A review of probes and robots: implementing new technologies in meat evaluation

Swatland, H. J.; Ananthanarayanan, S.P.; Goldenberg, A.A.

doi:10.2527/1994.7261475x

Cited by 18 publications

(8 citation statements)

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“…Probe parameters between the first and second pause were subsequently extracted to yield the area under this portion of the force curve ( Area-2) , modified ( Area-2B) , or Area divided by the time taken for the probe tip to progress into the muscle, which equals the distance between the first and second pauses ( Power-2 and Power-2B) . This position-based and time-based recording system was designed to overcome measurement disorder and recoil influences caused by piercing strong connective tissue sheaths, such as encompass the longissimus lumborum (Swatland et al, 1994).…”

Section: Resultsmentioning

confidence: 99%

Comparison of USDA quality grade with tendertec for the assessment of beef palatability.

George

Tatum²,

Dolezal³

et al. 1997

Journal of Animal Science

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Carcasses ( n = 265) selected to differ in USDA yield grade were evaluated by expert graders and assessed for tenderness with the Tendertec Mark III Beef Grading Instrument. Tendertec measurements were collected on longissimus lumborum muscles in both sides of each carcass. Sensory panel ratings for amount of connective tissue were correlated ( P < .01) with Tendertec output variables Area-2 and Area-2B ( r = −.168 and −.154, respectively), and ratings for overall tenderness were correlated ( P < .05) with the Area-2 output variable (r = −.131) but the coefficients were very low. Segregation analysis, using Tendertec output variables Area-2 and Power-2, significantly ( P < .05) stratified sensory panel ratings for connective tissue amount and overall tenderness. Even though the Tendertec probe detected some differences in connective tissue contributions to rib steak tenderness, it was not better than USDA quality grade at segmenting A-maturity carcasses into anticipated tenderness outcomes, and thus its applicability as a grading instrument may be limited to use on more mature beef carcasses.

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Section: Resultsmentioning

confidence: 99%

Comparison of USDA quality grade with tendertec for the assessment of beef palatability.

George

Tatum²,

Dolezal³

et al. 1997

Journal of Animal Science

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“…Regarding predicted leanness of pork carcasses, many different technologies have been used in the last 30 years for online measurement of lean meat percentage in pork carcasses, with the primary focus of identifying the true commercial value of the pork carcass to the processor and provide proper recommendations for pig producers ( Swatland et al, 1994 ). The most common method for the measurement of carcass lean yield in North America is the use of an optical probe to measure fat depth and muscle depth of the loin, and a subsequent conversion of these measurements presents the designated conversion equations used for the calculation of predicted lean yield ( Busk et al, 1999 ; Zhou and Bohrer, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

The relationship of pork carcass weight and leanness parameters in the Ontario commercial pork industry

Barducci

Zhou

Wormsbecher

et al. 2019

Translational Animal Science

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This study aimed to examine the correlation of carcass weight, fat depth, muscle depth, and predicted lean yield in commercial pigs. Data were collected on 850,819 pork carcasses from the same pork processing facility between October 2017 and September 2018. Hot carcass weight was reported following slaughter as a head-on weight; while fat and muscle depth were measured with a Destron PG-100 probe and used for the calculation of predicted lean yield based on the Canadian Lean Yield (CLY) equation [CLY (%) = 68.1863 − (0.7833 × fat depth) + (0.0689 × muscle depth) + (0.0080 × fat depth2) − (0.0002 × muscle depth2) + (0.0006 × fat depth × muscle depth)]. Descriptive statistics, regression equations including coefficients of determination, and Pearson product moment correlation coefficients (when assumptions for linearity were met) and Spearman’s rank-order correlation coefficients (when assumptions for linearity were not met) were calculated for attributes using SigmaPlot, version 11 (Systat Software, Inc., San Jose, CA). Weak positive correlation was observed between hot carcass weight and fat depth (r = 0.289; P < 0.0001), and between hot carcass weight and muscle depth (r = 0.176; P < 0.0001). Weak negative correlations were observed between hot carcass weight and predicted lean yield (r = −0.235; P < 0.0001), and between fat depth and muscle depth (r = −0.148; P < 0.0001). Upon investigation of relationships between fat depth and predicted lean yield, and between muscle depth and predicted lean yield using scatter plots, it was determined that these relationships were not linear and therefore the assumptions of Pearson product moment correlation were not met. Thus, these relationships were expressed as nonlinear functions and Spearman’s rank-order correlation coefficients were used. A strong negative correlation was observed between fat depth and predicted lean yield (r = −0.960; P < 0.0001), and a moderate positive correlation was observed between muscle depth and predicted lean yield (r = 0.406; P < 0.0001). Results from this dataset revealed that hot carcass weight was generally weakly correlated (r < |0.35|) with fat depth, muscle depth, and predicted lean yield. Therefore, it was concluded that there were no consistent weight thresholds where pigs were fatter or heavier muscled.

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“…Real time ultrasound (Miles et al 1987;Fursey et al 1991;Swatland et al 1994), velocity of sound (Porter et al 1990), bioelectrical impedance (Johns et al 1992;Marchello & Slanger 1994), and video image analysis (Eldridge 1994;Ferguson et al 1995) have been closely investigated in the last few years. However, a simple and potentially inexpensive technology that gave early promise does not appear to have been fully appraised.…”

Section: Introductionmentioning

confidence: 99%

Use of carcass density for determining carcass composition in beef cattle

Johnson

Chant

1998

New Zealand Journal of Agricultural Research

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A study of 38 beef sides (total dissected fat 16.5%-33.0%) showed that densities of forequarter, hindquarter, and side, each predicted percentage carcass components with less accuracy than 12th rib fat thickness. Hot carcass weight added to density of side improved the predictions of fat and bone only slightly (standard errors of estimate of 3.45% and 0.99%, respectively) over density of side alone. Density of primal cuts or combinations of primal cuts predicted carcass composition with lower standard errors of estimate than any of the above covariates.The addition of fat thickness at the 12th rib and carcass weight to the most accurate density measurements (loin and rump) did not significantly improve the prediction of any carcass component.Hot carcass weight was not related to the density of any carcass part. Side fat percentage was related to the density of all carcass parts except shin, and covariance analysis showed that breed effects remained in forequarter, side, and rump. Density of hindquarter, loin, and topside were free from breed effects.Density of side alone, or together with hot carcass weight, did not predict carcass composition with sufficient accuracy to be considered valuable for commercial adaptation.

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A review of probes and robots: implementing new technologies in meat evaluation

Cited by 18 publications

References 0 publications

Comparison of USDA quality grade with tendertec for the assessment of beef palatability.

Comparison of USDA quality grade with tendertec for the assessment of beef palatability.

The relationship of pork carcass weight and leanness parameters in the Ontario commercial pork industry

Use of carcass density for determining carcass composition in beef cattle

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