P.PIP.0593 - Utilising the VIAscan OM grading solution to provide objective carcase measurement

Summary

Carcase grading is currently done by trained and qualified graders in the plant. Whilst they are trained and have very good guidelines to follow, each grader can see different things and the consistency across the industry is not as good as it should be. By introducing an objective measuring tool such as the VIAscan, the accuracy and consistency can be achieved across many plants. The objective measures will then be able to be used in better grading of carcase’s, more consistency for customers and better feedback to producers enabling them to produce animals for optimal results.
VIAscan measures four carcase traits of eye muscle area, both MSA and AUS-MEAT marbling, meat colour, and fat colour. With these objective measurements, AMG will have the ability to grade more accurately and both recognise and provide feedback to the producers that are consistently meeting their optimum carcase requirements.

Objectives

The overall project objective is to deliver an early adoption and evaluation of the VIAscan grading system to measure beef ribeye grading characteristics with improved accuracy and consistency compared to current manual grading systems.

The specific objectives of the project are:

• Test and trial integration of developing equipment and integration of software into feedback systems including MSA grading outputs (across multiple sites and animal types)
• Evaluate the integration of the VIAscan grading solution into AMG’s operations’ workflows and business data management systems, including feedback to producers
• Evaluate device grading capabilities across multiple classes of animals and sites
• Develop protocols on how to integrate new OM technologies including data captured into existing business systems
• Develop generic guidelines for adoption and integration of new OM technologies

Key findings

The primary results and key findings of the project were that cut surface cameras used for predicting grading traits are an important technological advance in the red meat industry, but it is vital that these devices satisfy operator expectations with regards to functionality, portability, and usability otherwise uptake of this technology is hampered despite accuracy and repeatability of trait predictions.

The key takeaway from the project was that objective measurement devices and associated solutions need to be very ergonomic even at the expense of robustness, otherwise it is very difficult to overcome apprehension and resistance to adoption.

Another very important insight that was learned through the partnership with industry was it is best to have a single device for the collection of all grading traits, even if those traits are not specifically predicted by the device as this reduces the amount of equipment that must be carried.

Benefits to industry

This project identifies insights and lessons that can be adopted by VIAscan CAS engineers and by any R&D project team creating devices for the red meat industry. The most important insight and lesson is that the technology needs to be as ergonomic as possible otherwise this alone will create substantial barriers to adoption regardless of any other feature or function of the technology. Another key insight and lesson were that the technology needs to be all-encompassing (i.e. a meat grading device that predicts a subset of meat grading traits) and should also be able to accept and store entry of all meat grading traits.

Future research

Future research and development for VIAscan CAS should be aimed primarily at the ergonomic aspects and data entry aspects of the system as these were the key areas identified by AMG during the Pre-commercial and Testing Phase 1 trials as major barriers for adoption.
To remain relevant as a reliable OM solution, it is also critical that the technology provider remains at the leading lead to ensure that the technology maintains its AUS-MEAT accreditation, and continuously seeks advancements on accreditation of new traits where applicable. Work will also continue improving the accuracy of the predictions and research and development of new technologies to support and enhance the prediction of meat grading traits.