Food & beverage
Relearning product characteristics when inspecting food applications is a common challenge for manufacturers. Density, water concentration, storage temperatures, heat, thawing and even seasonal and soil variations, are among the many factors that could affect the performance of food inspection machines. Here, Phil Brown, European managing mirector of Fortress Technology and Sparc Systems explains the science and shares examples of smart technologies that adapt to changing food characteristics.
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igital technologies have quickly transformed how we all run processes. machine learning especially is pushing
the innovation boundaries in food manufacturing and streamlining production processes. it is a science which requires a deep understanding of the chemical makeup and molecular structures of foods. how fats, proteins, carbohydrates and sugars change during processing and storage will initiate a chemical reaction. To the naked eye, and even taste buds, these changes are barely noticeable. yet, for wet and conductive foods, such as bread, meats, dairy products and ready meals, to a sensitive metal detector inspecting the same foods over an eight hour shift, they can appear like completely different products. Thermal changes and water content are the
main factors that can interfere with metal detector signals. dealing with higher amplitudes of product signal can be especially pronounced in meat processing plants inspecting varying weights and sizes of joints, some boneless and others boned. phil brown explains: “With boneless joints
there’s a greater concentration of meat, which means they are denser and consequently weigh more. This water content, which could be intensified by ‘plumping’ – a common practice to maintain tenderised meat products by injecting salt water or stock - disrupts and mimics the metal detector signal causing traditional systems to react as if there’s a metal contaminant present and reject the product.” Known as ‘product effect’, these false rejects
more often than not result in perfectly good food being discarded.
THE PowER oF ARM To identify a metal contaminant within conductive products, a metal detector must remove or reduce this ‘product effect’. The solution is to change the frequency of operation to minimise the effect of the product. but there is a trade-off. doing this can impact the ability to find different
The chemisTry oF conTaminanT deTecTion
16 april 2021 | FacTory&handlingsolUTions
metals. dropping the frequency tends to enhance ferrous metal detection. yet this limits performance when it comes to non-ferrous metals, since the lower end of the frequency is more responsive to magnetic effects of the contamination. by the same token, the reverse happens when the frequency is taken higher – it starts to limit the ferrous detection capability but enhances the non-ferrous detection. simultaneous frequency is the most reliable
way to remove product effect without compromising the sensitivity of a metal detector. Fortress uses arm microprocessors to adapt to these changing product characteristics. This processing technology powers the Fortress interceptor, enabling it to run real-time analysis of the low-frequency and a high-frequency signals in parallel. by the same virtue, as products travel
through processing facilities, environmental conditions change. Factory temperatures rise and dip. For example, bread dough can warm causing moisture to gradually evaporate. alternatively, frozen food can thaw. a change of just 5°c is enough to affect the product characteristics and disrupt product signals. both Fortress and sparc have a couple of
technologies in their armoury to counteract changing food characteristics as they pass through the processing chain.
AuToMATic AlignMEnT autophase is a useful tool available on Fortress phantom, stealth and interceptor metal detectors that tracks long term changes within wet products, adapting and syncing to new
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