Hyperspectral camera - iRb17UVN16KM

Advanced spectral data acquisition (400–1700 nm)

Hyperspectral camera iRb17UVN16KM is a high-performance system for capturing and analyzing materials based on their spectral signature. It combines the visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) regions in a single device, enabling extremely accurate identification and characterization of substances.

It is suitable for research institutions, industry, and the development of advanced AI systems.

Hyperspectral camera iRb17UVN16KM on a movable table

Key features

  • Spectral range: 400–1700 nm
  • ~680 spectral bands
  • Spectral resolution: ~13 nm
  • Spatial resolution: 1280 px (line-scan)
  • Acquisition speed: do 579 fps
  • High SNR: ~600:1
  • Technology: pushbroom (line-scan)
  • Interface: USB 3.0
  • Lens mount: C-mount

How it works

The camera captures one line of the image at a time (line-scan), with each line containing the entire spectral record. The movement of the object or camera creates a hyperspectral cube (x, y, λ), which allows for the analysis of materials at the individual pixel level.

Features

✔ wide spectral range (VIS + NIR + SWIR)
✔ high analysis accuracy
✔ suitable for real-time applications
✔ easy integration (USB)
✔ support for AI and data analytics

Specifics of use

  • requires controlled lighting (e.g. halogen lights)
  • works on the scanning principle (movement required)
  • generates large amounts of data

Areas of application

Research & Development

  • chemometrics
  • development of AI algorithms
  • materials analysis

Industry

  • sorting materials
  • recycling (plastics, textiles, metals)
  • automatic quality control

Agriculture

  • plant and fruit health analysis
  • perception of stress and illness
  • production optimization

Food industry

  • food quality control
  • foreign object detection
  • composition analysis


We are in the phase of accepting projects and we recommend cooperation, so please contact us with your problem at the email address: marko.vovk1@um.si