PSXII-16 Evaluation of a Grade Ruler Approach for Yield Grading of Veal Carcasses

• Published in: Journal of animal science 2023-11, Vol.101 (Supplement_3), p.601-602
• Main Authors: Lopez-Campos, Oscar, Prieto, Nuria, Marcoux, Marcel, Thibault, Catherine, Zawadski, Sophie, Scott, Haley
• Format: Article
• Language: English
• Subjects: Body weight; Carcasses; Meat; Meat industry; Meat processing industry; Muscles; Quality; Rib; Ribs; Thickness; Veal; Yield
• ISSN: 0021-8812; 1525-3163
• DOI: 10.1093/jas/skad281.701

Abstract Introduction: yield estimations in carcass grading systems are important for the meat industry to assign value and sort carcasses for different market specifications. Previous studies in different species have reported yield estimations performed using anatomical linear measurements that segregate carcasses into grades or classes. However, veal carcasses are not currently segregated in terms of carcass yield performance and the Canadian veal industry is now discussing the option of implementing yield assessments for veal carcass grading purposes. The aim of the present study was to evaluate the feasibility of a grade ruler approach to predict yield performance of veal carcasses for grading purposes. A total of ~300 (males and females) veal carcass sides representative of the current Canadian veal population (body weight: 127.5 to 189.2 kg; backfat: 0.5 to 20.7 mm) were use in the present study. Carcass sides were scanned using a Lunar iDXA unit to evaluate the content of total fat, lean and bone. Results and discussion: total carcass lean was mainly correlated with the hot carcass weight (r = 0.65), ¼ backfat thickness (r = -0.33) and rib eye area (REA: r = 0.58) at the 11th – 12th rib-eye location (P < 0.05). Because the time to grade veal carcasses at the grading stand is limited and the efficiency of the grading tools (e.g., ruler) has to be maximized, the ¼ backfat thickness and the width and length of the ribeye were the main factors considered to develop a prediction equation. These continues variables (backfat thickness and REA) were then categorized assigning fat classes (1-10) in 2 mm increments and 4 muscle scores (small-large). The new equation developed for the estimation of lean yield incorporating the anatomical traits of muscle score and fat class showed a R2 of 0.62 and a root mean square error of 2.14 %. Based on this equation, a matrix of estimated lean meat yield percentage using the fat class and muscle score descriptors (Table 1) was developed for the implementation in a yield ruler. The results of the present study suggest that lean yield percentage from veal carcasses might be objectively and accurately predicted applying a yield ruler approach. This tool will facilitate the veal yield grading by just determining the fat class (at ¼ backfat thickness) and muscle score at the 11th – 12th rib location.