Neural network modeling of carcass measurements to predict beef tenderness

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1 Neural network modeling of carcass measurements to predict beef tenderness B. D. Hill 1, S. D. M. Jones 1, W. M. Robertson 2, and I. T. Major 1 1 Research Centre, Agriculture and Agri-Food Canada, P. O. Box 3000, Lethbridge, Alberta, Canada T1J 4B1 ( HillB@em.agr.ca); and 2 Research Centre, Agriculture and Agri-Food -Canada, Lacombe, Alberta, Canada T4L 1W1. LRC contribution no , received 17 June 1999, accepted 31 January Hill, B. D., Jones, S. D. M., Robertson, W. M. and Major, I. T Neural network modeling of carcass measurements to predict beef tenderness. Can. J. Anim. Sci. 80: Neural network (NN) models were developed for predicting and classifying an objective measurement of tenderness using carcass data such as pre-slaughter information (sex, age, kill order), weights, ph, temperatures, lean color readings, lab-determined measurements, grade measurements and organ weights. Tenderness was expressed objectively as Warner-Bratzler shear (WBS) force measured on steaks, aged 6 d, from the longissimus thoracis et lumborum (LTL) muscle. Carcass data from experiments conducted between 1985 and 1995 at the Lacombe Research Centre were combined to form large data sets (n = ) for modeling. Neural network models to predict actual shear values showed limited potential (R 2 = ) and were only marginally better than a multiple linear regression (MLR) model (R 2 = 0.34). Neural network models that classified carcasses into tenderness categories showed better potential (mean accuracy 51 53%). The best four-category (tender, probably tender, probably tough, tough) model classified tender and tough steaks with accuracies of 0.64 and 0.79, respectively. This model reduced tough and probably tough carcasses by 55% in our population. The model required the following 11 inputs, which, except for cooking method, are available by 24 h postmortem: sex, live plant weight, hot carcass weight, 24-h cooler shrink, 24-h ph, 24-h CIE color b*, 24-h CIE lightness L* hue angle, rib eye area, grader s marbling score (AMSA%), grade, and cooking method. By implementing techniques outlined in this study in a plant situation, the current 23% unacceptable consumer rating for Canadian beef could be reduced to 10 12%. Key words: Neural networks, beef, tenderness, carcass measurements, longissimus muscle Hill, B. D., Jones, S. D. M., Robertson, W. M. et Major, I. T Modélisation par réseaux neuraux des mesures de la carcasse pour prédire la tendreté de la viande bovine. Can. J. Anim. Sci. 80: Nous avons construit des modèles par réseaux neuraux (RN) permettant de prédire et de classer la mesure objective de la tendreté à partir de données de carcasse telles que le dossier préabattage (sexe, âge, ordre d abattage), le poids, le ph, la température, les valeurs colorimétriques du maigre, les mesures faites en laboratoire, les facteurs de classement et le poids des organes. La tendreté correspondait à la force de cisaillement Warner-Bratzler mesurée sur des steaks provenant du longissimus thoracis et lumborum (LTL) après 6 jours de rassissement. Pour la modélisation nous avons combiné les données de carcasse issues d expériences réalisées de 1985 à 1995 au Centre de recherches de Lacombe ce qui nous procurait de grands jeux de données (n = ). Pour l aptitude à prédire les valeurs de cisaillement réelles, les modèles RN ne démontraient que des potentialités limitées (R 2 = 0,37 à 0,45) et il n étaient guère meilleurs qu un modèle de régression linéaire multiple (R 2 = 0,34). Les modèles RN qui rangeaient les carcasses par catégories de tendreté offraient de meilleures potentialités (exactitude moyenne %). Le meilleur modèle à 4 catégories : tendre, probablement tendre, probablement coriace et coriace permettait de classer les steaks tendres et les steaks coriaces avec des degrés d exactitude respectifs de 0,64 et 0,79. Ce modèle a, en outre, permis de réduire de 55 % la fréquence des carcasses coriaces et probablement coriaces au sein des populations utilisées. Le modèle nécessitait l inclusion des 11 intrants suivants, lesquels sont disponibles 24 h après l abattage, c.-à-d. le sexe, le poids vif à l abattoir, le poids de carcasse chaude, la perte de poids après 24 h de ressuage, le ph, l indice colorimétrique b* (CIE) et le produit L* (CIE angle de phase à 24 h), la surface de la noix de côte, la cote de persillé au classement (AMSA%), la catégorie et la méthode de cuisson. L application en usine des techniques exposées dans l article pourrait abaisser de 23 % qu elle est actuellement à % la fréquence des cotes à la consommation inacceptables de la viande bovine canadienne. Mots clés: Réseaux neuraux, viande bovine, tendreté, mesures de carcasse, muscle lo The Canadian beef industry has a strategic plan to increase consumer satisfaction with beef products to at least 95%. In past surveys, Jeremiah et al. (1992) found that 15 32% of Canadian consumers gave unacceptable palatability ratings to samples of cooked steaks. Aalhus et al. (1992) estimated that with 6 d aging and no high-voltage electrical stimulation, more than 20% of steaks marketed in Canada would be rated as having unacceptable tenderness by consumers. Tenderness is the main factor thought to influence consumer acceptability. Jeremiah et al. (1993) reported that 64% of 311 Canadian survey respondents indicated that tenderness was the primary determinant of satisfaction when consuming beef steaks and Huffman et al. (1996) found that 51% of US consumers considered tenderness the attribute they wanted most in a steak. Huffman et al. (1996) also concluded that WBS force values can be used to determine a level of beef Abbreviations: CL, confidence limits; LTL, longissimus thoracis et lumborum; MLR, multiple linear regression; NN, neural network; WBS, Warner-Bratzler shear

2 312 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 1. Data sets from Lacombe Research Centre used in NN modeling Data set Year Number carcasses by sex Total no. Range of designation z slaughtered Breeds y Heifers Steers Bulls carcasses WBS (kg) Individual data sets Kobe , Wagyu , Bulls x SmTime x 1988, , GE FCBE w , Totals Combined data sets ALL v ALL-K v , 2, ALLGA v z Origin of data sets: Kobe = Dubeski et al. (1997a,b,c); Wagyu = Bailey, D. R. C. (Lacombe Research Centre, personal communication); Bulls = A. L. Schaefer (Lacombe Research Centre, personal communication); SmTime = [SmartAmineJ = Thomson et al. (1996) + Time Off Feed = Jones et al. (1989)]; GE = Aalhus et al. (1992); FCBE = Newman et al. (1994). y Breeds: 1 = British, British crossbreeds; 2 = Continental crossbreeds, British Continental crossbreeds; 3 = Wagyu influence (50%, 75%); 4 = Holstein. x Data from three different Bull experiments were combined to form a Bulls data set; data from a SmartAmineJ (slaughtered ) experiment and a Time Off Feed experiment (slaughtered 1988) were combined to form a SmTime data set. w The FCBE data set was not included in any combined data sets because some important carcass measurements were missing. v Combined data sets were designated as ALL (all data sets combined except FCBE); ALL-K (all data sets combined except FCBE and Kobe); ALLGA (grade A, AA, and AAA carcasses only from ALL data set). tenderness acceptable to consumers. To achieve the goal of 95%+ consumer satisfaction for beef, a non-destructive system is needed to assess meat tenderness within 24 h of slaughter. Such a system could include traditional carcass measurements such as weights, ph, temperatures, color readings and visual gradings, perhaps combined with newer technologies such as ultrasound, computer imaging, nearinfrared spectroscopy or a tenderness probe. Shackelford et al. (1997) expressed the opinion that newer technologies alone could not be expected to detect minute changes in raw meat that are responsible for variation in cooked meat tenderness. To date, it has not been possible to accurately predict beef tenderness from carcass measurements. Jeremiah et al. (1991) reported that subjective color scores, colorimeter readings, and ultimate ph values accounted for 33% or less of the variation in shear force values from 3425 beef carcasses (half or three-quarter Charolais, Limousin, Simmental, and Chianina) evaluated over a 10-yr period. May et al. (1993) found that temperature at 2.5 h postmortem, marbling score, days fed, fat thickness and carcass weight accounted for 46% of the variation in shear force of longissimus steaks from a limited number (n = 48) of Angus Hereford steers. The authors noted that a serial slaughter technique, uniform cattle type, and a breed type known to marble well, undoubtedly increased their observed relationships to tenderness. Jones and Tatum (1994) studied 240 beef carcasses (breeds not identified) processed under commercial conditions and found that marbling alone, or with ph at 3 h, accounted for only 9.0 and 11.5%, respectively, of the variation in WBS force. In a study of 317 animals (Bos taurus and Bos indicus crosses) slaughtered at a commercial facility, Wulf et al. (1997) found that marbling, ultimate ph, and colorimeter readings (luminance, L*, redness a*, yellowness b*) accounted for only 1, 12 and 18% of the variation in WBS force, respectively. All of these past attempts to predict beef tenderness from carcass measurements used traditional regression techniques and may have failed because carcass measurements do not relate linearly to beef tenderness in the cooked product. Because the prediction of beef tenderness is complex and non-linear, NN modeling may be the best approach to combine multiple carcass measurements for predicting tenderness. Neural networks are mathematical models that learn relationships from example data. They have the ability to find subtle non-linear relationships in data and are adept at solving complex problems with many variables (Hinton 1992). There have been no literature reports of NN modeling of multiple carcass measurements to predict beef muscle tenderness. The theory and specific procedures of NN modeling of meat data have been described in studies to predict marbling score (Brethour 1994), taste panel scores (Park et al. 1994), and pork carcass composition (Berg et al. 1998). The objectives of this study were to develop NN models 1) to predict beef tenderness (WBS) using multiple carcass measurements and 2) to classify carcasses into tenderness categories. To be useful to the beef industry, prediction models should explain at least half the variation (i.e., R 2 > 0.50) in WBS values and classification models should identify most of the toughest carcasses in a population. MATERIALS AND METHODS Several beef data sets ( total n = 1452; Table 1), consisting of carcass measurements paired with tenderness evaluations, were assembled at the Lacombe Research Centre from experiments conducted under controlled conditions between 1985 and Tenderness was expressed objectively as WBS force (Aalhus et al. 1992; Mir et al. 1997) and is the mean of WBS force measurements (kg) taken from three cores sampled across steaks from the LTL muscle aged 6 d then cooked. The cooking method used by the Lacombe

3 HILL ET AL. PREDICTING BEEF TENDERNESS 313 Table 2. Carcass measurements used as input variables in NN modeling Pre-slaughter information Color inputs breed breed specificity (dropped) z 24. tsc24 24-h technician subjective color sex heifer, steer, bull 25. tma24 24-h technician marbling AMSA% 2. agsla age at slaughter 26. tmat technician marbling texture korder kill order 27. a*24 24-h CIE a* (red-green axis) 28. b*24 24-h CIE b* (yellow-blue axis) Carcass weights 29. L*24 24-h CIE L* (lightness) 4. livewt live plant weight (kg) 30. a*6 6-d CIE a* 5. csw24 24-h cooler side weight (kg) 31. b*6 6-d CIE b* 6. hcw hot carcass weight (kg) 32. L*6 6-d CIE L* 7. cs24 24-h cooler shrink (g kg -1 ) 33. C*24 24-h CIE chroma y 34. L*C* h L* chroma ph inputs 35. hu24 24-h CIE hue angle y 8. ph45 45-min ph 36. L*hu24 24-h L* hue 9. ph24 24-h ph 37. C*6 6-d CIE chroma 10. ph6 6-d final ph 38. L*C*6 6-d L* chroma 39. hu6 6-d CIE hue angle Temperature inputs 40. L*hu6 6-d L* hue 11. temp45 45-min (hot) temperature 41. C*_chg change in chroma, 24 h vs. 6 d 12. temp24 24-h (cooler) temperature 42. L*C*_chg change in L* chroma, 24 h vs. 6 d 43. hu_chg change in hue, 24 h vs. 6 d Lab-determined invasive inputs 44. L*hu_chg change in L* hue, 24 h vs. 6 d 13. drip steak drip loss (mg g 1 ) 14. mois moisture content (mg g 1 ) Organ weights 15. imf intramuscular fat (mg g 1 ) 45. h&t head & tongue (kg) 46. kidney kidney (kg) Graded inputs 47. kfat kidney fat (kg) 16. avgfat average fat (mm) 48. liver liver (kg) 17. rea rib eye area GRID (cm 2 ) 49. heart heart (kg) 18. mau grader s marbling score USDA 50. spleen spleen (kg) 19. maa grader s marbling score AMSA% 51. %h&t % head & tongue x 20. mat maturity AMSA% 52. %kid % kidney 21. Jcs Japanese color standard %kfat % kidney fat 22. Jcg Japanese color grade %liv % liver 23. grade Canadian quality grade %hrt % heart AAA, AA, A, B1, B3, B4, 56. %spl % spleen D3, or D4 57. cooking method w z Breed was dropped because it would be a difficult variable to obtain in the deployed, plant situation. y CIE chroma calculated as (a* 2 + b* 2 ); CIE hue calculated as tan 1 (b*/a*) expressed in degrees, not radians. x Percent organ weights calculated as percent of live plant weight. w The cooking method used by Lacombe meat research facility changed from microwave method ( ) to water bath method (1992 present). Cooking method was added as a variable whenever a data set (Bulls, SmTime, combined data sets) contained carcasses cooked by the different methods. meat research facility changed from a microwave method used during the period (Aalhus et al. 1992), to a water bath method used from 1992 to present (Mir et al. 1997). Neural network modeling was performed on individual data sets and on combinations of individual data sets using the Predict v2.0 (NeuralWare Inc., Pittsburgh, PA) NN software platform. Some of the data sets (Bulls, SmTime; Table 1) were combinations of smaller data sets too small to be modeled individually (approximately 200 carcasses were considered necessary for NN modeling). The Kobe data set comprised unique carcasses because the cattle averaged 21 mm grade fat at slaughter. A total of 56 carcass measurements (Table 2) was potentially available for use as inputs in our NN models. However, no one individual data set contained all 56 measurements, as inputs varied from 27 to 54 across data sets (Table 3). The inputs were classified into the following eight groups: pre-slaughter information, carcass weights, ph, temperatures, color, lab-determined invasive inputs, grade inputs, and organ weights. The inputs for ph, temperature and color were measured at different times postmortem (45 min, 24 h, 6 d). An additional input variable, cooking method, was added to certain data sets (Bulls, SmTime, ALL, ALL-K, ALLGA) that contained carcass samples that were cooked by different methods. General descriptions of NN modeling can be found in Lawrence (1993), Smith (1993) and Kohzadi et al. (1995). Briefly, NN modeling using Predict v2.0 software involved: 1) selecting validation, training and test data subsets, 2) analyzing and transforming data, 3) selecting variables, 4) network construction and training, and 5) model verification. The software first partitions data presented to it into subsets as follows: 10% of the data are removed to form an external validation set, then the remaining 90% of the data are divided 80/20% into training and internal test sets. The training set is used to develop NN models and the internal test set is used to automatically adjust NN parameters during training. Both validation and internal test sets were chosen in a

4 314 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 3. Neural Network prediction models for individual and combined data sets Number Validation set x Net Variables used in the NN model v Data set z inputs y R 2 (N) I-H-O w No. Identity Kobe (40) korder, livewt, ph24, ph6, a*6, b*6, imf, avgfat, rea, mat, Jcs, hu24, grade Wagyu (19) cs24, ph24, tsc24, tma24, b*6, L*C*24, hu24, hu6, grade, %kfat Bulls 36 u 0.61 (21) sex, korder, cs24, temp24, b*6, L*C*24, C*6, C*_chg, L*hu_chg, grade SmTime 48 u 0.62 (19) a*24, b*24, a*6, L*hu24, hu_chg, L*hu_chg, cooking method GE (17) agsla, korder, livewt, hcw, cs24, ph24, ph6, temp45, a*24, b*24, L*6, mois, imf, maa, C*24, L*C*24, hu24, L*C*6, C*_chg, grade FCBE (27) agsla, korder, livewt, csw24, cs24, ph24, ph6, tma24, L*6, mois, imf, avgfat, mau, maa, C*6, L*hu6, grade ALL 34 u 0.37 (117) sex, korder, livewt, csw24, cs24, ph6,l*24, b*24, mois, mau, maa, C*24, C*6, L*C*6, L*hu6, cooking method ALL-K 34 u 0.40 (77) sex, cs24, hu24, L*hu24,L*C*6, L*C*_chg, grade ALLGA 34 u 0.45 (99) sex, korder, livewt, cs24, ph6, L*24, b*24, mois, rea, L*C*24, hu24, L*C*6, L*hu6, C*_chg, L*C*_chg, L*hu_chg, cooking method z Data sets described in Table 1. y Number of input variables presented to NN model. x Validation set is a 10% subset of data set aside during model development. Results indicate NN model performance on unseen data. w NN structure, no. neurons in the input-hidden-output layers. v NN models use only some of the input variables presented y. Some variables are transformed and used twice thus the number of variables used is less than the number of input neurons w. u Cooking method included as an input variable in these data sets. round-robin manner to ensure they contained data representative across the whole range of outputs (shears). After subset selection, the software analyzes the data and converts them into linear, binary, or one-of-n coding, as appropriate, then scales and applies transformations to make the distributions of inputs and outputs more similar. Then, variables that contain little or redundant information relative to the problem are eliminated. Predict uses a genetic algorithm to search different combinations of inputs for the best variable set to use. Then a computer simulation of biological neuron layers is created. This involves an input layer (each neuron represents a piece of known data) with weighted connections to a hidden layer (each neuron represents an interaction between inputs) then to an output layer (each neuron represents a possible result). The NN is trained by first supplying input information where the corresponding outputs are known. The NN adjusts itself (via the connection weights) so the output predicted from a set of inputs agrees with the known output. Predict uses proprietary non-linear, feed-forward, constructive algorithms over many iterations to minimize the errors in fitting the weighted connections to the data, then stops training automatically. The 10% validation sets were held separate during training, then, after model development, were used to verify the NN model performance on unseen data. Unless otherwise noted, all results presented here are the NN models performance on validation sets, not on training or internal test sets. Once trained and verified, a NN can be used to predict outputs for input data where the outputs are unknown. Initially, NN models were trained to predict actual shear values directly. Then, NN models were developed to classify carcasses into three or four categories based on shear values. Categories were determined using confidence limits (CL) and shear values were calculated from an extensive consumer survey (Aalhus 1997). The CL and corresponding WBS values were: 80%CL = 5.6 kg, 50%CL = 7.85 kg, and +68%CL = 9.6 kg. The resultant three-category classification was: tender (<5.6 kg), moderate ( kg) and tough (>9.6 kg); the four category classification was: tender (<5.6 kg), probably tender ( kg), probably tough ( kg) and tough (>9.6 kg). For comparison with NN prediction, some data sets were also analyzed by MLR using the PROC REG procedure of the SAS Institute, Inc. (1989) to obtain R 2 values. RESULTS AND DISCUSSION Neural Network Prediction Models The ability of NN models to predict actual shear values varied among the individual data sets (R 2 = ; Table 3). Predictions were poor for the FCBE and Kobe data sets with R 2 = 0.30 and 0.39, respectively. The FCBE data set had only 27 inputs and was missing several important input variables (45-min ph, 24-h CIE a*, b*, and L*). The poor prediction for the Kobe data set (Dubeski et al. 1997a,b,c) was not related to missing variables, but perhaps to the long feeding periods (up to 474 d) and excessive fatness of the carcasses (average grade fat = 21 mm). Prediction of shear values for the other four data sets was consistently good (R 2 = ). The NN results for these four data sets were a significant improvement over the R 2 = previously reported for regression analysis of carcass measurements to predict WBS (Jeremiah 1991; Jones and Tatum 1994; Wulf et al. 1997). Our NN models used 7 21 inputs (Table 3) compared with 2 5 inputs for the previous regression studies. The following variables were used most often in our NN models: kill order, 24-h cooler shrink, 24-h ph, lab-determined intramuscular fat and grade.

5 Fig. 1. Neural network predicted versus actual shears for the ALLGA data set. Neural network prediction of shear values for the three combined data sets (ALL, ALL-K, ALLGA) was poor (R 2 = , Table 3). Considering that predictions of WBS for the individual Wagyu, Bulls, SmTime and GE data sets were quite good (R 2 = ; Table 3), we had expected R 2 approaching 0.60 for the combined data sets. The poor predictions for the combined studies were probably reflective of the diverse conditions across the individual studies. Although restricting the combined data set to only those carcasses that were grade A, AA, or AAA (ALLGA data set; Table 3) improved predictions, R 2 = 0.45 was still judged inadequate for a reliable prediction of tenderness. A plot of the NN-predicted versus actual shear values over all the entire ALLGA data set (n = 991) showed that the NN model under-predicted high shear values and over-predicted low shear values (Fig. 1). This problem was related to the uneven distribution of shears in the ALLGA data set; the NN learned to more accurately predict the more abundant intermediate shears. Another source of error when predicting actual shear values may have been the variation in the mean shear values (7.8 ± 1.5 kg; overall mean ± SD for the ALL data set). Much of this variation was inherent across the steak and was not necessarily a reflection of sampling technique (Dugan and Aalhus 1998). The variation in shear values may have been especially problematic when modeling the combined data sets (i.e., the variation may have been fairly consistent within, but not between, individual data sets). Wheeler et al. (1994, 1996) identified several factors related to sampling, cooking, and coring that can affect the result of shear force evaluation. They recommended sampling six cores per steak as opposed to the three core samplings in the studies reported here. Although NN prediction results on four of the individual data sets were encouraging, it was decided to pursue the carcass classification approach to modeling tenderness because the R 2 < 0.50 on the combined data sets. The combined data sets represent a diversity in breeds, genders, and management factors that occur most days at a packing plant. Given HILL ET AL. PREDICTING BEEF TENDERNESS 315 this diversity, it appears unrealistic to use carcass measurements to predict WBS and account for 50% of the variation in a plant situation. Multiple Linear Regression Prediction Models For comparison with NN prediction, the ALLGA data set (Table 1) was also modeled using MLR. Multiple linear regression across all data (n = 991) gave R 2 = 0.34, which was only slightly less than the R 2 = 0.39 (Fig. 1) obtained for the equivalent NN prediction over all data (the R 2 = 0.45 for ALLGA in Table 3 was over the validation set only). The MLR results indicate that there is some linearity in the problem. The MLR used 10 input variables [sex, kill order, 24-h cooler shrink, 24-h ph, lab-determined moisture, rib eye area, grader s marbling score (USDA), 24-h L* hue, 6-d L* chroma, change in hue 24 h vs. 6 d] compared with 18 inputs (Table 3) for the corresponding NN model. These results confirmed the difficulty in predicting actual shear values and supported our decision to classify carcasses into tenderness categories. Neural Network Classification Models Classification models attempted to classify carcasses into either three (tender, moderate, tough) or four (tender, probably tender, probably tough, tough) tenderness categories based on shear values. Initially, models were developed for the ALL-K, ALL and ALLGA data sets using the three-category system with an equal number of carcasses in each category. These models achieved overall classification accuracies of 57 61%. Of the 34 inputs available, the model for the ALLGA data set used only five inputs: 24-h b*, 6-d L*, lab-determined moisture, 24-h chroma, change in L* chroma 24 h vs. 6 d. When three-category models were developed for the same combined data sets using only those 20 inputs available within 24 h of slaughter (plus cooking method), similar classification accuracies (53 60%) were achieved. The model for the ALLGA data set used six inputs: sex, live plant weight, 24-h cooler shrink, 24-h ph, 24-h L* chroma, 24-h L* hue. This suggested that a lot of redundant information was being presented to the NN if all available inputs were used. Thus, final classification models were developed by presenting only the 24-h inputs (these variables would also be more practical in a plant situation). Cooking method would not be used as an input variable in a plant situation, however, its potential effect on shear values could not be ignored in our modeling. The CL used to calculate the final tenderness categories were: 80%CL = 5.6 kg (any shear < 5.6 kg has an 80% or greater chance of being rated tender by the consumer), 50%CL = 7.85 kg (at 7.85 kg, there is a 50/50 chance that the consumer rating will be slightly tender or slightly tough), and +68%CL = 9.6 kg (any shear > 9.6 kg has a 68% or greater chance of being rated tough). The rationale for using the +68%CL as the limit for the tough category was that anything with >68% rejection is truly tough. For the tender category, 80%CL was used because it would give extra assurance that a carcass classified as tender was, indeed, tender. Others have used similar limits depending on the market segment. For a slightly tender rating,

6 316 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 4. Neural network three- and four-category classification models for the ALLGA data set z Confidence Shear Validation set Net Variables used in NN model limits y Category limits Accuracy N I H O x No. Identity Three-category model: 80% Tender < sex, ph24, rea, mau, 80% to 68% Moderate L*C*24, hu24, +68% Tough > cooking method (Total = 99) Four-category model: 80% Tender < % to 50% Probably tender sex, livewt, hcw, 50% to 68% Probably tough cs24, ph24, b*24, +68% Tough > rea, maa, L*hu24, (Total = 99) grade, cooking method z The ALLGA data set consisted of 991 grade A, AA, AAA carcasses with 21 input variables all available 24 h after slaughter. Cooking method was included as one of the input variables. y From Aalhus (1997) consumer survey. x NN structure, no. neurons in the input-hidden-output layers. Table 5. Linear relationships between the individual 24-h input variables and WBS for the ALLGA data set z Inputs Description r 2 1. sex y heifer, steer, bull NA 2. korder kill order livewt y live plant weight (kg) csw24 24-h cooler side weight (kg) hcw y hot carcass weight (kg) cs24 y 24-h cooler shrink (g/kg) ph45 45-min ph ph24 y 24-h ph temp45 45-min (hot) temperature a*24 24-h CIE a* (red-green axis) b*24 y 24-h CIE b* (yellow-blue axis) L*24 24-h CIE L* (lightness) C*24 24-h CIE chroma L*C*24 24-h L* chroma hu24 24-h CIE hue angle L*hu24 y 24-h L* hue rea y rib eye area GRID mau grader s marbling score USDA maa y grader ;s marbling score AMSA% grade Canadian quality grade 1993 NA 21. cooking method y microwave or water-bath NA z For the ALLGA data set, there were twenty-one 24-h input variables available for modeling WBS. Linear relationships were not applicable (NA) for the binary variables, sex, grade, and cooking method. y The best NN model (Table 4) used these 11 inputs to classify carcasses into four tenderness categories with a mean accuracy of Shackelford et al. (1991) used a 50%CL for retail and a 68%CL for food service. They also pointed out that as carcasses with excessively high shear values are removed from the population, the overall standard deviation will be reduced and these CL will increase. It may be quite reasonable then, in 1999, to aim for 80%CL as a tenderness limit, depending on the market. Neural network classification models, using the CL discussed above, displayed a distinct ability to correctly place carcasses into three (tender, moderate, tough) and four categories (tender, probably tender, probably tough, tough). The three-category system gave good classification accuracies (0.64 for tender, 0.32 for moderate, 0.57 for tough, Table 4). The NN were better able to classify the tender and the tough categories compared with the moderate category. Apparently, it was more difficult for the NN to differentiate an in-between carcass from the population than to differentiate a tender or tough carcass with more extreme characteristics. To improve the classification of the moderate category, a four-category classification system was implemented. The four-category system gave excellent classification accuracies, 0.64 and 0.79, for the tender and tough categories, respectively (Table 4). This compares well with the 75 83% accuracies reported by Park et al. (1994) who used ultrasonic spectral features and two NN outputs classes (tender or not tender) to classify taste panel ratings for 72 carcasses. Our classification accuracies for the probably tender (0.40) and probably tough (0.29) categories were more mediocre, indicating that the NN still had difficulty differentiating carcasses with in-between characteristics. Nevertheless, the ability to predict the tender and tough carcasses in a population will be important for improving consumer satisfaction with beef tenderness. Our four-category classification model required the following 11 inputs (Table 4): sex, live plant weight, hot carcass weight, 24-h cooler shrink, 24-h ph, 24-h CIE color b*, 24-h CIE lightness L* hue angle, rib eye area, grader s marbling score (AMSA%), grade, and cooking method. When the twenty-one 24-h input variables used to develop the four-category NN model were tested for simple linear relationships with WBS, none accounted for more than 15% of the variation in tenderness (Table 5). The variables cs24, L*24, hu24, and L*hu24 had the highest linear coefficients of determination (r 2 = ) but only two of these, cs24 and L*hu24, were actually used in the four-category model. Wulf et al. (1997) also reported poor simple correlations with WBS for several input variables similar to our 24-h inputs: slaughter weight (r = 0.24), hot carcass weight (r = 0.15), 32-h ph (r = 0.34), 27-h L* (r = 0.36), 27-h color a* (r = 0.24), 27-h color b* (r = 0.38), marbling score (r = 0.12). Their highest correlations to WBS were obtained from other inputs, namely temperament rating (r = 0.49), calpastatin at 24 h (r = 0.47) and average daily gain (r = 0.40).

7 HILL ET AL. PREDICTING BEEF TENDERNESS 317 Table 6. Confusion matrix for NN four-category classification of beef carcasses z NN-predicted categories (based on WBS) Probably Probably Classification Tender tender tough Tough x Actual Accuracy y Category (<5.6 kg) ( kg) ( kg) (>9.6 kg) totals 0.64 Tender Probably tender Probably tough Tough Total Potential market Restaurant Regular Tenderize Tenderize retail then retail or hamburger z Matrix automatically provided by Predict software to examine details of NN classifications for the external validation set (99 carcasses) from the ALLGA data set. NN-predicted classification listed on vertical axis (i.e., = 25 carcasses predicted as tender) vs. actual no. carcasses in each category listed on horizontal axis (i.e., = 14 carcasses actually tender). y Classification accuracy of NN for each category, i.e., 9/14 = 0.64 of actual tender carcasses predicted correctly. x Removing the NN-predicted 30 >tough = carcasses would give a ( )/( ) = 0.55 reduction in tough and probably-tough carcasses with a cost (tender and probably-tender misidentified) of (2 + 5)/( ) = The Predict v2.0 NN software also prints out a confusion matrix to allow closer inspection of the classification accuracies (Table 6). The number of actual carcasses in each category (based on WBS) are read on the horizontal axis, while the NN-predicted classifications are listed on the vertical axis. From this matrix, it is apparent that the four-category NN over-predicted the tender category (total 25 instead of 14) and the tough category (total 30 instead of 14), while under-predicting the probably tender and probably tough categories. On the horizontal, the category classification accuracies of 9/14 = 0.64 and 11/14 = 0.79 indicate that the NN could identify the tender and tough carcasses, respectively. In practice, however, it is the NN-predicted column accuracies that are important because in a deployed, plant situation the actual makeup of the NN-predicted tough column would be unknown and all carcasses within it would be treated the same. If the NN-predicted 30 tough carcasses were removed, this would represent a ( )/( ) = 55% reduction in the tough and probably tough carcasses in the population. The cost would be those tender and probably tender carcasses misidentified as tough, i.e. (2 + 5)/ ( ) = 12%. Compared with the three-category system, the advantage of the four-category system is that it partially separates the moderate category into the more specific, probably tender and probably tough categories. However, given the poor accuracies of these intermediate categories at the present, the best use of the four-category NN classification system would be to remove tough carcasses from a population. If accuracies for the probably tender and probably tough categories could be improved, the four-category system could be used to direct carcasses to potential markets. Such a system would allow the NN-predicted tough carcasses to be processed into hamburger or blade tenderized, the probably tough carcasses could be tenderized (blade tenderized or aging) then sold retail, the probably tender carcasses could be designated for the regular retail market, and the tender carcasses could be directed to the restaurant trade. It should be noted that the present study implies that LTL tenderness is a reliable prediction of carcass tenderness. Shackelford et al. (1995) reported that shear force of LTL was not highly related to shear force of other muscles. Thus, systems that accurately predict LTL tenderness are not necessarily good predictors of the tenderness of other muscles. Because LTL accounts for a higher proportion of carcass value, Shackelford et al. (1995) doubted if the benefits of predicting the tenderness of the other muscles could outweigh the costs. CONCLUSIONS AND IMPLICATIONS We have shown NN to be more versatile and superior to conventional statistics for modeling beef tenderness from carcass measurements. The NN technique has also proven to be quite robust given the large (n = 991) and variable ALLGA data set composed of animals from seven different experiments, with different breeds, sexes, management regimes, and ages at slaughter. Neural network modeling of carcass measurements for the direct prediction of WBS values has only limited potential; however, NN classification of carcasses into tenderness categories has very good potential. A NN classification using a four-category system is recommended with the shear value limits of the categories determined from consumer retail surveys of tenderness. Because the NN classification model used carcass measurements collected within 24 h of slaughter, it should be practical to implement such a system in a plant situation. The system could be implemented independently or it could be integrated with data from a computer imaging system and/or a tenderness probe to improve predictions. There will always be a cost involved in any NN-based carcass classification system because some carcasses are misidentified. Such a system would also require continual updating (perhaps every 6 mo) so the NN model could be adjusted for changes in livestock or plant conditions. Jeremiah et al. (1992) reported, on average, a 23% unacceptable palatability rating given by consumers to grade A, AA, AAA Canadian beef. If the results of this study could be applied to everyday plant situations, a 55% reduction in the number of tough and probably tough carcasses could be expected. This should translate into a 10 12% unacceptable

8 318 CANADIAN JOURNAL OF ANIMAL SCIENCE rating, a considerable improvement over the current 23% rating. ACKNOWLEDGMENTS We thank Jennifer Aalhus and Lyle Rode for their advice and consultations about beef tenderness. We also thank the scientists and technicians of the Lacombe Meat Research Centre who, over the period , collected the carcass data used in this project. Financial support from the Beef Industry Development Fund (Research Component) of the Alberta Agriculture Research Institute is gratefully acknowledged. Aalhus, J. L Tenderness strategy Interim report to CCA. Lacombe Research Centre, Agriculture and Agri-Food Canada, Lacombe, AB. March pp. Aalhus, J. L., Jones, S. D. M., Tong, A. K. W., Jeremiah, L. E., Robertson, W. M. and Gibson, L. L The combined effects of time on feed, electrical stimulation and aging on beef quality. Can. J. Anim. Sci. 72: Berg, E. P., Engel, B. A. and Forrest, J. C Pork carcass composition derived from a neural network model of electromagnetic scans. J. Anim. Sci. 76: Brethour, J. R Estimating marbling score in live cattle from ultrasound images using pattern recognition and neural network procedures. J. Anim. Sci. 72: Dubeski, P. L., Aalhus, J. L., Jones, S. D. M., Robertson, W. M. and R. S. Dyck. 1997a. Meat quality of heifers fattened to heavy weights to enhance marbling. Can. J. Anim. Sci. 77: Dubeski, P. L., Aalhus, J. L., Jones, S. D. M., Tong, A. K. W. and Robertson, W. M. 1997b. Fattening heifers to heavy weights to enhance marbling: Efficiency of gain. Can. J. Anim. Sci. 77: Dubeski, P. L., Jones, S. D. M., Aalhus, J. L. and Robertson, W. M. 1997c. Canadian, American, and Japanese carcass grades of heifers fed to heavy weights to enhance marbling. Can. J. Anim. Sci. 77: Dugan, M. E. R. and Aalhus, J. L Beef tenderness: within longissimus thoracis et lumborum steak variation as affected by cooking method. Can. J. Anim. Sci. 78: Hinton, G. E How neural networks learn from experience. Sci. Am. Sept: Huffman, K. L., Miller, M. F., Hoover, L. C., Wu, C. K., Brittin, H. C. and Ramsey, C. B Effect of beef tenderness on consumer satisfaction with steaks consumed in the home and restaurant. J. Anim. Sci. 74: Jeremiah, L. E., Tong, A. K. W. and Gibson, L. L The usefulness of muscle color and ph for segregating beef carcasses into tenderness groups. Meat Sci. 30: Jeremiah, L. E., Tong, A. K. W., Jones, S. D. M. and McDonell, C Consumer acceptance of beef with different levels of marbling. J. Consumer Studies Home Econ. 16: Jeremiah, L. E., Tong, A. K. W., Jones, S. D. M. and McDonell, C A survey of Canadian consumer perceptions of beef in relation to general perceptions regarding foods. J. Consumer Studies Home Econ. 17: Jones, B. K. and Tatum, J. D Predictors of beef tenderness among carcasses produced under commercial conditions. J. Anim. Sci. 72: Jones, S. D. M, Schaefer, A. L., Robertson, W. M. and Vincent, B. C The effects of withholding feed and water on carcass shrinkage and meat quality in beef cattle. Meat Sci. 28: Kohzadi, N., Boyd, M. S., Kaastra, I., Kermanshahi, B. S. and Scuse D Neural networks for forecasting: An introduction. Can. J. Agric. Econ. 43: Lawrence, J Introduction to neural networks. Design, theory, and applications. 5th ed. Calif. Sci. Software Press, Nevada City, CA. 324 pp. May S. G., Dolezal, H. G., Gill, D. R., Ray, F. K. and Buchanan, D. S Effects of days fed, carcass grade traits, and subcutaneous fat removal on postmortem muscle characteristics and beef palatability. J. Anim. Sci. 70: Mir, P. S., Bailey, D. R. C., Mir, Z., Jones, S. D. M., Entz, T. Husar, S. D., Shannon, N. H. and Robertson, W. M Effect of feeding barley based diets on animal performance, carcass characteristics and meat quality of crossbred beef cattle with and without Waygu genetics. Can. J. Anim. Sci. 77: Newman, J. A., Rahnefeld, G. W., Tong, A. K. W., Jones, S. D. M., Fredeen, H. T., Weiss, G. M. and Bailey, D. R. C Slaughter and carcass traits of calves from first-cross and reciprocal back-cross beef cows. Can. J. Anim. Sci. 74: Park, B., Chen, Y. R., Whittaker, A. D., Miller, R. K. and Hale, D. S Neural network modeling for beef sensory evaluation. Trans. ASAE. 37(5): SAS Institute, Inc SAS/STAT user s guide, Version 6, 4th ed. Vol. 2. SAS Institute, Inc., Cary, NC. Shackelford, S. D., Morgan, J. B., Cross, H. R. and Savell, J. W Identification of threshold levels for Warner-Bratzler shear force in beef top loin steaks. J. Muscle Foods 2: Shackelford, S. D., Wheeler, T. L. and Koohmaraie, M Relationship between shear force and trained sensory panel tenderness ratings of 10 major muscles from Bos indicus and Bos taurus cattle. J. Anim. Sci. 73: Shackelford, S. D., Wheeler, T. L. and Koohmaraie, M Tenderness classification of beef: I. Evaluation of beef longissimus shear force at 1 or 2 days postmortem as a predictor of aged beef tenderness. J. Anim. Sci. 75: Smith, M Neural networks for statistical modeling. Van Nostrand Reinhold, New York, NY. 235 pp. Thomson, C. A., Jones, S. D. M., Schaefer, A. L., Colyn, J. and Robertson, W. M Use of rumen-protected amino acids to improve carcass composition of feedlot cattle. Final technical report to Alberta Agricultural Research Institute Matching Grants Program, Project #AARI94M pp. Wheeler, T. L., Koohmaraie, M., Cundiff, L. V. and Dikeman, M. E Effect of cooking and shearing methodology on variation in Warner-Bratzler shear force values in beef. J. Anim. Sci. 72: Wheeler, T. L., Shackelford, S. D. and Koohmaraie, M Sampling, cooking, and coring effects on Warner-Bratzler shear force values in beef. J. Anim. Sci. 74: Wulf, D. M., O Connor, S. F., Tatum, J. D. and Smith, G. C Using objective measures of muscle color to predict beef longissimus tenderness. J. Anim. Sci. 75: