Agriculture and Agriculture et Food Canada Agroalimentaire Canada

Agriculture and Agriculture et -Food Canada Agroalimentaire Canada Beef Grading in Canada: Past, Present and Future May 24 th, 2014 CMSA/CMC Symposium Óscar López-Campos Lacombe Research Centre Oscar. Lopez. Campos@AGR. GC. CA olopezcampos@gmail. com Nuria Prieto Lacombe Research Centre Nuria. Prieto@AGR. GC. CA

Classification or Grading? Classification system is the sorting of carcasses according to given parameters describing all commercially important traits of the carcass Grading system places carcasses with similar characteristics into commercial groups 2

Classification and Grading systems in our industries Column III Item Kind 1. Apples Import Grade Name (a) Extra Fancy Grade Canada Grade Standard (a) Canada Extra Fancy (b) Fancy Grade (c) Commercial Grade (a) No. 1 Grade (b) Domestic Grade (b) Canada Fancy (c) Canada Commercial (a) Canada No. 1 (b) Canada Domestic (c) Canada Hailed Canada No. 1 2. Apricots 3. Blueberries (c) Hailed Grade No. 1 Grade 4. Cantaloups No. 1 Grade Canada No. 1 5. Cherries (a) No. 1 Grade (b) Domestic Grade (a) Canada No. 1 (b) Canada Domestic (c) Orchard Run Grade (c) Canada Orchard Run 6. Crabapples (a) No. 1 Grade (b) Domestic Grade (a) Canada No. 1 (b) Canada Domestic 7. Cranberries (a) No. 1 Grade (b) Domestic Grade (a) Canada No. 1 (b) Canada Domestic 8. Grapes 9. Peaches (a) No. 1 Grade (b) Domestic Grade (a) Canada No. 1 (b) Canada Domestic 10. Pears (a) Extra Fancy Grade (a) Canada Extra Fancy (b) Fancy Grade (c) Commercial Grade (a) No. 1 Grade (b) Domestic Grade No. 1 Grade (b) Canada Fancy (c) Canada Commercial (a) Canada No. 1 (b) Canada Domestic Canada No. 1 11. Plums and Prunes 12. Field Rhubarb 13. Strawberries GRADE NAMES FOR FRESH FRUIT Source: Fresh Fruit and Vegetable Regulations

Classification and Grading systems in our industries Column I Item Kind 1. Asparagus 2. 3. 4. 5. 6. 7. 8. 9. Column III Import Grade Name (a) No. 1 Grade (b) No. 1 Slender Grade Canada Grade Standard (a) Canada No. 1 (b) Canada No. 1 Slender (c) No. 2 Grade (a) No. 1 Grade (b) No. 2 Grade Brussels Sprouts (a) No. 1 Grade (b) No. 2 Grade (a) No. 1 Grade Cabbages (b) No. 2 Grade (a) No. 1 Grade Carrots (b) No. 2 Grade (a) No. 1 Grade Cauliflower (b) No. 2 Grade (a) No. 1 Grade Celery (b) No. 1 Heart Grade (c) No. 2 Grade No. 1 Grade Sweet Corn (c) Canada No. 2 (a) Canada No. 1 (b) Canada No. 1 Heart (c) Canada No. 2 Canada No. 1 Field Cucumbers (a) No. 1 Grade (b) No. 2 Grade (a) Canada No. 1 (b) Canada No. 2 Beets GRADE NAMES FRESH VEGETABLES Column I Item Kind 10. Greenhouse Cucumbers 11. 12. 13. 14. 15. 16. 17. Source: Fresh Fruit and Vegetable Regulations Column II Import Grade Name (a) No. 1 Grade (b) No. 2 Grade Head Lettuce (Iceberg Type) (a) No. 1 Grade (b) No. 2 Grade (a) No. 1 Grade Onions (b) No. 1 Pickling Grade (c) No. 2 Grade (a) No. 1 Grade Parsnips (b) No. 2 Grade (a) No. 1 Grade Potatoes (b) No. 1 Large Grade (c) No. 1 Chef Grade No. 1 Grade Rutabagas (a) No. 1 Grade Field Tomatoes (b) No. 2 Grade (c) No. 1 Picklers Grade (d) No. 2 Picklers Grade (a) No. 1 Grade Greenhouse Tomatoes (b) No. 1 Extra Large Grade (c) Commercial Grade (d) No. 2 Grade Column III Canada Grade Standard (a) Canada No. 1 (b) Canada No. 2 (a) Canada No. 1 (b) Canada No. 1 Pickling (c) Canada No. 2 (a) Canada No. 1 (b) Canada No. 1 Large (c) Canada No. 1 Chef Canada No. 1 (a) Canada No. 1 (b) Canada No. 2 (c) Canada No. 1 Picklers (d) Canada No. 2 Picklers (a) Canada No. 1 (b) Canada No. 1 Extra Large (c) Canada Commercial (d) Canada No. 2

Classification and Grading systems in our industries LUMBER GRADE STAMPS Source: Canadian Wood Council WESTERN CANADIAN WHEAT CLASSES Source: Canadian Grain Commission

CANADIAN BEEF GRADING SYSTEM

The Past 1992 1972 -1990 1941 -1958 Wartime Beef Policies (1941 -1943) National Beef Grades (1947) British Columbia Beef Regulations Grading Policy Revised (1958) 1938 1927 -1929 New Beef Grading System (1972) Beef Gradin Regulations -Major Revi Beef Regulations Revised (1978, 1984, 1986, 1987 and 1990) 1 st Grading Policy Source: CBGA

The Past • Adoption of the USDA standards for Slight and levels. Regulations -Major RevisionØ 1992 Grading 1996 Beef. Small 1997 2001 2010 • Introduction of Canada PRIME grade • Reduce minimum fat cover requirements • Modify the minimum muscling requirements • Definition for “approved grading technology” • Borderline colour chit – Canada B 4 Present grading sytem

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 $1 M Source: CBGA

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

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

Downgrades symbols A Maturity (Age) D grades C Meat Colour B 4 F+ Excessive Fat thickness F- Insufficient Fat thickness M Marbling O Soft (not firm) S Staggy T Muscling (Type) Y Fat colour (Yellow) B 3 and D 2 B 2 and D 2 13

13 Quality Grades Grade Maturity (Age) Muscling Rib Eye Muscle CANADA PRIME Youthful Good to excellent with some deficiencies Deficient to excellent Deficient to good Firm, bright red Slightly abundant CANADA Youthful A, AAA B 1 Youthful B 2 Youthful B 3 Youthful B 4 Youthful D 1 Mature D 2 Mature D 3 Mature D 4 Mature E Youthful or mature Marbling Fat Colour and Fat Measure Texture Firm, white or 2 mm or more amber Firm, bright red A - trace AA - slight AAA - small Firm, bright red N/A Firm, white or amber 2 mm or more Firm, white or amber Less than 2 mm Bright red N/A Yellow N/A Bright red N/A White or amber N/A Deficient to excellent Excellent Dark red N/A N/A Medium to excellent Deficient N/A Firm, white or Less than 15 mm amber White to yellow Less than 15 mm N/A N/A Less than 15 mm N/A 15 mm or more Deficient to N/A excellent Pronounced masculinity N/A

Lean meat yield estimation v Prime AAA AA A Research work at the Lacombe Research Centre on the actual dissection of 540 carcasses Lean % = 63. 65 + 1. 05 (muscle score) - 0. 76 (grade fat) Length Width Fat class Yield grade Canada 1 ≥ 59 Canada 2 58 to 54 Canada 3 ≤ 53 15

“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

Electromagnetic radiation Spectrum Vision Analysis Systems

Vision Analysis Systems Whole Carcass Rib eye Research Management Systems / Computer Vision System E+V® Technology Gmb. H VBS 2000 VGB 2000

Electromagnetic radiation Spectrum Dual Energy X-ray Absorptiometry DEXA

Dual Energy X-ray Absorptiometry DEXA Carcass is scanned X-ray image is generated Scans are nondestructive using low energy X-rays Grey scale information Carcass cut out Prediction equations Fat Lean Bone

Agriculture and Agriculture et -Food Canada 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: • Ist stage (building prediction equations) n = 160 • 2 nd stage (to validate the equations) n = 80 • 3 rd stage (to validate commercial) n = 100 Animals serially slaughtered weight 900 – 1, 600 lb back fat depths 1 – 21 mm Calf-fed and Yearling Implanted and non implanted

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

Agriculture and Agriculture et -Food Canada Agroalimentaire Canada USING DEXA TECHNOLOGY FOR A RAPID, NON-INVASIVE CARCASS FAT AND LEAN PREDICTION IN BEEF Fat Lean Bone Chuck 0. 858 0. 754 0. 485 Rib 0. 923 0. 818 0. 636 Brisket 0. 815 0. 682 0. 454 Flank 0. 874 0. 867 0. 308 Foreshank 0. 192 0. 076 0. 073 Loin 0. 865 0. 817 0. 577 Round 0. 788 0. 602 0. 288 Plate 0. 860 0. 799 0. 270 Overall 0. 958 0. 862 0. 520 a. R 2: coefficient of determination 30 Dexa Total Fat Beef primal Traditional Cutout Total Fat vs Dexa Total Fat R 2 = 0, 9577 20 10 10 15 20 25 30 Traditional Cutout Total Fat 35 Traditional Cutout Total Lean vs Dexa Total Lean 90 Dexa Total Lean Relationship (R 2)a between DEXA values and the traditional carcass cut-out for lean, fat and bone of the different primal cuts (n=158). R 2 = 0, 862 80 70 60 50 60 70 Traditional Cutout Total Lean 80

Agriculture and Agriculture et -Food Canada Agroalimentaire Canada USING DEXA TECHNOLOGY FOR A RAPID, NONINVASIVE 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.

Electromagnetic radiation Spectrum Near Infrared Spectroscopy (NIRS)

1. 8 1. 6 1. 4 1. 2 1. 0 0. 8 0. 6 0. 4 0. 2 0. 0 O-H N-H Monochromatic light C-H Useful analytical region Absorbance = log (1/Reflectance) 1100 1172 1244 1316 1388 1460 1532 1604 1676 1748 1820 1892 1964 2036 2108 2180 2252 2324 2396 2468 Absorbance Near Infrared Spectroscopy (NIRS) Wavelength (nm) Photodetector Sample Photodetector Quartz Cell

Using NIRS technology to segregate B 4’s n = 120 B 4’s = 60 AAA, A = 60 95% of the samples were correctly classified

NIRS spectrum for beef NIRS technology 1. 8 O-H 1. 6 N-H O-H N-H 1. 2 C-H 1. 0 Organic compounds 2468 2396 2324 2252 2180 2108 Meat quality parameters Wavelength (nm) 2036 1676 1604 1532 1460 1388 1316 1244 1172 0. 0 1964 0. 2 1892 0. 4 Useful analytical region C-H Useful analytical region 1820 0. 6 1748 0. 8 1100 Absorbance 1. 4 Animal tissues

1. 8 1. 6 1. 4 1. 2 1. 0 0. 8 0. 6 0. 4 0. 2 0. 0 O-H N-H C-H Useful analytical region 1100 1172 1244 1316 1388 1460 1532 1604 1676 1748 1820 1892 1964 2036 2108 2180 2252 2324 2396 2468 Absorbance The FUTURE Wavelength (nm) Toward an International System?

“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

The Future 2014 -Future 1992 -2014 1990 1947 1927 -1929 Yield inclusion Age Yield Marbling Colour Bundle of technologie assisting in grading assessmen National Beef Grades 1 st Grading Policy Source: CBGA

v Prime AAA AA A v B 1 B 2 B 3 B 4 v D 1 D 2 D 3 Certificated brands v E

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

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