The presence of heavy metals in food or feed powders involves contamination of the food chain and potential harm to public health, as such, rapid detection is a time-critical issue. The uncertainty about food safety caused by the possible presence of heavy metals is of concern to consumers and regulatory authorities and this is typically addressed by increasing the testing frequency of food or feed samples. However, existing testing methods are often time-consuming and require highly skilled laboratory personnel to perform the testing.
This technology employs spectroscopic imaging methods and machine learning techiniques to rapidly detect heavy metals in food or feed samples. The machine learning model can perform a multi-class differentiation of the various heavy metals based on spectroscopic measurements. It is also able to predict the concentration of heavy metals present in food or feed powders using spectroscopic measurements. Minimal sample preparation is required for this method, allowing for the rapid screening of food or feed powder samples.
The technology owner is interested in collaboration with companies working with food powders, with an interest in heavy metal content within food powders.
The features and specifications of this rapid screening technology include:
This technology is further customisable to include other classes of heavy metals e.g. mercury, and to include other food types e.g. seafood, meats etc.
Detection and measurement of heavy metal species in food/feed powder products such as: