Advances in technology to predict meat eating quality
The use of new scientific techniques may help determine predictions for the eating quality of meat, a meat quality bioscientist has said.
Dr Bruce Moss, project leader meat quality at the Agri-Food and Bioscience Institute (AFBI) at Queen’s University, Belfast, compared two scientific methods used to predict the compositional aspects of meat at the 14th Langford Food Industry Conference, outlining the potential of near-infrared reflectance spectroscopy (NIR) and Raman spectroscopy in predicting beef eating quality, as well as their practical application.
Spectroscopy is the study of how light is absorbed and emitted, which is used in meat science to detect, identify and analyse the different molecules in meat tissue and provide information on the proteins, fatty acid and marbling that may help determine the eating quality.
“Most processors are using process and form measurement to provide quality assessment systems,” he said, “but… we can determine both the eating quality and the nutritional quality using the same spectroscopy,” adding that NIR also had the potential to predict appearance as well.
He said that NIR had been extensively researched, but its strong absorption of water was a disadvantage not shared by Raman spectroscopy – making Raman a better indicator of marbling. He pointed out that it was possible to measure fatty acids and make meaningful predictions, as these do not change as the meat is aged, unlike marbling or pH. Raman spectroscopy also gives a greater spectrum of results, he pointed out, allowing for a better interpretation of how the meat is changing over time, although the area tested is tiny (less then 1mm spots) which means that you need to test a lot of different spots.
He said that pre- and post-slaughter handling and processing, as well as meat science aspects, needed to be considered when developing prediction systems, because there was a lot of interaction between hanging methods, breed, pH and meat quality. There was a small effect of carcase suspension, and the time of blooming also had an effect on visible and infrared. “We might have think about these more when developing predictions,” he said.
Both NIR and Raman spectroscopy prediction show considerable variability, he said, and further work is required to understand the differences in prediction ability.
“The NIR has worked for a long time, but still, as far as I’m aware, I don’t think we’ve got a consistent in-plant operation. We need more work on NIR – we need to understand what’s going on and why we are getting these differences in prediction ability.”
He concluded that although there were some problems associated with Raman spectroscopy in predicting meat quality, if these could be overcome, it had potential.
“Raman spectroscopy is very good at predicting fatty acids, so if we’re looking at nutritional quality, that might be a better way to go,” he said.