Hyperspectral Cameras
Unlock the full spectrum of possibilities with our Hyperspectral Cameras. These advanced imaging devices capture data from across the electromagnetic spectrum, far beyond what the eye can see, enabling detailed analysis for applications ranging from agriculture to waste management. Each pixel in a hyperspectral image contains a full spectrum of information, providing powerful insights into materials, processes, and conditions.
How they work
At the core of our hyperspectral cameras lies the principle of advanced light capture and spectral analysis. These cameras are engineered to operate across a broad spectral range, catering to diverse analytical needs. Each pixel in the camera's sensor captures spectral data with high resolution
FigSpec Series Hyperspectral Cameras
Advanced Imaging for Spectral Precision
Unlock the power of hyperspectral imaging with the FigSpec® series. These cameras are engineered to address the complexities traditionally associated with hyperspectral imaging, providing a user-friendly, integrated solution. With high-efficiency transmission grating and sensitive surface array cameras, the FigSpec® series excels in rapid spectral image acquisition. Whether for laboratory, industrial, or field use, these cameras are designed to seamlessly integrate with standard C-mount lenses or microscopes.
Microscopic Hyperspectral Imaging System
Transformative Microscopic Hyperspectral Imaging System
Revolutionise microscopy with hyperspectral capabilities. Adapt existing microscopes to see beyond the visible, extracting rich spectral data at a microscopic level.
FS60- UAV Hyperspectral Measurement System
FS60-UAV - The Aerial Analyst
Elevate your environmental insights. The FS60-UAV system integrates advanced spectral imaging with drone technology for comprehensive aerial data collection.
FS-63 UAV Near-Infrared Hyperspectral Measurement System
FS-63 UAV Hyperspectral System
Precision from above. The FS-63 UAV system brings hyperspectral analysis to new heights, ideal for agricultural monitoring and environmental assessment.
Case Studies: Enhancing Food Safety with Hyperspectral Imaging
1. Detecting Plastic Adulteration in Rice
Situation: In a world where food adulteration is a growing concern, detecting non-edible substances like plastic in rice can be challenging. Traditional methods and human inspection may not always be reliable due to the similarity in appearance between rice grains and plastic particles.
Solution: The FS series hyperspectral camera captures detailed hyperspectral images, revealing differences invisible to the naked eye and traditional cameras. In the provided image, the camera distinctly identifies plastic particles amidst real rice grains, highlighted in different spectral colours.
Outcome: Enhanced food safety through the accurate and non-invasive detection of adulteration, ensuring the purity and authenticity of rice.
2. Identifying Fungal Infections in Mushrooms
Situation: Fungal infections in food items, such as mushrooms, can be difficult to detect, especially in the early stages when visible signs are not yet apparent.
Solution: Using the FS series hyperspectral camera, we can observe a detailed spectral analysis of mushrooms. The camera differentiates healthy tissues from infected ones based on their spectral signatures.
Outcome: Early detection of fungal infections, enabling better quality control and reducing the risk of consuming contaminated food.
3. Enhancing Apple Quality Assessment with Hyperspectral Imaging
Evaluating Damage and Sugar Content in Apples
Situation: Determining the internal quality of apples, such as sugar content, alongside identifying potential areas of damage, is often a complex task with standard inspection methods. Traditional evaluations might overlook subtle internal changes that impact the fruit's overall quality and flavour.
Solution: By employing the FS series hyperspectral camera, we are able to analyse apples in a thorough yet non-destructive manner. The camera's hyperspectral imaging distinctly reveals various aspects of the apples' condition through a spectrum of colours. In the accompanying image, the damaged sections on the apples are distinctly marked in red, providing a clear contrast to the healthier sections of the fruit. The green regions likely indicate areas with higher sugar content, suggesting sweetness and maturity, whereas the blue areas may highlight differences in texture or composition
Outcome: This advanced technology facilitates a comprehensive quality assessment of apples, ensuring only top-grade fruits are offered to consumers. Precise identification of damaged areas and evaluation of sugar content allows for better sorting and marketing of the produce. Consequently, growers and retailers can assure the delivery of superior quality fruits, while consumers enjoy the benefits of consistent and high-quality produce.
Case Study: Revolutionising Industrial Sorting with Hyperspectral Imaging
Advancing Material Sorting in Industrial Applications
Situation: In the realm of industrial sorting, the non-destructive identification and segregation of defective products, various adulterants, and materials with similar appearances but different compositions pose a significant challenge. The task becomes even more complex when sorting involves materials with subtle differences in shape, surface, or internal composition, such as in nuts, grains, seeds, vegetables, fruits, waste materials, and plastics.
Solution: The FS series hyperspectral cameras offer a groundbreaking solution in this domain. Moving beyond traditional visible light solutions, these cameras incorporate near-infrared spectral imaging systems, which are increasingly becoming vital in sorting a wide range of materials.
Identifying Rocks from Potatoes:
Rocks and a potato – similar in colour and shape – are distinctly identified, with the rocks appearing red and the potato green in the hyperspectral image, highlighting their different spectral signatures.
Waste Material Classification:
various waste materials that, while appearing uniform in colour in a standard image, are distinctly categorised into different colours in the hyperspectral image. This differentiation indicates the presence of diverse materials like metals, plastics, and textiles.
Sorting Nuts, Grains, and Seeds:
The hyperspectral camera effectively segregates nuts, grains, and seeds, each appearing in different colours in the spectral image, allowing for precise sorting based on their unique spectral properties.
Polymer Waste Sorting:
Used polymer waste, including bottles, lids, and cups, are analysed. The hyperspectral camera differentiates these polymers, with PMMA appearing as Orange/Yellow, POM as Green, PVC as Blue, and PP as Black. An accompanying graph further illustrates their distinct spectral reflectance profiles, providing a clear and scientific basis for sorting.
Outcome: By leveraging the capabilities of hyperspectral imaging, the FS series cameras are setting new standards in industrial sorting. They offer an efficient, accurate, and non-destructive method to distinguish and sort a vast array of materials. This technology not only enhances the efficiency of sorting processes but also contributes significantly to waste management and recycling efforts, playing a crucial role in sustainable industrial practices.
