Predicting percentage of intramuscular fat using two types of real-time ultrasound equipment.

Abstract

A total of 500 steers were used to develop models for prediction of percentage of intramuscular fat (PIMF) in live beef cattle. Prior to slaughter, steers were scanned across the 11th and 13th ribs using Aloka 500V and PIE scanner 200 machines. After slaughter, a cross-sectional slice of the longissimus dorsi muscle from the 12th rib facing was used for chemical extraction to determine carcass intramuscular fat measures (CIMF). Texture analysis software was used by two interpreters to define image parameters, which included Fourier, gradient, histogram, and co-occurrence parameters. A total of four prediction models were developed for each machine. These included, models developed without transformation of CIMF (model-I), models based on logarithmic transformation of CIMF (model-II), ridge regression (model-III), and principal components regression (model-IV) models. Model R2 and root mean square error (RMSE) of Aloka models I, II, III and IV were .72, .84%; .72, .86%; .69, .91%; and .71, .86%; respectively. The corresponding R2 and RMSE value of PIE models I, II, III, and IV were .68, .87%; .70, .85%; .64, .94%; and .65, .91%; respectively. All models were validated on an independent data set from 71 feedlot steers. The overall mean bias, standard error of prediction (SEP), and rank correlation coefficient across the four Aloka models were .42%, .84%, and .88, respectively. For PIE models the corresponding values were .67%, .81%, and .91, respectively. Both Aloka and PIE equipment can be used to accurately predict PIMF in live cattle. Further improvement in the accuracy of prediction could be achieved through increasing the development data set and the variation in PIMF of cattle used. Keywords ASL R1732 Disciplines Animal SciencesThis Craig Hays, CUP manager Summary A total of 500 steers were used to develop models for prediction of percentage of intramuscular fat (PIMF) in live beef cattle. Prior to slaughter, steers were scanned across the 11th and 13th ribs using Aloka 500V and PIE scanner 200 machines. After slaughter, a cross-sectional slice of the longissimus dorsi muscle from the 12th rib facing was used for chemical extraction to determine carcass intramuscular fat measures (CIMF). Texture analysis software was used by two interpreters to define image parameters, which included Fourier, gradient, histogram, and co-occurrence parameters. A total of four prediction models were developed for each machine. These included, models developed without transformation of CIMF (model-I), models based on logarithmic transformation of CIMF (model-II), ridge regression (model-III), and principal components regression (model-IV) models. Model R 2 and root mean square error (RMSE) of Aloka models I, II, III and IV were .72, .84%; .72, .86%; .69, .91%; and .71, .86%; respectively. The corresponding R 2 and RMSE value of PIE models I, II, III, and IV were .68, .87%; .70, .85%; .64, .94%; and .65, .91%; respectively. All models were validated on an independent data set from 71 feedlot steers. The overall mean bias...

Intramuscular fat content in meat-producing animals: development, genetic and nutritional control, and identification of putative markers

“…Folch et al, 1957) or estimated in vivo by real-time ultrasound (e.g. Hassen et al, 2001;Newcom et al, 2002) and H 1 -NMR (e.g. Boesch et al, 1997;Toussaint et al, 2002) techniques.…”

“…As marbling score in these animals is genetically highly correlated with muscle lipid content (r G 5 0.91), selection based on the former may lead to a correlated improvement in tenderness (for review, see Hocquette et al, 2006). This relationship explains most of the efforts dedicated to the development of live scanning for IMF content as a selection tool in the United States and Australia (Reverter et al, Intramuscular fat content in meat-producing animals 2000; Hassen et al, 2001;Sapp et al, 2002). However, as marbling is positively genetically correlated to the carcass fatness, the undesirable side effects of selection on the basis of IMF content or marbling are often the increase in carcass fatness.…”

El análisis textural mediante las matrices de co-ocurrencia (GLCM) sobre imagen ecográfica del tendón rotuliano es de utilidad para la detección cambios histológicos tras un entrenamiento con plataforma de vibración. (Textural Analysis by Means of a Grey Level Co-Occurrence Matrix Method on Patellar Tendon Ultrasonography is Useful for the Detection of Histological Changes after Whole-Body Vibration Training)

“…Sería deseable en futuros estudios evaluar el efecto que tiene tomar distintas distancias entre píxeles para comprobar cuál es la que mejor discrimina, así como combinar los distintos factores que pueden influir (variables, orientaciones, etc) y realizar un análisis discriminante que permita detectar los cambios texturales con mayor precisión (Christodoulou et al, 2003;Hassen et al, 2001;Sheppard & Shih, 2005;Michail et al, 2007). Es posible además pensar que la respuesta no sea igual en hombres que en mujeres (Gaida et al, 2004), debido a factores hormonales que puedan influir en la síntesis de coláge-no, por lo que en futuros estudios habrá que tener en cuenta esta variable.…”

Prediction of intramuscular fat in live bulls using real-time ultrasound and image analysis

“…Wilson et al (1993) and Izquierdo et al (1996) found an R 2 range from 0.44 to 0.70 for various prediction models. Hassen et al (2001) found values of R 2 ranging from 0.69 to 0.91 for various prediction models of IMF content in meat. Liu et al (1993) and Gerrard et al (1996) correlated the marbling degree detected subjectively by a group of classifi ers with the IMF content determined chemically, and found highly signifi cant correlations r=0.92 and r=0.75.…”

“…Several papers have dealt with assessing IMF content and marbling degree by ultrasound only or combined with computer image analysis (Newman, 1987;Wilson et al, 1993;Brethour, 1994;Park et al, 1994;Hassen et al, 2001). The results to date of different authors suggest that the method can also be used in practice.…”