Óscar López -Campos Lacombe Research Centre Oscar.LopezCampos@AGR.GC.CA olopezcampos@gmail.com

1. Agriculture and Agri-Food Canada Agriculture et Agroalimentaire Canada Beef Grading in Canada: Past, Present and Future ÓscarLópez-CamposLacombe Research CentreOscar.LopezCampos@AGR.GC.CAolopezcampos@gmail.com May 24th, 2014 CMSA/CMC Symposium Nuria Prieto Lacombe Research CentreNuria.Prieto@AGR.GC.CA

2. Classificationsystemisthesorting of carcasses according to given parameters describing all commercially important traits of the carcass Grading system places carcasses with similar characteristics into commercial groups ClassificationorGrading?

3. GRADE NAMES FOR FRESH FRUIT Classification and Gradingsystems in our industries Source: Fresh Fruit and Vegetable Regulations

4. GRADE NAMES FRESH VEGETABLES Classification and Gradingsystems in our industries Source: Fresh Fruit and Vegetable Regulations

5. LUMBER GRADE STAMPS Classification and Gradingsystems in our industries Source: Canadian Wood Council WESTERN CANADIAN WHEAT CLASSES Source: Canadian Grain Commission

7. CANADIAN BEEF GRADING SYSTEM

8. The Past 1992 1972-1990 1941-1958 1938 1927-1929 Source: CBGA

9. The Past Present grading sytem • 1992 Beef Grading Regulations -Major Revision-

10. The Past Before 1996: - AAFC delivery - Federal grading Regulations - Cost Recovery 1996: - Government and industry consensus to create CBGA Since 1996: - CBGA Delivery - Federal grading Regulations - Cost Recovery • reduced cost by $1M Source: CBGA

11. The Present • Canadian Beef Grading Agency (CBGA) Influenceonquality Key factors • Sex Tenderness • Age (Maturity) Tenderness • Conformation (Muscling) Yield • Fat (colour, texture and cover) Acceptability / yield • Meat (colour, texture and cover) Acceptability / quality

12. Carcass Quality Segregation The Present Carcass • Canadian Beef Grading Agency (CBGA) The grading system is based on a series of pass/fail tests To get the top grade you have to pass all the tests If you fail at any steps the carcass is downgraded Influenceonquality Key factors Too masculine Steer / Heifer • Sex Tenderness Mature Youthful • Age (maturity) Tenderness Deficient Well muscled fat • Conformation (muscling) Yield Yellow or lacking Quality • Fat (colour, texture and cover) Acceptability / yield Dark or soft Bright red • Meat (colour, texture and cover) Acceptability / quality Marbling

13. Downgrades symbols D grades A Maturity (Age) B4 C Meat Colour F+ Excessive Fat thickness F- Insufficient Fat thickness M Marbling O Soft (not firm) B3 and D2 SStaggy T Muscling (Type) B2 and D2 Y Fat colour (Yellow)

14. 13 QualityGrades

15. Lean meatyieldestimation • Prime AAA AA A Researchworkat theLacombeResearch Centreontheactual dissectionof 540 carcasses Lean % = 63.65 + 1.05 (musclescore) - 0.76 (grade fat) Length Canada 1 ≥59 Yield grade Canada2 58 to 54 Width Canada 3 ≤53 Fatclass

16. “The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog.The dog will be there to keep the man from touching the equipment.” – Warren G.Bennis • “90% of what is considered impossible is, in fact, possible. The other 10% will become possible with the passage of time and technology.” – • Hideo Kojima • The FUTURE

17. Electromagnetic radiation Spectrum VisionAnalysisSystems

18. VisionAnalysisSystems Whole Carcass Rib eye Research Management Systems / Computer Vision System • E+V® Technology GmbH VBS 2000 VGB 2000

19. Electromagnetic radiation Spectrum Dual EnergyX-ray Absorptiometry DEXA

20. Dual EnergyX-rayAbsorptiometry DEXA Carcassisscanned X-rayimageisgenerated Grey scaleinformation Carcasscutout Scans are non-destructive using low energy X-rays Predictionequations Fat Lean Bone

21. Agriculture and Agri-Food Canada Agriculture et Agroalimentaire Canada “Measuring the Canadian Beef Advantage: Development of a platform technology for rapid, non-invasive carcass fat and lean predictions in beef carcass” Total n = 240 finisher cattle: n = 160 • Ist stage (building prediction equations) n = 80 • 2nd stage (to validate the equations) n = 100 • 3rdstage (to validate commercial) Animals serially slaughtered weight 900 – 1,600 lb back fat depths 1 – 21 mm Calf-fed and Yearling Implanted and non implanted

22. Agriculture and Agri-Food Canada Agriculture et Agroalimentaire Canada DEXA TECHNOLOGY FOR A RAPID, NON-INVASIVE CARCASS FAT AND LEAN PREDICTION IN BEEF Lean Yield Defined BONELESSCLOSELY TRIMMED RETAIL CUTS, USA (Saleable) SUB-PRIMALS HIND DISSECTED LEAN CANADA (Efficiency)

23. Agriculture and Agri-Food Canada Agriculture et Agroalimentaire Canada USING DEXA TECHNOLOGY FOR A RAPID, NON-INVASIVE CARCASS FAT AND LEAN PREDICTION IN BEEF Relationship (R2)a between DEXA values and the traditional carcass cut-out for lean, fat and bone of the different primal cuts (n=158). aR2: coefficient of determination

24. Agriculture and Agri-Food Canada Agriculture et Agroalimentaire Canada USING DEXA TECHNOLOGY FOR A RAPID, NON-INVASIVE CARCASS FAT AND LEAN PREDICTION IN BEEF • CONCLUSIONS • The results of the present study suggest that DEXA technology has the potential to estimate beef carcass traits, particularly total fat and lean. • Studies are ongoing to improve and validate calibration curves to increase the prediction accuracy for use in beef populations.

25. Electromagnetic radiation Spectrum NearInfrared Spectroscopy (NIRS)

26. Monochromatic light Absorbance = log (1/Reflectance) NearInfraredSpectroscopy(NIRS) 1.8 O-H 1.6 N-H O-H N-H 1.4 1.2 C-H 1.0 Absorbance 0.8 Usefulanalyticalregion C-H 0.6 Usefulanalyticalregion 0.4 0.2 Photodetector Photodetector 0.0 Quartz 2468 1100 1172 1244 1316 1388 1460 1532 1604 1676 1748 1820 1892 1964 2036 2108 2180 2252 2324 2396 Sample Wavelength (nm) Cell

27. Using NIRS technology to segregate B4’s B4’s = 60 n = 120 95% of the samples were correctly classified AAA, AA, A = 60

28. NIRS spectrum for beef NIRS technology 1.8 O-H 1.6 N-H O-H N-H 1.4 Meat quality parameters 1.2 C-H 1.0 Absorbance 0.8 Usefulanalyticalregion C-H 0.6 Usefulanalyticalregion 0.4 0.2 0.0 2468 Organic compounds Animal tissues 1100 1172 1244 1316 1388 1460 1532 1604 1676 1748 1820 1892 1964 2036 2108 2180 2252 2324 2396 Wavelength (nm)

29. The FUTURE 1.8 O-H 1.6 N-H O-H N-H 1.4 1.2 C-H 1.0 Absorbance 0.8 Usefulanalyticalregion C-H 0.6 Usefulanalyticalregion 0.4 0.2 0.0 2468 1100 1172 1244 1316 1388 1460 1532 1604 1676 1748 1820 1892 1964 2036 2108 2180 2252 2324 2396 Towardan International System? Wavelength (nm)

30. “The assessment of carcass merit is complex and no single approach or set of criteria will ideally suit all the objectives.” Kempster et al. 1982 “Grading has to group carcasses in order of excellence in accordance with the needs of the day.” Naude et al. 1990

31. The Future 2014-Future 1992-2014 1990 1947 1927-1929 Source: CBGA

32. E • Prime AAA AA A • B1 B2 B3 B4 • D1 D2 D3 Certificated brands

33. The primary purpose of beef grading is to facilitate trade by describing the commercially important attributes of the carcass.

34. Thank you for your attention ÓscarLópez-Campos • Lacombe Research Centre • Telephone 403-782-8195 • Facsimile 403-782-6120 • Oscar.LopezCampos@AGR.GC.CA • olopezcampos@gmail.com