Prediction of the Crude Fat Contents in Rib-Eye Muscle of Beef Using the Fat Area Ratio Calculated by Computer Image Analysis

Kuchida, Keigo; Konishi, Kazuyuki; Suzuki, Mitsuyoshi; Miyoshi, Shunzo

doi:10.2508/chikusan.69.585

Cited by 5 publications

(5 citation statements)

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“…The use of image analysis to produce the official JMGA photographic standards and in direct carcase assessment has been reported by Kuchida et al (2006) and may provide a more sophisticated and objective tool for marbling classification (Kuchida et al 1997;Kuchida et al 1998;Kuchida et al 2000;Kuchida et al 2001). Konarska et al (2013) also reported a test procedure for image analysis of beef samples that had been first frozen and then thawed prior to analysis.…”

Section: Introductionmentioning

confidence: 99%

Relationships between marbling measures across principal muscles

et al. 2017

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As marbling is a principal input into many grading systems it is important to have an accurate and reliable measurement procedure. This paper compares three approaches to measuring marbling: trained personnel, near infrared spectroscopy (NIR) and image analysis. One 25mm slice of meat was utilised from up to 12 cuts from 48 carcasses processed in Poland and France. Each slice was frozen to enable a consistent post-slaughter period then thawed for image analysis. The images were appraised by experienced beef graders and the sample used to determine fat content by NIR. We find that image analysis based marbling measures are capturing something different to trained personnel and that there is a strong relationship between near infrared spectroscopy and trained personnel. Finally, we demonstrate that marbling measures taken on one muscle can be predictive of marbling in other muscles in the same carcase. This is particularly important for cut based models such as the Meat Standards Australia system.

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Section: Introductionmentioning

confidence: 99%

Relationships between marbling measures across principal muscles

et al. 2017

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“…The effects of breed groups were not included in the mathematical models, although several breed groups were used at Station A. Effects due to breed groups have been shown to be not significant (Kuchida et al, 1998). The GLM procedure of SAS (1989) was used for statistical analysis.…”

Section: Discussionmentioning

confidence: 99%

“…This software draws contours of marbling particles for a specified area on the computer screen that displays the original color image of the ribeye area. The ratio of fat area (RFA) to area of ribeye muscle obtained with this program could be used as a linear covariate to predict crude fat percentage in ribeye muscle with high precision (r 2 = .91) and accuracy (error of prediction within ±3%; Kuchida et al, 1998). However, the image data in that study were taken by CCD camera at only one laboratory, and prediction error when using a traditional film camera, the type that is widely used to photograph meat, was not evaluated.…”

Section: Introductionmentioning

confidence: 99%

Prediction of crude fat content of longissimus muscle of beef using the ratio of fat area calculated from computer image analysis: comparison of regression equations for prediction using different input devices at different stations.

Kuchida

Kono²,

Konishi

et al. 2000

Journal of Animal Science

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Crude fat content of longissimus (ribeye) muscle of beef cattle was predicted from a ratio of fat area (RFA) to area of ribeye muscle calculated from computer image analysis (CIA). Cross sections of 64 ribeyes taken from the 6-7th rib from cattle at experiment station A and cross sections of 94 ribeyes taken from the 6-7th rib from cattle at Experiment Station B were used in this study. Slices (1 to 1.5 cm in thickness) of just the Longissimus dorsi were homogenized and sampled for chemical estimation of crude fat content using petroleum ether. Crude fat content as determined from chemical analysis was used as the true estimate of fat content. A CCD (charge-coupled device) camera was used as the input device at Experiment Station A, and a single-lens reflex camera was used at Experiment Station B to photograph ribeyes for CIA. The contour comparison method, which assigns a threshold value for each marbling particle, was used to obtain accurate binarization in this study. Minimum and maximum of chemical measurements of crude fat were 2.1 and 39.8%, and for CIA calculation of the RFA were 6.1 and 56.8%, respectively. This range covered almost the complete range of the beef marbling standard used in carcass grading in Japan. The equation for the regression of the crude fat content (Y) on RFA (X) calculated from CIA for all of the data was Y = .793X-3.04 with r2 = .96. Regression equations for prediction of crude fat percentage from RFA taking into consideration the effect of experiment station were Y = .741X-2.22 with r2 = .91 for Experiment Station A, and Y = .782X-2.54 with r2 = .91 for Experiment Station B. Analysis of covariance showed that the effects of experiment stations on intercepts and slopes were not significant (P > .10). The ranges of differences between actual and predicted crude fat content from the prediction equation that was calculated without consideration of the effect of station were -6.4 to 4.0%. CIA of cross sections of the ribeye muscle seems to have potential for prediction of crude fat content.

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“…Many authors used image analysis for intramuscular visible fat ratio evaluation in meat (Chandraratne et al, 2003;Kuchida, Konishi, Suzuki, & Miyoshi, 1998;Kuchida et al, 2000) and put into correlation the results with those obtained from chemical analysis, in order to find a new technique able, with some limits, to replace the latter which is expensive and long time taking. The correlations obtained were very high as fat distribution was probably very uniform.…”

Section: Visible Fat Area Ratiomentioning

confidence: 99%