Hardware accelerated real time classification of hyperspectral imaging data for coffee sorting

Hyperspectral imaging has been proven to be a viable tool for automated food inspection that is non-invasive and on-line capable. In this contribution a hardware implemented Self-Organizing Feature Map with Conscience (CSOM) is presented that is capable of on-line adaptation and recall in order to learn to classify green coffee varieties as well as coffee of different roast stages. The CSOM showed favourable results in some datasets compared to a number of classical supervised neural network classifiers. The massive parallel neural hardware architecture allows for constant processing times at different map sizes.