Rapid Beef Quality Classification Using SIFT-MS

4 months ago

For beef consumers, aroma is a key indicator of beef quality. Beef aroma can be graded by human sensory panels but this is not routine, as panels can only practically test a small number of samples due to their high inherent costs. Until now, technology has failed to produce a robust lower cost, scalable alternative to the old-fashioned sensory-based quality testing method.

Meat aroma is derived from VOCs, which give favorable or unfavorable properties. These VOCs are ideally suited to analysis using SIFT-MS, a real-time analysis technique. Analysis of the VOC profiles emitted from meat carcasses using this real-time technique instead of sensory panels and other traditional methods enables faster, lower cost grading of beef. There is also the potential for SIFT-MS aroma testing to be directly integrated into the beef production line.

In a recent study (page 8), a series of New Zealand beef samples were prepared with a range of flavor profiles by using meat from different types of cattle or with differing meat processing/storage conditions. Sensory panel specialists then scored the beef samples for a series of flavor attributes. Beef sample headspaces were also analyzed using SIFT-MS and the VOC compositions were statistically processed (using the soft independent modelling by class analogy (SIMCA) algorithm) to distinguish between prime beef and samples deemed defective by the sensory panel. The three-dimensional class projection, a visual representation of separability of the beef aroma data set, is shown in Figure 1. It was found that each of the beef classes could be distinguished by the instrument, with SIMCA inter-class distances above the required threshold of three.

SIFT-MS enables a wider scale aroma quality testing solution for beef manufacturers compared with the traditional sensory approach


Figure 1 : Class projections of SIMCA data from the headspace, from a series of New Zealand beef samples for beef quality classification.

These results show that SIFT-MS can effectively distinguish between prime and defective beef aromas. The Syft Voice200 SIFT-MS instrument provides a robust, simple solution for sensitive, quantitative screening of large numbers of samples per day, both manually and automatically (via autosampler integration). SIFT-MS enables a wider scale aroma quality testing solution for beef manufacturers compared with the traditional sensory approach.

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Lalit Rane Bsc IT
Marketing Manager