Any bulk food product – any critical defect.
We are pushing the boundaries of what is possible and setting new standards in the detection and selection of foreign objects and food defects. Using Deep Neuronal Network technology in combination with the well proven Chemical Imaging Technology (CIT® Gen3), SHERLOCK HYPERNOVA detects, processes and optimizes data from the sorting process with the highest reliability. The combination of these breakthrough technologies creates infinite possibilities on an unprecedented scale.
EVERYTHING NEW STARTS WITH A BIG BANG.
Thanks to the latest generation of Chemical Imaging Technology (CIT® Gen3) in combination with high-resolution color cameras, both the smallest foreign bodies and product defects can be sorted out with unprecedented accuracy. This is done with maximum conservation of product and energy resources.
The new Sherlock HYPERNOVA has been specially developed for sorting small-sized food products. The modular design allows to be configured specifically for each product, position in the line and tailored to individual requirements.
Artificial Intelligence applied in real–time
For the first time, artificial intelligence in the most versatile form of Deep Neuronal Networks is applied in real time to analyse the collected image data. It creates algorithms that enable the detection of even the smallest visible product defects as well as defects invisible to the human eye, fast enough to eject them from high-speed product flows.
This new disruptive technology opens unimaginable applications and performance levels for food processors.
InlineFOODLAB 4.0 allows processors to get quantiative chemical data of the product and the most reliable real time inline quality data available in the industry. Drymatter values in potato products, rancidity in nuts, amygdalin in almonds, oilcontent in pumpkin seeds, moisture level in dried fruit, shell count in nuts or Brix level in fruits are just some examples. They can be delivered combined with color and shape characteristics as well as with size values of the objects. Additionally any Foreign Material can be recorded with a picture and delivered to your data base where applicable for the purpose. By doing so, quality managers have better tools to control the incoming raw material as well as the outgoing final product in order to reduce product rework and claim rates or to prevent product recalls better than ever before.
SOME DEFECT EXAMPLES
- All types of foreign material
- Bitter Almonds
- Moisture content
- Oil content
- Chip & Scratch
- Insect damage
- Embedded shells
- Visible and invisible rot in nuts