Venison raised at AgResearch Invermay is feeding into a wide collaborative red meat industry programme. The promise is for exciting and innovative New Zealand-developed red meat sensor technology adding value to New Zealand’s red meats.
Deer Industry NZ (DINZ) is particularly interested to find out more about the sensory aspects of venison quality, explains Dr Cameron Craigie, AgResearch science impact leader for meat products and supply.
Craigie is project leader of a team of eight meat scientists working on the development of new, and what will be world-leading, technology that has been funded by the Ministry of Business, Innovation and Employment (MBIE) in a five-year $4.5 million Endeavour Fund grant approved in 2016. This is aiming to extract more value from New Zealand’s red meat markets over the next decade.
Now halfway through, the research has moved from phase one – examining the available sensor technology for capability to measure meat pH, tenderness and intramuscular fat – and into phase two, taking the most promising technology out of the laboratory and into more real-life plant situations.
The research team’s advantage was already knowing everything about the Invermay animals, which had been bred and raised on the research farm, explains Craigie.
“It was a good example of the industry collaborating to get more out of what we’re all doing,” says Craigie, who drew on his own background assessing venison quality traits using near infra-red (NIR) spectroscopy technology in a 2014 Society of Animal Production paper and in his PhD at Massey University.
The loins of 60 of the deer were tenderness tested to the 21-day Cervena® standard, for pH and muscle fibre (sarcomere) lengths – to assess whether there had been “cold-shortening” or toughening of the fibres – and also for micro-fibrillar fragmentation which shows how much the meat had tenderised after the 21 days. For DINZ purposes, they also looked at the amount of weight lost after storage, the meat colour in packs and also on the shelf at retail.
The venison was subjected to five different sensor technologies at AgResearch Invermay, none of which are new in themselves. These included two Raman spectroscopy devices, linescan hyperspectral, snapshot hyperspectral and contact NIR spectroscopy. Each piece of data is now being analysed separately and together at the University of Otago and AgResearch.
“While 60 animals won’t give us enough information now, in time we could look at calibrating the instruments to predict sensory scores specifically for venison. For example, predicting, at 24 hours post-slaughter what the consumer response is likely to be to the meat when they buy it,” says Craigie.
In the wider context of the work, the team was also looking, for example, at whether the Raman, NIR and hyperspectral devices predict tenderness, pH and Intramuscular fat better alone or in combination with any of the other tests or perhaps by adding in data already routinely collected, such as carcase weight, explains Craigie. This is the exciting and new “sensor fusion” part of the MBIE programme, upon which the team is just embarking.
While each of the collaborators is really skilled at analysing their part of the puzzle, the team has found the bigger picture of sensor fusion and data integration from multiple sources, is a specialist field with huge potential that has received little focus in meat quality research until now, he says.
“A systems approach to data analysis and interpretation is essential for robust, relevant research outcomes that can be implemented to enable production of the world’s best pasture-raised beef, lamb and venison products,” he explains.
Another requirement was making sure that meats at all pH levels are represented accurately in the data distribution.
Commenting on the practicalities of applying the research, Craigie noted: “We were finding we needed to find meat samples at the high, medium and low pH end-points in-plant post-mortem, but pre-rigour, to do this,” he said, adding it’s “not a trivial undertaking cost-wise” either to find the samples and to do all the tests that were required.
“In addition, when taking the most promising technology out of the lab into the plant environment, it changes a lot when you have a time budget of 10 seconds to get a reading.”
Callaghan Innovation is also part of the research team that has developed a robot to connect the relevant sensors. The researchers found the best way to get accurate and fast readings on the processing chain was to hard-mount the relevant sensors in a frame. This was recently tested on samples of chilled beef striploin and lamb, which had been taken out of the chiller, cut and rested for 30 minutes and inserted into the robot. The samples are 3D scanned for contour and shape, corrected for the undulations in the surface of the meat, and then programmed to be moved around the frame to the different sensor points to be accurately assessed.
“The robot is doing the job of five people from a research point of view. It gives us the chance to analyse the same piece of meat and in real-time with minimal human error, all in less than 90 seconds,” explains Craigie.
DINZ venison marketing manager Nick Taylor has been keeping abreast of the new technology, attending an industry day in Hamilton on 21 November where the team were running the robot sensor on beef and spent a day later checking in on the sensory testing of the venison.
“It’s interesting to see the evolution of technology and how quickly we can now get data to help inform decision making,” he says. “It is important for our industry to be aware of the technological developments and for us to start having conversations now, considering how we can use this technology in our business and the possible impact it may have.”
DINZ science and policy manager Catharine Sayer also notes the limitations of current quality assessment tools, such as the genetic prediction of the eye muscle area of a live animal which is positively correlated with tenderness (but not yet adopted by industry), or invasive techniques on the carcase involving taking physical samples from the venison, which is slow and costly in terms of assessment time and loss of product.
“While reliance on our Cervena standards for inputs and DINZ slaughter standards guarantee a consistent premium product, sensory techniques could allow even more precise differentiation between the optimium marketing channel for each cut,” she says.
Alongside DINZ, Craigie has appreciated the involvement of industry collaborators in the work. These include: Scott Technologies, First Light Foods, Duncan NZ, FarmIQ systems, Silver Fern Farms, Alliance, Beef and Lamb NZ Genetics and other beef and sheepmeat processors including Greenlea Premier Meats, ANZCO Foods, Progressive Meats, Provenance Meats, and the Meat Industry Association.
“It’s what industry engagement is all about. We were able to work together and produce work to meet each other’s needs and for a better outcome for the whole industry,” says Craigie.
“In terms of venison, it’s a very high value product and if you can get it even higher by guaranteeing the quality then I’m sure that will pay dividends for the New Zealand meat industry.”
Results from the sensory assessments will be available little later this year. An update on the MBIE work will be reported first to red meat processing representatives attending the AgResearch Meat Technology Workshop in Hamilton (13-14 March), where attendees will see an MBIE trial with a prototype of the new robot.
Watch the video to learn more about the programme and see the prototype sensor robot in action:
For further information contact Dr Cameron Craigie Cameron.email@example.com, 021-289 9063.
This is a longer version of an article which appeared in the latest edition of Deer Industry News magazine (February/March 2019) is reproduced here with permission. Check out the magazine for more in-depth deer industry specific news, including on-farm fieldays, trials and more.