Integrated in the processing line, the sensor system separates the raw material objects and automatically examines morphology and material composition. The sensor system includes mechanical separation, optical sensing and on-board algorithms.
The novel sensor allows the material-specific and morphological composition of the raw material objects to be continuously monitored in the processing line, and corrective measures can be initiated at an early stage in the event of deviations from the specification. This increases the quality of the output and significantly reduces costs by minimizing wastage. The reduced wastage also results in considerable energy savings during production, which contributes to the reduction of CO2 emissions.
Hyperspectral imaging provides sensitive, non-contact (non-invasive) analysis capabilities for diverse fields such as medicine (e.g. dermatology), food industry, raw material processing, industrial automation and others.
Hyperspectral imaging measures multiple and higher resolution spectra, which can include the near (non-visible) infrared range, compared to the color camera, which essentially resolves three colors. Light interacts with material in different ways, and the interaction depends on wavelength, resulting in a characteristic spectral fingerprint. In hyperspectral camera imaging, the wavelength spectrum of the received light is recorded per pixel, which allows physical and chemical properties to be assigned to objects in the image area. Unlike classical spectroscopy, hyperspectral imaging can measure large areas with high spatial resolution.
For example, the characteristic light scattering/reflection enables detection of material categories (e.g., plastic type), monitoring of the degree of ripeness of single fruits and other agricultural products, identification of tumor tissue and measurement of blood flow in skin, and large-area characterization of thin layers.