Prediction of lean yield in cull sows

1993 ◽  
Vol 73 (4) ◽  
pp. 839-845 ◽  
Author(s):  
N. N. Aziz ◽  
W. A. Rae ◽  
R. O. Ball
Keyword(s):  
Cross Section ◽  
Prediction Accuracy ◽  
Lumbar Vertebrae ◽  
Carcass Composition ◽  
Coefficient Of Determination ◽  
Single Measurement ◽  
Electronic Probe ◽  
Fat Thickness ◽  
Cull Sows ◽  
Lean Weight

Data from 204 sows were used to predict percentage carcass lean yield and lean weight. Backfat thickness (probe fat) and muscle depth (probe lean) were measured with an electronic probe. Fat thickness was also measured by ruler at the midline at maximum fat depth over the lumbar vertebrae (maximum fat), fat depth at the last rib (last-rib fat), and fat depth between the 3rd- and 4th-last ribs (fat depth 3–4). Fat depth over maximum loin-muscle depth (loin fat 1), maximum fat depth over loin muscle (loin fat 2), maximum loin depth (loin lean 1) and maximum loin width (loin lean 2) were measured on loin cross section. On the warm carcass, the prediction accuracy of percentage lean yield was highest for probe fat (R2 = 0.77), whereas probe lean had the lowest coefficient of determination (R2 = 0.01). Among the ruler measurements, maximum fat was associated with the most accurate prediction of percentage lean yield (R2 = 0.71). Among cross-section measurements, loin fat 2 was the most accurate predictor of percentage lean yield (R2 = 0.78). For predicting lean weight in the carcass, carcass weight gave the highest coefficient of determination (R2 = 0.82) of any single measurement, but addition of probe fat to the equation improved R2 by 11% and reduced the RSD from 3.16 to 2.00. A single measurement by probe (probe fat) or ruler (maximum fat) was concluded to be sufficient to accurately predict percentage lean yield in sow carcasses. Key words: Lean yield, sows, prediction, carcass composition, grading

An assessment of carcass probes for use in Great Britain for the EC pig Carcass Grading Scheme

1989 ◽  
Vol 48 (2) ◽  
pp. 427-434 ◽  
Author(s):  
G. L. Cook ◽  
J. P. Chadwick ◽  
A. J. Kempster
Keyword(s):  
Great Britain ◽  
Subcutaneous Fat ◽  
Optical Probe ◽  
Carcass Composition ◽  
Coefficient Of Determination ◽  
The Third ◽  
Slaughter Pigs ◽  
Fat Thickness ◽  
Statistical Criteria ◽  
Do So

ABSTRACTTo gain approval for use in the revised European Community (EC) Pig Grading Scheme to be introduced in 1989, methods of estimating carcass lean proportion must be shown to do so with a coefficient of determination greater than 0·64 and a residual s.d. of less than 25 g/kg. A trial was carried out to assess a number of methods for use in the EC Scheme as applied in Great Britain. Subcutaneous fat and m. longissimus depths at the head of the last rib and at the third/fourth from last rib were measured using the optical probe (OP), the Fat-O-Meater (FOM), the Hennessy Grading Probe II (HGP) and the Destron PG-100 Probe (DST) on a broad sample of 162 commercial carcasses representative of the ranges in fatness and weight found nationally. The left side of each carcass was separated into component tissues. Although the instruments all achieved similar levels of accuracy in predicting carcass lean proportion, some differences were found. The DST just failed to reach the required statistical criteria for approval in the EC Scheme. The results for the other three instruments were submitted to Brussels as evidence of suitability and they have been approved.Using the regression relationships found between carcass composition and fat thickness together with results from earlier studies, it was estimated that the carcass separable fat proportion of British slaughter pigs has fallen at the annual rate of 7 g/kg since 1975.

THE USE OF UREA DILUTION AND ULTRASONIC BACKFAT THICKNESS TO PREDICT THE CARCASS COMPOSITION OF LIVE LAMBS AND CATTLE

1982 ◽  
Vol 62 (2) ◽  
pp. 371-379 ◽  
Author(s):  
S. D. M. JONES ◽  
J. S. WALTON ◽  
J. W. WILTON ◽  
J. E. SZKOTNICKI
Keyword(s):  
Subcutaneous Fat ◽  
Carcass Composition ◽  
Multiple Regression Equation ◽  
Lean Tissue ◽  
Live Animal ◽  
Subcutaneous Fat Thickness ◽  
Fat Thickness ◽  
Total Fat ◽  
The Relationship ◽  
Lean Weight

Thirty-eight lambs (22 rams, 16 ewes), 25 Holstein cows and 30 steers were evaluated ultrasonically for subcutaneous fat thickness. Urea space was also estimated, using the dilution principle, by a single injection of a known amount of urea and taking a single blood sample 12 min later. All lambs and cattle were slaughtered within 2 days and the half-carcasses were separated into fat, lean and bone. Urea space (R2 = 0.10) and fat thickness (R2 = 0.18) in lambs were poorly related to the weight of half-carcass lean tissue. Neither urea space nor fat thickness improved the level of explained variation in half-carcass lean tissue weight over that explained by liveweight alone (R2 = 0.73). Urea space showed a larger association with half carcass lean weight in cows (R2 = 0.55) than in steers (R2 = 0.14), but again did not improve the relationship provided by liveweight alone (R2 = 0.60). Fat thickness provided nonsignificant regressions (P < 0.05) with half-carcass lean weight both in cows and in steers. Liveweight was the dominant independent variable (R2 = 0.33) for predicting total fat in lamb half-carcasses; urea space (R2 = 0.08) and fat thickness (R2 = 0.13) did not improve the prediction given by liveweight alone. Fat thickness was poorly related to total fatness both in steers (R2 = 0.12) and in cows (nonsignificant regression). A multiple regression equation combining fat thickness and liveweight provided the best prediction of half-carcass fat in cows, whereas a similar equation with the addition of urea space gave the best prediction of half-carcass fat in steers. The measurement of urea space and fat thickness to predict the weight of carcass tissues (lean, fat) in live lambs and cattle over the weight (41.9 + 9.7 kg(SD) lambs, 624 ± 62.8 kg cows and 466 ± 63.2 kg steers) and fatness (19.9 ± 3.27% (SD) lambs, 21.9 ± 2.18% cows and 20.9 ± 3.98% steers) ranges studied was of limited value. Key words: Urea dilution, ultrasound, live animal evaluation, carcass composition

Estimation of lamb carcass composition using an electronic probe, a visual scoring system and carcass measurements

1992 ◽  
Vol 72 (2) ◽  
pp. 237-244 ◽  
Author(s):  
S. D. M. Jones ◽  
L. E. Jeremiah ◽  
A. K. W. Tong ◽  
W. M. Robertson ◽  
L. L. Gibson
Keyword(s):  
Scoring System ◽  
Muscle Thickness ◽  
Visual Assessment ◽  
Carcass Composition ◽  
Visual Scoring ◽  
Electronic Probe ◽  
Fat Thickness ◽  
Variation Explained ◽  
Total Tissue ◽  
Probe Measurements

Sixteen hundred and sixty lambs were used to determine the precision of carcass measurements (fat thickness, muscle thickness, tissue depth) and a visual scoring system for muscle and fat thickness to estimate carcass composition. Measurements of fat (F) and muscle (M) thickness were made in warm and cold carcasses and total tissue depth in warm carcasses only between the 10th and 11th ribs and the 12th and 13th ribs using an electronic probe (Hennessy Grading Probe HGP). F explained 40–64% of the variation in carcass lean and 44–72% of the variation in carcass fat depending on the location and number of measurements and whether they were made on a warm or cold carcass. In most cases when M was added to F there was no increase in the variation explained in composition over that provided by F alone. Total tissue depth measurements differed in precision for the prediction of carcass lean content with the 12th rib being superior to the 10th rib (RSD for 12th rib, 33.2 g kg−1; 10th rib, 36.6 g kg−1). Visual assessment of carcasses for fatness had the lowest precision for the prediction of lean content (RSD, 44.5 g kg−1). Loin eye area and fat thickness measured at the 12th rib had similar precision for the estimation of lean content as probe measurements. It was concluded that probe measurements of F or tissue depth between the 12th and 13th ribs would provide a superior method to the visual assessment of carcass fatness used in this study for classifying lamb carcasses for lean content and would allow carcasses to be graded on the slaughter floor. Key words: Lamb, carcass, grading, Hennessy Grading Probe, composition

Carcass composition of cull sows

1993 ◽  
Vol 73 (4) ◽  
pp. 829-838 ◽  
Author(s):  
N. N. Aziz ◽  
W. A. Rae ◽  
R. O. Ball ◽  
J. W. Allan
Keyword(s):  
Significant Influence ◽  
Live Weight ◽  
Carcass Composition ◽  
Carcass Weight ◽  
Backfat Thickness ◽  
Class 1 ◽  
Electronic Probe ◽  
Fat Yield ◽  
Commercial Yield ◽  
Cull Sows

Two hundred and four sows were slaughtered in seven weight classes (WCs) from < 99.9-kg to > 225-kg carcass weight in 25-kg increments and 11 fat classes (FCs) from < 9.9-mm to > 55-mm backfat depth in 5-mm increments. Backfat thickness (probe fat) and loin muscle depth (probe lean) were measured on the left side of the carcass between 3rd and 4th last rib 7 cm from midline by electronic probe. The left side was cut into four primals: shoulder, ham, loin and belly. Shoulder, ham and loin were then separated into trimmed commercial cuts to determine commercial yield and then defatted and deboned to determine retail yield, lean yield, fat yield and bone yield. Dressing percentage was lowest for those sows in fat class 1 (77.4%) and highest for those in fat class 11 (83.7%). Percentage of shoulder and ham in the carcass side decreased, while the percentage of the loin and belly increased as WC and FC increased. FC produced significant effect on the percentage of the shoulder, loin and belly, whereas WC had significant influence only on the proportion of the shoulder and belly. There was a significant WC × FC interaction (P < 0.002) upon the percentage of the belly yield. The percentages of commercial yield, retail yield, lean yield and bone yield were reduced, and that of fat yield increased as WC and FC increased, but WC only produced significant effects on the percentage of lean and bone yield. Carcass composition of cull sows was better correlated to backfat thickness than carcass weight, since the increase in carcass weight as live weight increased was primarily fat. Key words: Carcass composition, sows, lean yield

The use of carcass cuts to predict beef carcass composition: a research technique

1981 ◽  
Vol 32 (6) ◽  
pp. 987 ◽  
Author(s):  
ER Johnson ◽  
DD Charles
Keyword(s):  
Multiple Regression ◽  
Prediction Accuracy ◽  
Carcass Composition ◽  
Standard Errors ◽  
Regression Analyses ◽  
Slight Improvement ◽  
B Values ◽  
Bone Content ◽  
Fat Thickness ◽  
Carcass Cuts

Eleven Angus, 12 Friesian and 12 Hereford steers were used to investigate the degree of accuracy and usefulness of primal cut tissues in predicting side composition. The criteria used for evaluating the cuts were: (a) standard error of estimate of the equation, (b) homogeneity of 'b' values among breeds, (c) appreciable bone content in cut to allow the prediction of side bone, and (d) the absence of major difficulties in the replication and dissection of cuts. Simple and multiple regression analyses showed that the most accurate predictors of carcass composition, in descending order, with standard errors of estimate of muscle, fat and bone percentages respectively, were: hindquarter plus rib cut (0.37 %, 0.47 %, 0.30 %); hindquarter (0.73 %, 0.87%, 0.49%); loin plus rib cut (0.84%, 0.88%, 0.48%); rib cut (1.13%, 1.26%, 0.59%); loin (1.24%, 1.21 %, 0.72%). The most useful of four easily obtained carcass variables in improving the prediction accuracy of carcass components from multiple regression proved to be primal cut weight and fat thickness at the 12th rib, particularly the former. Both significantly reduced the standard errors of estimate of muscle, fat or bone in equations based on loin, rib cut and loin plus rib cut, but not in equations based on hindquarter plus rib cut and hindquarter. Kidney plus pelvic fat weight was of limited value, resulting only in a slight improvement in the prediction of side bone percentage using the equations based on bone percentage of the hindquarter. Carcass weight was of equal value to primal cut weight in improving the prediction accuracy of multiple regression. Five sets of part-carcass prediction equations are given, providing a choice of prediction accuracy, labour expenditure and cost for research workers whose requirements and resources may vary.

scholarly journals Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

2021 ◽  
Vol 13 (7) ◽  
pp. 3870
Author(s):  
Mehrbakhsh Nilashi ◽  
Shahla Asadi ◽  
Rabab Ali Abumalloh ◽  
Sarminah Samad ◽  
Fahad Ghabban ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Sustainability Assessment ◽  
Regression Trees ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Sustainability Performance ◽  
Learning Techniques ◽  
Self Organizing

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

Estimation of the carcass composition of different cattle breeds and crosses from fatness measurements and visual assessments

1986 ◽  
Vol 106 (2) ◽  
pp. 223-237 ◽  
Author(s):  
A. J. Kempster ◽  
J. P. Chadwick ◽  
D. D. Charles
Keyword(s):  
Subcutaneous Fat ◽  
Visual Assessment ◽  
Carcass Composition ◽  
Beef Breed ◽  
Percentage Unit ◽  
Wide Range ◽  
Fat Thickness ◽  
Substantial Bias ◽  
Visual Assessments ◽  
Thickness Measurements

SUMMARYCarcass data for 1053 steers from the Meat and Livestock Commission's beef breed evaluation programme were used to examine the relative precision of alternative fatness assessments for predicting carcass lean percentage. The data were from four trials and comprised both dairy-bred and suckler-bred cattle by a wide range of sire breeds.A visual assessment of carcass subcutaneous fat content to the nearest percentage unit (SFe) was the single most precise predictor both overall (residual S.d. = 2·28) and within breed (residual S.d. = 2·05). Precision was improved by the addition in multiple regression of the percentage perinephric and retroperitoneal fat (KKCF) in carcass, a visual score of the degree of marbling in the m. longissimus and selected fat thickness measurements taken by calipers on cut surfaces (residual S.d. = 2·11 (overall) and 1·90 (within breed)).When the best overall equation was applied to the breed means, there was substantial bias (predicted – actual carcass lean percentage). Biases ranged from +2·5 (purebred Canadian Holstein and Luing) to – 1·3 (Limousin crosses).Breeds differed significantly in carcass lean content when compared at equal levels of fatness measurements. The differences depended both on the precision with which the measurements predicted carcass lean content and the observed differences in carcass composition that existed before adjustments to equal fatness were made.The robustness of prediction equations was examined by applying them to independent sets of data (a total of 334 carcasses) from four other trials involving steers, heifers, cows and young bulls. Equations were stable for cattle of the same breed, sex and similar levels of fatness but important bias was found between more extreme types of cattle.

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

An evaluation of the Hennessy and Chong Fat Depth Indicator, and the Ulster Probe, for use in pig carcass classification and grading

1981 ◽  
Vol 33 (3) ◽  
pp. 319-324 ◽  
Author(s):  
A. J. Kempster ◽  
J. P. Chadwick ◽  
D. W. Jones ◽  
A. Cuthbertson
Keyword(s):  
Standard Deviation ◽  
Multiple Regression ◽  
Lumbar Vertebrae ◽  
Optical Probe ◽  
Carcass Weight ◽  
Residual Standard Deviation ◽  
Fat Thickness ◽  
Prediction Residual ◽  
Thickness Measurements ◽  
Cut Surface

ABSTRACTThe Hennessy and Chong Fat Depth Indicator and the Ulster Probe automatic recording instruments developed for measuring fat thickness were tested against the optical probe for use in pig carcass classification and grading.Fat thickness measurements were taken using each probe 60 mm from the dorsal mid-line over the m. longissimus at the positions of the 3rd/4th lumbar vertebrae, 3rd/4th last ribs and last rib on a total of 110 hot carcasses covering the range of market weights in Great Britain. The standard deviation of carcass lean proportion at equal carcass weight was 35·4 g/kg.The instruments differed little in the precision of carcass lean proportion prediction: residual standard deviation (g/kg) for the multiple regression with carcass weight and the best individual fat measurement for each probe were: last rib optical probe, 22·1; last rib Ulster Probe, 22·7; and 3rd/4th last rib Fat Depth Indicator, 21/6. Residual standard deviation (g/kg) for carcass lean proportion prediction from carcass weight and all three fat measurements in multiple regression were 21·3 optical probe, 21·3 Ulster Probe and 201 Fat Depth Indicator.Similar mean fat measurements were obtained from the optical probe and Fat Depth Indicator, and for these instruments, but to a lesser extent for the Ulster Probe, the regression relationships with each other and with fat thickness measurements taken on the cut surface of the cold carcass were also similar.The differences recorded in precision are unlikely to be sufficiently important to influence the choice of one probe rather than another.

scholarly journals The First Identification and Some Carcass Characteristics of the 7 Lumbar Vertebrae in Sheep in Turkey

2021 ◽  
Author(s):  
Omer Faruk Gungor ◽  
Necmettin Unal ◽  
Ceyhan Ozbeyaz
Keyword(s):  
Carcass Traits ◽  
Lumbar Vertebrae ◽  
Carcass Characteristics ◽  
Lean Weight

Abstract The purpose of this study was to draw attention to the number of the lumbar vertebrae in R1 BBA crosses (Bafra x F1 (Bafra x Akkaraman)) lambs (75% Bafra and 25% Akkaraman), and the effect of 7 lumbar vertebrae on some carcass traits. Even though some studies reported that the number of the lumbar vertebrae might be considerably different dependent on the sheep genotype, this has not been reported for Turkish breeds. While a study on the R1 BBA lambs has been performed, seven lumbar vertebrae have been identified in four of the eighteen lambs. The means of the carcass length (80.800±0.583 and 84.375±1.375 cm) (P= 0.036), leg weight (5.942±0.079 and 6.209±0.052 kg) (P= 0.032), loin weight (1.560±0.096 and 1.849±0.060 kg) (P= 0.048), and loin’s lean weight (0.875±0.059 and 1.058±0.032 kg) (P= 0.040) were statistically different between the groups of lambs (6 and 7 lumbar vertebrae, respectively). In conclusion, the number of lumbar vertebrae has economically affected important parts of the carcass.

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