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Online prediction of beef tenderness using a computer vision system equipped with a BeefCam module¹

Department of Animal Sciences, Colorado State University, Fort Collins 80523-1171

² Correspondence:
phone: 970-491-5826; fax: 970-491-0278; E-mail:
keith.belk{at}colostate.edu.

	Abstract

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Four experiments were conducted in two commercial packing plants to evaluate the effectiveness of a commercial online video image analysis (VIA) system (the Computer Vision System equipped with a BeefCam module [CVS BeefCam]) to predict tenderness of beef steaks using online measurements obtained at chain speeds. Longissimus muscle (LM) samples from the rib (Exp. 1, 2, and 4) or strip loin (Exp. 3) were obtained from each carcass and Warner-Bratzler shear force (WBSF) was measured after 14 d of aging. The CVS BeefCam output variable for LM area, adjusted for carcass weight (cm²/kg), was correlated (P < 0.05) with WBSF values in all experiments. The CVS BeefCam lean color measurements, a* and b*, were effective (P < 0.05) in all experiments for segregating carcasses into groups that produced LM steaks differing in WBSF values. Fat color measurements by CVS BeefCam were usually ineffective for segregating carcasses into groups differing in WBSF values; however, in Exp. 4, fat b* identified a group of carcasses that produced tough LM steaks. Quality grade factors accounted for 3, 18, 21, and 0% of the variation in WBSF among steaks in Exp. 1 (n = 399), 2 (n = 195), 3 (n = 304), and 4 (n = 184), respectively, whereas CVS BeefCam output variables accounted for 17, 30, 19, and 6% of the variation in WBSF among steaks in Exp. 1, 2, 3, and 4, respectively. A multiple linear regression equation developed with data from Exp. 2 accurately classified carcasses in Exp. 1 and 4 and thereby may be useful for decreasing the likelihood that a consumer would encounter a tough (WBSF > 4.5 kg) LM steak in a group classified as "tender" by CVS BeefCam compared with an unsorted population. Online measurements of beef carcasses by use of CVS BeefCam were useful for predicting the tenderness of beef LM steaks, and sorting carcasses using these measurements could aid in producing groups of beef carcasses with more uniform LM steak tenderness.

Key Words: Beef • Carcass Grading • Color • Palatability • Tenderness • Video Cameras

	Introduction

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To develop a total quality management plan for assuring the tenderness of beef, Tatum et al. (1999) described the need for identifying the causes of beef toughness so that corrective action could be taken in order to produce more consistently palatable beef. There has been considerable effort to develop an objective measure of beef palatability, and this measure could then be used to classify carcasses into groups with steaks of similar tenderness or palatability characteristics. One of these technologies, video image analysis or computer vision, has been reported to be able to measure crude fat content (Kuchida et al., 2000), color (Gerrard et al., 1996; Belk et al., 2000), and textural properties (Li et al., 1999), which could explain differences in cooked beef palatability.

Belk et al. (2000) reported that a prototype video imaging system (BeefCam) could identify carcasses that would yield steaks that would be "tender" after aging and cooking. However, this prototype BeefCam did have limitations that prevented its use in a commercial setting, and according to the National Beef Instrument Assessment Planning Symposium (NLSMB, 1994), for an instrument to be successful, it must be tested under real-world conditions. Smart Machine Vision (Reston, VA) and Research Management Systems USA (RMS Inc., Fort Collins, CO) have recently integrated features contained in the prototype BeefCam into the Computer Vision System (CVS). The CVS, which is manufactured by Research Management Systems USA, has proven useful in predicting the composition of beef carcasses under commercial conditions (Cannell et al., 2002). Four independent experiments were conducted to determine the effectiveness of the CVS, equipped with a BeefCam module (CVS BeefCam) for predicting Warner-Bratzler shear force (WBSF) values of longissimus muscle (LM) steaks from beef carcasses, and classifying those carcasses according to beef tenderness differences, in a commercial setting.

	Materials and Methods

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Four experiments were conducted in two commercial packing plants that utilize electrical stimulation, to determine the effectiveness of the CVS BeefCam for predicting the Warner-Bratzler shear force (WBSF) values of longissimus muscle (LM) steaks from carcasses. As carcasses were presented for grading, digital video images (charge-coupled device, 3-CCD) of the 12th-/13th-rib interface were obtained using a CVS BeefCam (labeled the "Cold Camera" by the manufacturer) and processed via proprietary software (CVS version 1.5) to generate output variables, which were then evaluated for their relationship to beef tenderness. Carcasses moved by the grading stand at an approximate speed of 200 carcasses per hour for Exp. 1, 2, and 4, and at approximately 380 carcasses per hour for Exp. 3. Bar-coded tags also were scanned at the time of grading, allowing CVS BeefCam measurements for each carcass to be matched with the appropriate in-plant carcass identification number and hot carcass weight.

Carcass Selection

Plant A. Beef carcasses (Exp.1, n = 399; Exp. 2, n = 195; and Exp. 4, n = 184) were randomly selected following a 48-h chill, ribbing, and line-grading over 2 d of production at a commercial packing plant (Sam Kane Beef Processors, Corpus Christi, TX). Carcasses sampled for Exp. 1 and 2 were from the USDA Choice and Select quality grades, whereas carcasses chosen for Exp. 4 were from the USDA Select grade only. A representative of USDA Agricultural Marketing Service (Exp. 1) or Colorado State University personnel (Exp. 2 and 4) collected carcass data on selected carcasses. The time carcasses were ribbed was recorded for Exp. 2 and 4 in order to determine the length of bloom at the time of imaging. For Exp. 1, individual carcass bloom times were not calculated, but carcasses were allowed to bloom for approximately 30 min. From one side of each carcass, a rib steak from the 12th- and 13th-rib interface (4-cm to 6-cm thick) was removed, vacuum-packaged, and transported in coolers (2°C) to the Meat Science Laboratory at Colorado State University. Longissimus muscle samples were aged at 2°C until 14 d postmortem, at which time they were frozen at -20°C and stored for later analysis.

Plant B. Steers (Exp. 3, n = 304) representing English breed, Continental European breed, and Bos indicus-influenced genetic backgrounds were harvested on four separate days at a commercial packing plant (ConAgra Beef Co., Greeley, CO). Following a 36-h chill, CVS BeefCam measurements were made following an approximate bloom time of 18 min, and a panel of three Colorado State University personnel independently obtained carcass grade data. Values from the three evaluators were averaged to produce a single value for each factor for each carcass. Strip loins (Institutional Meat Purchase Specifications 180; USDA, 1988) from the right side of each carcass were collected following fabrication, placed in plastic bags in boxes, and immediately transported to the Meat Science Laboratory at Colorado State University. The strip loins were vacuum-packaged and aged until 14-d postmortem at 2°C and then stored at -20°C for subsequent evaluation of WBSF. Frozen strip loin samples were fabricated (in the frozen state) into steaks (2.54 cm) using a band saw. One steak from the anterior end of each strip loin was designated for WBSF determination.

Warner-Bratzler Shear Force Determination.

Frozen LM samples were removed from vacuum packages, sawed into 2.54-cm-thick steaks, and thawed for 24 h at 4°C (precooking internal steak temperatures were monitored to ensure that the steaks were between 1 and 5°C for Exp. 2, 3, and 4) before cooking for WBSF determination. For Exp. 1, steaks were broiled on a Hobart Char Broiler (model CB 51, Hobart, Troy, OH); steaks were turned every 4 min until reaching a final internal temperature of 70°C and were monitored by a thermocouple (model 31380-KF, Atkins Technical, Gainesville, FL). For Exp. 2, 3, and 4, steaks were cooked using a Magikitch’n belt grill (Magigrill model TBG-60; Magikitch’n Inc., Quakertown, PA) set to cook steaks to an endpoint temperature of 70°C (settings: top heat = 177°C, bottom heat = 177°C, preheat = disconnected, height = 1.85 cm, cook time = 6.45 min). Final endpoint temperatures were monitored using a handheld thermometer (model HH21 thermometer; Omega Engineering, Inc., Stamford, CT). Cooked steaks were allowed to cool to room temperature (25°C) before removing 6 to 10 cores (1.27 cm in diameter) parallel to the muscle fiber orientation (AMSA, 1995). A single, peak shear force measurement was obtained for each core using a WBSF machine (G-R Electric Manufacturing Co., Manhattan, KS). Individual-core, peak shear force values were averaged to assign a mean peak WBSF value to each steak.

Data Analyses.

Descriptive statistics were computed by experiment for selected carcass traits, CVS BeefCam output variables, and WBSF values. Pearson’s correlation coefficients were calculated between CVS BeefCam output variables and WBSF values within an experiment (SAS Inst., Inc., Cary, NC). In order to determine if carcasses could be classified using output variables from the CVS BeefCam into groups that would produce LM steaks that were more uniform with respect to tenderness, segregation analyses were performed by experiment. Carcasses were classified into three groups (Low, Medium, and High; where Low < output variable mean - 1 SD, Medium = output variable mean ± 1 SD, and High > output variable mean + 1 SD) based on each output variable except the CVS BeefCam output variable for marbling (CVS BeefCam marbling). Then, the effects of classification by each output variable were tested using ANOVA, with marbling score as a linear covariate for Exp. 1, 2, and 3 (covariate was nonsignificant for Exp. 4). Least squares means were separated using a protected pairwise t-test when F-tests were significant at = 0.05 (SAS Inst., Inc.).

A natural logarithmic transformation of WBSF values was conducted to normalize the distribution of WBSF values before developing simple and multiple linear regression models using the stepwise model selection procedure (SAS Inst., Inc.), with the significance level for entry set at 85% ( = 0.15) and the significance level for exit set at 84% ( = 0.16). The frequency procedure of SAS was used to compare the frequency of carcasses that produced "tough" LM steaks (WBSF 4.5 kg; Shackelford et al., 1991) within certification level.

	Results and Discussion

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Carcass Characteristics.

Means and standard deviations for hot carcass weight, LM area (LMA), skeletal maturity, lean maturity, marbling score, WBSF values and L* (higher the value, lighter the color), a* (higher the value, redder the color), and b* (higher the value, the more yellow the color) values for lean and fat are presented in Table 1. There was a large range of carcass weights and LMA for carcasses sampled for all four experiments. The variation in marbling score depended on experiment, with Exp. 3 being the most variable because the carcasses were selected prior to ribbing and presentation for grading. The narrow range in USDA marbling scores in Exp. 4 was expected because carcasses were selected from only those stamped USDA Select. The range in lean and fat color was comparable across experiments, but the means for lean a*, lean b*, and fat a* values among experiments were substantially different and could have resulted from differences in cattle type, carcass management, length of bloom at the time of imaging, CVS BeefCams, or CVS BeefCam color calibration between the four experiments. Longissimus WBSF values (Table 1) indicated that the carcasses sampled would yield LM steaks that would result in a high percentage of consumer dissatisfaction (Shackelford et al., 1991) and the values were much higher than those in a survey of retail longissimus steaks conducted by Brooks et al. (2000).

Simple correlations of CVS BeefCam output variables and WBSF values are presented in Table 2. The CVS BeefCam output variable for LMA adjusted for carcass weight (cm²/kg) was positively correlated (P < 0.05) with WBSF for all experiments. The output variable, CVS BeefCam marbling, is a measure of the amount intramuscular fat found in the segmented lean of the LM adjusted to eliminate large streaks or coarse flecks of marbling. The correlation coefficient between CVS BeefCam marbling and WBSF was negative for all experiments and was correlated (P < 0.05) to WBSF for all experiments except for Exp. 4; however, the carcasses sampled in Exp. 4 represented a much narrower range of expert marbling scores than did the other experiments. Similar correlations within experiment were observed between marbling score and WBSF (data not presented in tabular form).

Although not always significant, all correlation coefficients between lean color variables and WBSF were negative, suggesting that higher WBSF values were associated with darker-colored lean. Other researchers have also reported that darker-colored lean is related to higher LM WBSF values (Wulf et al., 1997; Wulf and Page, 2000). The magnitude of lean color and WBSF correlations differed among experiments. For example, lean a* was moderately correlated (P < 0.05) to WBSF in Exp. 1 and 2 (r = -0.38 and -0.40, respectively) and marginally correlated to WBSF in Exp. 3 (r = -0.13), but not in Exp. 4. Wulf et al. (1997) reported that lean b* was the color measurement with the highest correlation to tenderness. Correlations (P < 0.05) between lean b* and WBSF were observed in three of the four experiments. Correlations between WBSF values and fat color measurements were usually not significant (P > 0.05). Interestingly, fat b* was significantly correlated to WBSF values among steaks in Exp. 4, whereas none of the lean color measurements was significantly correlated with WBSF values in this population of steaks.

Segregation Analyses.

The effects of segregating carcasses into categories using CVS BeefCam output variables are presented in Table 3.Wulf and Page (2000) reported that lean L* values might be most useful in sorting off a group of beef carcasses likely to yield steaks that are low in palatability. In Exp. 2 and 3, the Low lean L* groups contained steaks that were tougher (P < 0.05) than those from the Medium or High lean L* groups. The output variables lean a* and lean b* were effective (P < 0.05) in segregating carcasses into groups differing in tenderness of their steaks (as determined by WBSF) in all experiments. For both lean a* and lean b*, lower (P < 0.05) WBSF values were associated with higher output variable values. Based on these results, either lean a* or lean b* could be used to identify (and, sort off) a group of carcasses likely to produce "more tender" or "less tender" steaks. Although fat L* was able to segregate carcasses (P < 0.05) for Exp. 3 across all experiments, fat L* appeared ineffective (P > 0.05) for identifying carcasses as likely to produce either tender or tough steaks. Similarly, fat a* failed (P > 0.05) to separate carcasses according to WBSF values of their steaks across all experiments. In Exp. 4, fat b* was successful (P < 0.05) in identifying a group of carcasses that produced tough LM steaks; however, fat b* was ineffective (P > 0.05) in sorting carcasses for tenderness/toughness of their steaks in the other three experiments.

Regression Analyses.

For each experiment, simple and multiple linear regression equations were developed to predict the natural logarithm of WBSF values using USDA quality grade factors and using output variables from the CVS BeefCam (Table 4). Marbling score entered into models for Exp. 1, 2, and 3 and accounted for 3, 11, and 13%, respectively, of the observed variation in WBSF values, but was not useful in accounting for variation in WBSF values in Exp. 4 (Table 4). The relationship between marbling score and tenderness has varied in the literature and depends largely upon the range of marbling scores included in a particular study. Smith et al. (1984) reported that marbling score accounted for 34% of the observed variation in overall palatability ratings for loin steaks from A maturity carcasses that ranged in marbling score from practically devoid to moderately abundant. In a study that contained mostly USDA Select carcasses, Wulf et al. (1996) reported that marbling score was not related (P > 0.05) to WBSF. The results of the present experiments also suggest that as the range in marbling scores increases, the amount of variability explained in WBSF increases.

In Exp. 1, lean b* singularly accounted for 14% of the variation in WBSF values, and when combined with CVS BeefCam marbling plus fat b*, accounted for 17% of the variation in WBSF values (Table 4). The best equation for predicting WBSF values for carcasses/steaks in Exp. 2, using only output variables from the CVS BeefCam included the variables lean a*, marbling, and lean b*, with lean a* accounting for 16% of the observed variation in WBSF. The best equation for predicting WBSF values for carcasses/steaks in Exp. 3 included the independent variables lean L*, CVS BeefCam LMA (cm²), lean b*, CVS BeefCam marbling and fat b* and accounted for 19% of the variation in WBSF values. For Exp. 4, fat b* and lean b* were the only variables that entered the model, and when combined, resulted in a coefficient of multiple determination of 0.06.

Realizing that for commercial utilization, an objective measurement of tenderness must be able to predict or classify carcasses in real-time, a multiple linear regression equation for predicting WBSF values was developed using data from Exp. 2. These data were chosen (as the training data) because the bloom times were known (mean = 93 min, SD = 9 min; data not presented in tabular form) and were sufficient to allow for the color of the LM to stabilize. Wulf and Wise (1999) have reported that lean L*, lean a*, and lean b* values remain relatively constant after 33, 78, and 78 min, respectively, of blooming time. Additionally, the carcasses sampled for Exp. 2 represented both the USDA Choice and Select quality grades. The equation developed using data from Exp. 2 included the output variables CVS BeefCam marbling, lean a*, CVS BeefCam adjusted LMA, and fat b* and was developed to predict the natural logarithm of WBSF values. The equation, including beta coefficients, is as follows for the prediction of untransformed WBSF values:

This equation was then tested on carcasses from Exp.1 and 4, combined, and explained 19% of the variation in WBSF values of those LM steaks. To assess the ability of the regression equation to sort carcasses, carcasses were ranked from predicted most tender to predicted least tender and then "certified" as likely to produce tender steaks in 10% increments (10% certified represents the predicted most tender 10% of carcasses, 20% certified represents the predicted most tender 20% of carcasses, etc). The frequency of carcasses certified as tender that yielded tough (WBSF 4.5 kg) steaks was computed and compared to all other certification levels for all carcasses combined in Exp. 1 and 4 and for only stamped USDA Select carcasses combined in Exp. 1 and 4 (Table 5). Collectively, the frequency of carcasses that generated tough LM steaks for Exp. 1 and 4 was 36% for all carcasses and 43% for USDA Select carcasses (Table 5). Although the equation could not identify a group of beef carcasses that would produce only "tender" steaks, up to 80% could be certified for all carcasses and up to 60% could be certified for USDA Select carcasses (Table 5) to result in a reduced percentage of carcasses that produced tough steaks in the certified group compared to no sorting (all carcasses).

	Implications

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The Computer Vision System equipped with a BeefCam module captured and segmented video images at commercial packing-plant chain speeds to produce information useful in explaining observed variation in Warner-Bratzler shear force values of steaks, even when there was a narrow range of marbling scores. This information could be used to sort carcasses according to expected palatability differences of their steaks. Postmortem processes known to improve beef tenderness could then be selectively applied to carcasses identified as unlikely to produce tender steaks, and branded beef programs could use the machine measurements in addition to USDA quality grades to help ensure or improve the palatability of their product.

	Footnotes

 
¹ The authors would like to thank M. Goldberg, Smart Machine Vision, Reston, VA, and R. Richmond and A. Wyle, Research Management Systems USA, Fort Collins, CO, for providing the Computer Vision Systems equipped with BeefCam modules used in these experiments.

Received for publication February 25, 2002. Accepted for publication November 14, 2002.

	Literature Cited

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AMSA. 1995. Guidelines for Cookery and Sensory Evaluation of Meat. Am. Meat Sci. Assoc., Chicago, IL.

Belk, K. E., J. A. Scanga, A. M. Wyle, D. M. Wulf, J. D. Tatum, J. O. Reagan, and G. C. Smith. 2000. The use of video image analysis and instrumentation to predict beef palatability. Proc. Recip. Meat Conf.53:10–15.

Brooks, J. C., J. B. Belew, D. B. Griffin, B. L. Gwartney, D. S. Hale, W. R. Henning, D. D. Johnson, J. B. Morgan, F. C. Parrish, Jr., J. O. Reagan, and J. W. Savell. 2000. National beef tenderness survey—1998. J. Anim. Sci.78:1852–1860.[Abstract/Free Full Text]

Cannell, R. C., K. E. Belk, J. D. Tatum, J. W. Wise, P. L. Chapman, J. A. Scanga, and G. C. Smith. 2002. Online evaluation of a commercial video image analysis system (Computer Vision System^TM) to predict beef carcass red meat yield and for augmenting application of USDA yield grades. J. Anim. Sci.80:1195–1201.[Abstract/Free Full Text]

Gerrard, D. E., X. Gao, and J. Tan. 1996. Beef marbling and color score determination by image processing. J. Food Sci.61:145–147.

Hilton, G. G., J. D. Tatum, S. E. Williams, K. E. Belk, F. L. Williams, J. W. Wise, and G. C. Smith. 1998. An evaluation of current and alternative systems for quality grading carcasses of mature slaughter cows. J. Anim. Sci.76:2094–2103.[Abstract/Free Full Text]

Kuchida, K., S. Kono, K. Konishi, L. D. Van Vleck, M. Suzuki, and S. Miyoshi. 2000. 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. J. Anim. Sci.78:799–803.[Abstract/Free Full Text]

Li, J., J. Tan, F. A. Martz and H. Heymann. 1999. Image texture features as indicators of beef tenderness. Meat Sci.53:17–22.

NLSMB. 1994. National beef instrument assessment plan. National Live Stock and Meat Board, Chicago, IL.

Page, J. K., D. M. Wulf, and T. R. Schwotzer. 2001. A survey of beef muscle color and pH. J. Anim. Sci.79:678–687.[Abstract/Free Full Text]

Shackelford, S. D., J. B. Morgan, J. W. Morgan, J. W. Savell, and H. R. Cross. 1991. Identification of threshold levels for Warner-Bratzler shear force in top loin steaks. J. Muscle Food2:289–296.

Smith, G. C., Z. L. Carpenter, H. R. Cross, C. E. Murphey, H. C. Abraham, J. W. Savell, G. W. Davis, B. W. Berry, and F. C. Parrish Jr. 1984. Relationship of USDA marbling groups to palatability of cooked beef. J. Food Qual7:289–308.

Tatum, J. D., K. E. Belk, M. H. George, and G. C. Smith. 1999. Identification of quality management practices to reduce the incidence of retail beef tenderness problems: Development and evaluation of a prototype quality system to produce tender beef. J. Anim. Sci.76:2805–2810.

USDA. 1988. Institutional Meat Purchase Specifications for Fresh Beef. Agric. Marketing Serv., USDA, Washington, DC.

Wulf, D. M., J. B. Morgan, J. D. Tatum, and G. C. Smith. 1996. Effects of animal age, marbling score, calpastatin activity, subprimal cut, calcium injection, and degree of doneness on the palatability of steaks from Limousin steers. J. Anim. Sci.74:569–576.[Abstract]

Wulf, D. M., S. F. O’Connor, J.D. Tatum, and G. C. Smith. 1997. Using objective measures of muscle color to predict beef longissimus tenderness. J. Anim. Sci.75:684–692.[Abstract/Free Full Text]

Wulf, D. M., and J. W. Wise. 1999. Measuring muscle color on beef carcasses using the L*a*b* color space. J. Anim. Sci.77:2418–2427.[Abstract/Free Full Text]

Wulf, D. M., and J. K. Page. 2000. Using measurements of muscle color, pH, and electrical impedance to augment the current USDA beef quality grading standards and improve the accuracy and precision of sorting carcasses into palatability groups. J. Anim. Sci.78:2595–2607.[Abstract/Free Full Text]

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