute errors on subcutaneous fat depth (SFD) measurements, and the effects on the stability of models to predict the lean meat proportion (LMP) of lamb carcasses. Ninety eight lambs (72 males and 26 females) of Churra Galega Bragançana breed were slaughtered, and carcasses were weighed (HCW) approximately 30 min after exsanguination. During carcasses quartering a caliper was used to perform SFD measurements, over the maximum depth of longissimus muscle (LM), between the 12th and 13th ribs (C12), and between the 3rd and 4th lumbar vertebrae (C3). A computer program was written in order to simulate measurement errors for C12 and C3 measurements. Two scenarios were simulated, and C12 and C3 were contaminated with: 1) proportional errors of 5, 10, and 15%, and 2) absolute errors of 0.25, 0.50, and 0.75 mm. Simple and multiple linear models to predict LMP were developed using as independent variables: 1) the measured (original) SFD measurements, and 2) the biased SFD measurements. The coefficient of determination ( ) and the residual SD (RSD) were computed. Our study demonstrates that measurement errors can have a high impact on the SFD measurements, and on models stability. We conclude that SFD measurements of higher magnitude should be preferred as predictors of LMP since they are less influenced by measurement errors, thus contributing to more stable regression models.
URI: http://hdl.handle.net/10198/2923
ISBN: 90-77381-56-4
Aparece nas colecções:CIMO - Publicações em Proceedings Indexadas à WoS/Scopus

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