Digital-image analysis to predict weight and yields of boneless subprimal beef cuts

Abstract

For several decades, beef carcass evaluation for grading or research purposes has relied upon subjective visual scores, and manually taken measurements, but in recent times there has been a growing interest in new technologies capable of improving accuracy of estimates. Equations to predict weight and yield of beef pistol subprimal cuts were developed in this work using digital image analysis (VIA) of the 12 th rib steak. Equations to predict total pistol subprimal cuts weight (CUTS) had coefficients of determination (CD) of 0.84, or 0.87 to 0.88, when the independent variables were the VIA parameters and the half carcass weight (HC) or the total pistol weight (TP), respectively. The predicted values for the total seven subprimal cuts, as a percentage of half carcass weight (CUTS%), presented CD values ranging from 0.37 to 0.47, or 0.21 to 0.31, using HC or TP as a principal independent variable. Likewise, the equation for weight of the individual subprimal cuts had CD values ranging from 0.40 to 0.72, or 0.43 to 0.74 using HC or TP, respectively. In this research, the developed VIA procedure has demonstrated good repeatability and accuracy to estimate the total pistol subprimal weights, and some individual subprimal weights. Key words: beef industry, beef carcass, image analysis, rib eye area, fat thickness ANÁLISE DE IMAGEM DIGITAL PARA A PREVISÃO DE PESOS E RENDIMENTOS DE CORTES DE CARNE BOVINARESUMO: Por várias décadas, a avaliação de carcaça bovina em sistemas de tipificação ou em pesquisas tem dependido de escores subjetivos e medidas obtidas manualmente, mas ultimamente tem havido um crescente interesse por novas tecnologias capazes de aumentar a acurácia das estimativas. Este trabalho teve como objetivo desenvolver equações para a previsão de pesos e rendimentos de cortes bovinos, através da análise de imagem digital (VIA) de uma seção do contrafilé da 12 a costela. As equações de previsão do peso dos cortes do traseiro especial (CUTS) apresentaram coeficientes de determinação (CD) de 0,84 e de 0,87 -0,88, quando as variáveis independentes usadas eram os parâmetros VIA e o peso da meia carcaça (HC) ou o peso total do traseiro especial (TP), respectivamente. As equações de previsão do rendimento dos cortes do traseiro especial (CUTS%) representaram de 37,1 a 46,8% e de 21,3 a 30,6% da variação total, quando a principal variável independente utilizada na equação era HC ou TP, respectivamente. Nas equações de previsão dos pesos individuais dos cortes do traseiro especial o CD variou de 0,40 -0,72 e de 0,43 -0,74, usando as variáveis HC ou TP, respectivamente. O sistema de análise de imagem digital utilizado apresentou boa repetibilidade, podendo ser considerado um procedimento confiável para a estimativa do peso em cortes do traseiro especial e de alguns dos seus cortes individuais. Palavras-chave: indústria da carne, carcaça bovina, análise de imagem, área de olho de lombo, espessura de gordura

A review of the development and use of video image analysis (VIA) for beef carcass evaluation as an alternative to the current EUROP system and other subjective systems

“…These findings are summarised in Table 3. The R 2 and RSD values were very low in the results presented by Teira et al (2003), which is probably because there was very little variation (CV = 4.0%) in SMY% in the Nelore steers used in that experiment. In contrast, a higher amount of variation (CV = 9.6%) existed between steers and heifers in the results of Shackelford et al (1998) and a higher R 2 and lower RSD values for the prediction equation were obtained.…”

“…A different approach to collect VIA data that involved removing a section of M. longissimus thoracis from the posterior half of the 12th thoracic vertebra has been tried a number of times with varying results (Farrow, Loneragan, Pauli, & Lawrence, 2009;Shackelford, Wheeler, & Koohmaraie, 1998;Teira, Tinois, Lotufo, & Felício, 2003). Shackelford et al (1998) developed a 5 variable prediction equation, that accounted for 89% of the variation in SMY% across the combined experimental and validation datasets.…”

“…However, they did not report the accuracy of their VIA system for predicting FAT% or total fat trim (kg). Teira et al (2003) derived equations to predict individual subprimal yields using a removed steak that could explain 45% of the variation in pistola cut SMY% (including top round, bottom round, eye round, knuckle, rump, cube roll and strip-loin, and tenderloin as percentages of carcass weight). Farrow et al (2009) could explain 68% of the SMY% variation in 87 steers (coefficient of variation (CV%) = 7.3%) from an image of the 12/13th rib interface with more complex equations.…”

Accuracy of body measurements using digital image analysis in female Holstein calves

“…In recent years, computer-aided software has been developed and this software has been used for livestock production. Investigations have shown that the digital image analysis has been applied to determine meat quality, egg quality, marbling score, BM, liveweight and growth of pigs, rabbits, chickens, horses, goats, sheep and cattle (McDonald and Chen 1990;Kuchida et al 1991;Shackelford et al 1998;Shiranita et al 1998;Karnuah et al 2001;Cannell et al 2002;Teira et al 2003;De Wet et al 2003;Aktan 2004;Bozkurt et al 2007;Negretti et al , 2008Negretti et al , 2011Ozkaya and Bozkurt 2008;Wang et al 2008;Mollah et al 2010;Tasdemir et al 2011). Stajnko et al (2008) reported that the BM and liveweight of the bulls were estimated by thermal camera.…”

The relationship of parameters of body measures and body weight by using digital image analysis in pre-slaughter cattle

“…LI et al, (1997) showed that brittleness of meat could be determined by vision texture analysis. Similarly, investigations have shown this method can be used to analyze meat, determine marbling score and evaluate MLD area (NEWMAN, 1984;KUCHIDA et al, 1991;SHACKELFORD et al, 1998;SHIRANITA et al, 2000;KARNUAH et al, 2001;CANNELL et al, 2002;TEIRA et al, 2003). BOZKURT et al, (2006) used digital image analysis technology regarding prediction of body weight from body measurements in beef cattle.…”