Developing technology to tackle meat fraud

Despite the inclusion of analytical testing within meat production systems, meat fraud still happens — counterfeiting meat species or cuts is reported to be the most common type of meat fraud.

Now this issue is being tackled at Queensland Alliance for Agricultural and Food Innovation (QAAFI) by Professors Louwrens Hoffman and Daniel Cozzolino using the analytical power of machine learning models to validate the species, provenance and cut of meat.

As Chair of Meat Science at QAAFI, Professor Hoffman is concerned by limitations in the testing technology currently used within meat production systems to detect fraud or accidental substitution.

Consequently, he has been examining newer technology for its potential to overcome current limitations and says a step-change in testing capability is possible. “The cut, the species and even the provenance of meat — down to the region of origin and feedlot — can be rapidly determined using imaging technology that is easy and non-destructive to use,” he said.

Using light-based (spectroscopic) technology such as handheld NIRs to provide the ‘signature’ of the meat, the data can then be analysed by advanced machine learning algorithms that QAAFI is helping to develop.

Professor Hoffman explains that light is especially useful for analytical purposes because of a quirk of physics. Atoms (or more specifically, electrons in atoms) can absorb and emit light. As a result, every atom, molecule and compound in the universe produces a unique ‘spectrum’ of reflected light. This acts as a signature that can be used to forensically identify any compound.

The caveat with this approach is that the spectral signatures that can identity meat cuts, species and provenance have to be decoded beforehand. This is where additional R&D is needed, and Professor Hoffman turned to machine learning algorithms to solve this ‘statistical jigsaw puzzle’.

“To develop the analytical software, we matched the spectral signatures of meat products of known species, cut, provenance or other variable of interest,” he said. “That data is used to train machine learning algorithms to detect what distinguishes the different samples from a complex set of spectral clues.”

This training process for machine learning can be expanded in the future as industry needs evolve — the work could eventually include insects.

Professor Hoffman has been involved in field testing the handheld NIR technology, including in South Africa where it proved highly effective.

“We could rapidly differentiate between South African game species, the muscle type and whether the meat was fresh or frozen,” Professor Hoffman said.

Accuracies for species differentiation ranged from 89.8 to 93.2% and included ostrich, zebra and springbok game meat. Given that South Africa currently has no game meat quality standards or standardised meat cuts, this kind of technological advance opens up new opportunities to provide consumer protection.

Moving forward, Professor Hoffman points out the technology is only as good as the back-end analytical software. That is where he says industry should collaboratively focus its attention in terms of R&D investment to effectively stamp out meat fraud.

“Once the machine learning models are operational, the system is fast, cheap, reliable and accurate,” he said.

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