Beef Grading in Canada:

Past, Present and Future

Agriculture and Agri-Food Canada

Agriculture et Agroalimentaire Canada

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

May 24th, 2014CMSA/CMC Symposium

Nuria PrietoLacombe Research Centre Nuria.Prieto@AGR.GC.CA

Classification or Grading?

2

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

Item

Column I Column II Column III

Kind Import Grade Name Canada Grade Standard1. Apples (a) Extra Fancy Grade (a) Canada Extra Fancy

(b) Fancy Grade (b) Canada Fancy(c) Commercial Grade (c) Canada Commercial

2. Apricots (a) No. 1 Grade (a) Canada No. 1(b) Domestic Grade (b) Canada Domestic

(c) Hailed Grade (c) Canada Hailed3. Blueberries No. 1 Grade Canada No. 14. Cantaloups No. 1 Grade Canada No. 15. Cherries (a) No. 1 Grade (a) Canada No. 1

(b) Domestic Grade (b) Canada Domestic

(c) Orchard Run Grade (c) Canada Orchard Run

6. Crabapples (a) No. 1 Grade (a) Canada No. 1(b) Domestic Grade (b) Canada Domestic

7. Cranberries (a) No. 1 Grade (a) Canada No. 1(b) Domestic Grade (b) Canada Domestic

8. Grapes (a) No. 1 Grade (a) Canada No. 1(b) Domestic Grade (b) Canada Domestic

9. Peaches (a) No. 1 Grade (a) Canada No. 1(b) Domestic Grade (b) Canada Domestic

10. Pears (a) Extra Fancy Grade (a) Canada Extra Fancy

(b) Fancy Grade (b) Canada Fancy(c) Commercial Grade (c) Canada Commercial

11. Plums and Prunes (a) No. 1 Grade (a) Canada No. 1(b) Domestic Grade (b) Canada Domestic

12. Field Rhubarb (a) No. 1 Grade (a) Canada No. 1(b) Domestic Grade (b) Canada Domestic

13. Strawberries No. 1 Grade Canada No. 1

GRADE NAMES

FOR

FRESH FRUIT

Source: Fresh Fruit and Vegetable Regulations

Classification and Grading systems in our industries

Item

Column I Column II Column III

Kind Import Grade Name Canada Grade Standard1. Asparagus (a) No. 1 Grade (a) Canada No. 1

(b) No. 1 Slender Grade (b) Canada No. 1 Slender

(c) No. 2 Grade (c) Canada No. 22. Beets (a) No. 1 Grade (a) Canada No. 1

(b) No. 2 Grade (b) Canada No. 23. Brussels Sprouts (a) No. 1 Grade (a) Canada No. 1

(b) No. 2 Grade (b) Canada No. 24. Cabbages (a) No. 1 Grade (a) Canada No. 1

(b) No. 2 Grade (b) Canada No. 25. Carrots (a) No. 1 Grade (a) Canada No. 1

(b) No. 2 Grade (b) Canada No. 26. Cauliflower (a) No. 1 Grade (a) Canada No. 1

(b) No. 2 Grade (b) Canada No. 27. Celery (a) No. 1 Grade (a) Canada No. 1

(b) No. 1 Heart Grade (b) Canada No. 1 Heart(c) No. 2 Grade (c) Canada No. 2

8. Sweet Corn No. 1 Grade Canada No. 1

9. Field Cucumbers (a) No. 1 Grade (a) Canada No. 1(b) No. 2 Grade (b) Canada No. 2

GRADE NAMES FRESH VEGETABLES

ItemColumn I Column II Column IIIKind Import Grade Name Canada Grade Standard

10. Greenhouse Cucumbers (a) No. 1 Grade (a) Canada No. 1(b) No. 2 Grade (b) Canada No. 2

11. Head Lettuce (Iceberg Type) (a) No. 1 Grade (a) Canada No. 1(b) No. 2 Grade (b) Canada No. 2

12. Onions (a) No. 1 Grade (a) Canada No. 1(b) No. 1 Pickling Grade (b) Canada No. 1 Pickling(c) No. 2 Grade (c) Canada No. 2

13. Parsnips (a) No. 1 Grade (a) Canada No. 1(b) No. 2 Grade (b) Canada No. 2

14. Potatoes (a) No. 1 Grade (a) Canada No. 1(b) No. 1 Large Grade (b) Canada No. 1 Large(c) No. 1 Chef Grade (c) Canada No. 1 Chef

15. Rutabagas No. 1 Grade Canada No. 1

16. Field Tomatoes (a) No. 1 Grade (a) Canada No. 1(b) No. 2 Grade (b) Canada No. 2(c) No. 1 Picklers Grade (c) Canada No. 1 Picklers(d) No. 2 Picklers Grade (d) Canada No. 2 Picklers

17. Greenhouse Tomatoes (a) No. 1 Grade (a) Canada No. 1(b) No. 1 Extra Large Grade (b) Canada No. 1 Extra Large

(c) Commercial Grade (c) Canada Commercial(d) No. 2 Grade (d) Canada No. 2

Source: Fresh Fruit and Vegetable Regulations

Classification and Grading systems in our industries

WESTERN CANADIAN WHEAT CLASSES

Source: Canadian Grain Commission

LUMBER GRADE STAMPS

Source: Canadian Wood Council

Classification and Grading systems in our industries

CANADIAN BEEF GRADING SYSTEM

1st Grading Policy

British Columbia Grading Policy

Wartime Beef Policies (1941-1943) National Beef Grades (1947)Beef Regulations Revised (1958)

New Beef Grading System (1972)Beef Regulations Revised (1978, 1984, 1986, 1987 and 1990)

Beef Grading Regulations-Major Revision-

The Past

1927-1929

1938

1941-1958

1972-19901992

Source: CBGA

1996

• Adoption of the USDA standards for Slight and Small levels

1997

• Introduction of Canada PRIME grade

2001

• Reduce minimum fat cover requirements• Modify the minimum muscling requirements• Definition for “approved grading technology”

2010

• Borderline colour chit – Canada B4

Present grading sytem

1992 Beef Grading Regulations -Major Revision-The Past

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

Key factors

11

• Age (Maturity)

Influence on quality• Sex

Tenderness

Tenderness

Acceptability / yield

Yield• Conformation (Muscling)

• Fat (colour, texture and cover)

• Meat (colour, texture and cover) Acceptability / quality

The Present Canadian Beef Grading Agency (CBGA)

The grading system is based on a series of pass/fail testsTo get the top grade you have to pass all the testsIf you fail at any steps the carcass is downgradedKey factors

12

• Age (maturity)

Influence on quality• Sex

Tenderness

Tenderness

Acceptability / yield

Yield• Conformation (muscling)

• Fat (colour, texture and cover)

• Meat (colour, texture and cover) Acceptability / quality

The Present Canadian Beef Grading Agency (CBGA)

Carcass

Steer / HeiferToo masculine

YouthfulMature

Well muscled fatDeficient

Yellow or lacking Quality

Bright redDark or soft

Marbling

Carcass Quality Segregation

13

Downgrades symbols

F+ Excessive Fat thickness

F- Insufficient Fat thickness

A Maturity (Age)

C Meat Colour

M Marbling

S Staggy

O Soft (not firm)

Y Fat colour (Yellow)

T Muscling (Type)

D grades

B4

B2 and D2

B3 and D2

Grade Maturity (Age) Muscling Rib Eye Muscle Marbling Fat Colour and Texture

Fat Measure

CANADA PRIME

Youthful Good to excellent with some deficiencies

Firm, bright red Slightly abundant Firm, white or amber

2 mm or more

CANADA A, AA, AAA

Youthful Good to excellent with some deficiencies

Firm, bright red A - traceAA - slightAAA - small

Firm, white or amber

2 mm or more

B1 Youthful Good to excellent with some deficiencies

Firm, bright red N/A Firm, white or amber

Less than 2 mm

B2 Youthful Deficient to excellent

Bright red N/A Yellow N/A

B3 Youthful Deficient to good Bright red N/A White or amber N/A

B4 Youthful Deficient to excellent

Dark red N/A N/A N/A

D1 Mature Excellent N/A N/A Firm, white or amber

Less than 15 mm

D2 Mature Medium to excellent

N/A N/A White to yellow Less than 15 mm

D3 Mature Deficient N/A N/A N/A Less than 15 mm

D4 Mature Deficient to excellent

N/A N/A N/A 15 mm or more

E Youthful or mature

Pronounced masculinity

 

13 Quality Grades

15

Length

Width

Fat class

Prime AAA AA A Lean meat yield

estimation

Lean % = 63.65 + 1.05 (muscle score) - 0.76 (grade fat)

Yieldgrade

Canada 1 ≥59Canada 2 58 to 54Canada 3 ≤53

Research work at the Lacombe Research Centre on the actual dissection of 540 carcasses

“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

The FUTURE

“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

Electromagnetic radiation Spectrum

Vision Analysis Systems

VBS 2000

Research Management Systems / Computer Vision System

VGB 2000

Whole Carcass

E+V® Technology GmbH

Rib eye

Vision Analysis Systems

Dual Energy X-ray AbsorptiometryDEXA

Electromagnetic radiation Spectrum

Dual Energy X-ray Absorptiometry

Carcass is scannedX-ray image is generated

Prediction equations

Carcass cut outGrey scale informationScans are non-

destructive using low energy X-rays

Fat LeanBone

DEXA

“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:

Animals serially slaughtered

weight 900 – 1,600 lb

back fat depths 1 – 21 mm

n = 160• Ist stage (building prediction equations)

n = 80

Calf-fed and Yearling

Implanted and non implanted

• 2nd stage (to validate the equations)

• 3rd stage (to validate commercial) n = 100

Agriculture and Agri-Food Canada

Agriculture et Agroalimentaire Canada

HIND

SUB-PRIMALS

DISSECTED LEANCANADA

BONELESSCLOSELY TRIMMED

RETAIL CUTS, USA

(Saleable)

(Efficiency)

Lean Yield Defined

Agriculture and Agri-Food Canada

Agriculture et Agroalimentaire Canada

DEXA TECHNOLOGY FOR A RAPID, NON-INVASIVE CARCASS FAT AND LEAN PREDICTION IN BEEF

USING DEXA TECHNOLOGY FOR A RAPID, NON-INVASIVE CARCASS FAT AND LEAN PREDICTION IN BEEF

Agriculture and Agri-Food Canada

Agriculture et Agroalimentaire Canada

Beef primal Fat Lean BoneChuck 0.858 0.754 0.485Rib 0.923 0.818 0.636Brisket 0.815 0.682 0.454Flank 0.874 0.867 0.308Foreshank 0.192 0.076 0.073Loin 0.865 0.817 0.577Round 0.788 0.602 0.288Plate 0.860 0.799 0.270Overall 0.958 0.862 0.520

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

10 15 20 25 30 3510

20

30

f(x) = 0.81280396535996 x + 0.324801410649343R² = 0.957703653627354

Traditional Cutout Total Fat vs Dexa Total Fat

Traditional Cutout Total Fat

Dexa

Tot

al F

at

50 60 70 8060

70

80

90

f(x) = 0.9028025235428 x + 21.8393575944869R² = 0.862013955511628

Traditional Cutout Total Lean vs Dexa Total Lean

Traditional Cutout Total Lean

Dexa

Tot

al Le

an

USING DEXA TECHNOLOGY FOR A RAPID, NON-INVASIVE CARCASS FAT AND LEAN PREDICTION IN

BEEF

Agriculture and Agri-Food Canada

Agriculture et Agroalimentaire Canada

• 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.

CONCLUSIONS

Near Infrared Spectroscopy (NIRS)

Electromagnetic radiation Spectrum

Sample Quartz

Cell

Photodetector Photodetector

Monochromatic light

Absorbance = log (1/Reflectance)

Near Infrared Spectroscopy (NIRS)

2252

0.00.20.40.60.81.01.21.41.61.8

1100

1172

1244

1316

1388

1460

1532

1604

1676

1748

1820

1892

1964

2036

2108

2180

2324

2396

Wavelength (nm)

Abs

orba

nce

Useful analytical region

Useful analytical region

C-H

C-H

O-HO-H N-H N-H

2468

n = 120B4’s = 60

AAA, AA, A = 60

PC-1 (95%)

-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

PC

-2 (

4%)

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Scores

B4

B4

B4

AA

A

A

A

A

A

AA

B4

B4

B4

B4

B4

B4 B4B4

A

A

A

B4

B4

B4

B4

B4

B4

B4

B4B4

B4

B4

B4

B4

B4B4

B4

B4

B4

B4

B4

B4

B4

B4B4

B4

A

A

A

A

A

AA

A

A

A

A

A

A

A

A

A

A

A

A

A

A

A

AA

B4B4

B4

B4

B4B4

B4

B4

B4

B4

B4

B4

A

A

A

AAAA

A

A

A

A

A

A

A

A

A

A

A

A

AA

A

A

A

B4

B4B4

B4

B4

B4

B4

B4

B4

B4

B4

B4

Using NIRS technology to segregate B4’s

95% of the samples were correctly classified

NIRS technology

2252

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.811

00

1172

1244

1316

1388

1460

1532

1604

1676

1748

1820

1892

1964

2036

2108

2180

2324

2396

Wavelength (nm)

Abs

orba

nce

Useful analytical region

Useful analytical region

C-H

C-H

O-HO-H N-H N-H

2468Meat quality parameters

Organic compounds

Animal tissues

NIRS spectrum for beef

Toward an International System?

The FUTURE

2252

0.00.20.40.60.81.01.21.41.61.8

1100

1172

1244

1316

1388

1460

1532

1604

1676

1748

1820

1892

1964

2036

2108

2180

2324

2396

Wavelength (nm)

Abs

orba

nce

Useful analytical region

Useful analytical region

C-H

C-H

O-HO-H N-H N-H

2468

“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

1st Grading Policy

National Beef Grades

Yield inclusion

Age

Yield

Marbling

Colour

Bundle of technologies

assisting in the grading assessments

The Future

1927-1929

1947

1990

1992-20142014-Future

Source: CBGA

Certificated brands

Prime AAA AA A B1 B2 B3 B4 D1 D2 D3 E

The primary purpose of beef grading is to facilitate trade by describing the commercially

important attributes of the carcass.

34

Óscar López-Campos

Lacombe Research CentreTelephone 403-782-8195Facsimile 403-782-6120

Oscar.LopezCampos@AGR.GC.CAolopezcampos@gmail.com

Thank you for your attention