Effectiveness of the SmartMV prototype BeefCam System to sort beef carcasses into expected palatability groups

Area of Science:

• Food Science
• Agricultural Engineering
• Meat Science

Background:

• Beef carcass palatability is a key driver of consumer satisfaction.
• Objective methods for predicting beef eating quality are needed to improve grading accuracy.

Purpose of the Study:

• To evaluate the effectiveness of the prototype BeefCam Video Imaging System for classifying beef carcasses into palatable groups.
• To develop and validate regression models using BeefCam data to predict carcass palatability.

Main Methods:

• Collected images of the longissimus muscle from 769 beef carcasses across three USDA quality grades.
• Developed two regression models (BeefCam data only, and BeefCam data with quality grade) to predict palatability.
• Validated models using independent carcass data (n=292) and Warner-Bratzler shear force (WBSF) measurements.

Main Results:

• Both models successfully certified carcasses for palatability, with Model I and Model II certifying 47.3% and 42.1% of all carcasses, respectively.
• The frequency of tough steaks (WBSF ≥ 4.5 kg) within the certified groups was low: 1.4% for Model I and 1.6% for Model II.
• Validation showed a significant decrease in tough steaks within the certified groups compared to the original population.

Conclusions:

• The prototype BeefCam system demonstrates potential for objective beef carcass palatability classification.
• Further development of a commercial BeefCam system is warranted based on its ability to reduce the incidence of tough steaks.
• This technology could enhance beef quality grading and consumer confidence.