BARCODING, LABELLING & PACKAGING u Jenton Dimaco Food label verification:


A future shaped by data Dr Russell Sion, director of JentonDimaco, says in any business world, data is reshaping the landscape


F


ood processing is no exception. If we consider the next big technology development, it will inevitably be a non-


incremental, non-organic, move away from human labour and towards automation. In food production humans generally perform two sub- tasks for each primary task. In virtually every case, one of these sub-tasks is checking/quality control. After biological contamination, label related


defects are cited as the second most common cause behind costly and brand-damaging product withdrawals and retailer returns. There are several powerful reasons to ensure that the labelling of food products is correct. From the perspective of a food processor, the reasons broadly fit into three categories – improved production efficiency, waste reduction and regulatory compliance.


BOOSTING PRODUCTION EFFICACY Automated label verification has a huge part to play in increasing production line efficiency in food factories. Many applications which were previously deemed uneconomic to automate are now being automated based solely on the unavailability of a skilled workforce. Using technology to verify the label offers far


greater benefits compared to manual inspection. Given today’s production speeds, it’s impossible for one human to accurately read and process all the data for an entire shift. Let alone check all elements of the label integrity, use by dates, country of origin, special promotions and cooking


A well-designed label verification system should be able to verify 100% of the data on 100% of the products against validated source production data.jpg


time. Operative fatigue swiftly kicks in. A few minutes is more realistic. Having decided to automate the two main questions that follow are “How to go about it?” and soon after “what’s the risk?”. Most automation tasks should be achievable and contacting organisations such as BARA can help guide projects.


JentonDimaco’s advanced optical character recognition (OCR) software reliably scans up to 200 pack labels p/m


14 April 2023 Irish Manufacturing


Among some of the many automation risks, one of the most significant is removing the final visual check due to labour shortages. For instance, a food operative putting meat steaks into a retail tray will inevitably check a multitude of critical factors, such as general appearance, colour, fat content etc. as they pick up and place the steaks into the tray. However, inspecting established quality parameters in each piece of meat on processing lines can be further enhanced with the use of automated vision solutions. Machine vision companies can almost replicate (and in many cases outdo) a human operative. It is non- destructive, easy and quick. It is entirely feasible that inspection processes on meat and poultry processing and packing lines will be dominated by cameras in the next decade.


In the case of checking the label is correct, the case for automation is much stronger. The label not only identifies the product but contains critical variable information such as “use by” dates and pricing. A well-designed label verification system should be able to verify 100% of the data on 100% of the products against validated source production data. Typically via the factory MRP system, not from a production line operative. As well as guaranteeing its accuracy, having this complete audit trail provides proof that all data or labels are verified.


ADDRESSING FOOD WASTE A study by climate action NGO WRAP titled “Quantification of food surplus, waste and related materials in the UK grocery supply chain” calculated that approximately 1.7 million tonnes of food were wasted in the UK prior to it reaching the consumer, almost half of which was preventable. A further study by Rentokil determined that mislabelling was the second most common cause of food recalls in the US, and the third in the UK. A well-designed label verification system would be able to detect not only that an incorrect label was being used but also any incorrect data


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