The presence of heavy metals in food / feed is a timely issue that involves contamination of the food chain and poses risks to public health. One potential solution is to conduct more frequent testing of food / feed samples. However, existing testing methods are often time-consuming and require highly skilled laboratory personnel. This technology combines spectroscopic methods with machine learning to enable the rapid detection of heavy metals in food / feed samples.
The developed machine learning model can:
perform a multi-class differentiation of different types of heavy metals based on spectroscopic measurements.
Predict the concentration of heavy metals in food / feed powders using spectroscopic measurements.
Require minimal sample preparation, allowing for the swift screening of food / feed powder samples.
Applicable to insect powders, animal feeds, milk powders, protein supplement powders, and plant-based nutritional supplements
Quick detection of heavy metal species with minimal sample preparation.
Capacity to screen large quantities of samples in a short amount of time.
Comparable model performance and predicted results to industry-accepted methods for measuring heavy metal content.
This technology is available for licensing and technology transfer.
Get in touch with us today for more information and collaboration opportunities! Drop Us a Message