Detect and classify using the spectral dimension!
Manteca sits in the agricultural heart of California. It is home to some of the best and most wholesome produce found anywhere. It is also home to Travaille & Phippen, a renowned supplier of the world's finest almonds.
To achieve its esteemed reputation, Travaille and Phippen embraces innovation in the same way that Headwall does. Spectral imaging technology combined with robotics and applications software powers their ability not only to find and remove foreign materials, but also to classify almonds based on a range of geometric and spectral characteristics. This is the essence of advanced machine vision and it enables a degree of product grading unobtainable by any other means.
The ability to grade food products based on numerous geometric and spectral classifications is a key differentiator for hyperspectral imaging sensors. Nuts and specialty crops can be inspected based not on simple 'pass' or 'fail' metrics, but on more subtle characteristics that maximize quality and throughput.
Infrared hyperspectral imaging is a major step forward in the successful detection of foreign matter and pathogens that are visually undetectable but impact the safety of the foods we eat. From poultry and seafood to lamb, beef, and specialty crops, spectral imaging delivers a level of material classification that far exceeds typical RGB color cameras.
Precise color is fundamental to industries such as automotive, textiles, and paint. Headwall's spectral imaging sensors and spectrometers deliver very high spatial and spectral resolution across a wide field of view. Raman and NIR spectroscopy is also useful for material classification such as polymers.
Currency and document inspection are global concerns. Hyperspectral imaging is a non-invasive technology useful to detect subtle differences in materials, pigments, inks, and methods of production. These can all be identified and characterized with very high spatial and spectral resolution through spectral imaging in the NIR and SWIR regions.