Hyperspectral Imaging Application
Food Industry
Learn more about how to you use spectral imaging cameras for food industry monitoring
HAIP Solutions
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Food Industry
Hyperspectral Imaging allows to study food chemical composition, adulteration, ripeness, freshness or bruising on various fruits and vegetables. It can also identify foreign materials such as shell pieces, wood, stones, paper, metal or insects.
In comparison to many other technologies, HSI can reliably identify materials based on biological and chemical content which enable the supply chain to optimize product consistency, improve yield, reduce waste and deliver quality.
Common use cases
- Food fraud detection
- Foreign object detection
- Chemical composition
- Quality differentiation
- Ripeness measurement
- Protein content
Where hyperspectral imaging creates real impact
BlackIndustry SWIR 1.7 Max: Detection of Undesired Components in the Almond Production
Almonds, the edible seeds of the Prunus dulcis tree, have been prized for …
BlackIndustry SWIR 1.7 / Max: Foreign Objects in Rice
Rice plays an essential social and economic role worldwide as a staple food and commodity. Almost a third of …
Hyperspectral Imaging Solutions
Food Industry
BlackIndustry VNIR V2
500-1000 nm wavelength range
BlackIndustry NIR
700-1050 nm wavelength range
BlackBullet V2
Combined RGB & HSI camera
BlackIndustry SWIR
1.7
900-1730 nm wavelength range
BlackIndustry SWIR 1.7
Max
900-1730 nm wavelength range
BlackIndustry SWIR 1.7 Pro Max
900-1730 nm wavelength range
Other Applications
VEGETATION
Hyperspectral imaging enable detailed monitoring of vegetation, allowing to analyse plant stress responses, nutrient status and growth dynamics over time.
RECYCLING
HSI allows sorting systems to identify materials based on their spectral properties, enabling more accurate classification and improved recyclate quality.
ENVIRONMENTAL MONITORING
Hyperspectral cameras provide high-resolution spectral data that supports large-scale environmental monitoring and ecosystem analysis.
GEOLOGY
HSI makes the identification and classification of minerals based on their spectral signatures possible, supporting more detailed material analysis.