Research Publications
Explore hyperspectral imaging by reading our research articles.
07/01/2026
Old or valuable? - Detection of degraded polyethylene using NIR hyperspectral imaging for highquality plastic recycling
The quality of polyethylene (PE) recyclates is crucial for their use in new applications. However, the presence of aged PE products in the recycling stream can reduce PE recyclate quality. To minimize the need for virgin material or additional stabilizers to counteract this quality loss, reliable identification and removal of heavily aged materials is essential for sustainably improving the quality of PE recyclates. In this study, the potential of near-infrared (NIR) hyperspectral imaging for detecting aging and degradation in PE was investigated. For this purpose, PE samples were artificially aged by UV irradiation …
2026
Assessing Melt Flow Rate in Post-Consumer Polypropylene via Near-Infrared Hyperspectral Imaging
Plastics are widespread across sectors, with packaging alone accounting for approximately 40% of total plastic use [1]. Recognizing the environmental imperative to close material loops, the European Union’s Packaging and Packaging Waste Regulation (PPWR) mandates minimum recycled-content targets by 2030—for instance, 10% for contact-sensitive packaging made from plastics other than polyethylene terephthalate (PET) and 35% for non-contact-sensitive packaging [2]. While these requirements are expected to stimulate demand for high-quality recyclates, …
2024
Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping
This research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping. Furthermore, a concept for the analysis of quality assured hyperspectral images to investigate plant disease progress is proposed. The quality assurance was applied to a handheld line scanning HSI-system consisting of evaluating spatial and spectral quality parameters as well as the integrated illumination. To test the spatial accuracy at different working distances, the sine-wave-based spatial frequency response (s-SFR) was analysed. The spectral accuracy was assessed by calculating the correlation of …
23/04/2024
15th International Mine Water Association Congress 2024: Hyperspectral UAV-sensing for monitoring tailing ponds: Towards responsible resource repurposing
The interest in repurposing mine residues has substantially increased over the last decade as the mining industry endeavors to minimize environmental footprints and legacies. The industry also aims to meet the growing demand for critical raw materials
(CRMs), especially those essential for technology, by considering mine waste as a potential supplementary secondary resource. As part of the ongoing mine water management perpetual tasks in the Ibbenbüren coalfield in Germany (closed in 2018), residual sludge material from the Gravenhorst sewage treatment ….
14/12/2023
Estimating dry matter and total soluble content in apples using a commercial portable hyperspectral imaging system
The quest for rapid, non-destructive, and precise technologies for fruit quality estimation is motivated by the needs across the whole food production chain. One of the emerging technologies fulfilling these requirements is spectral imaging. However, despite documented successes, the technology is yet to become established in commercial applications. The best results reported in the literature rely on fixed, non-portable dedicated setups, and controlled light conditions, which limits the potential use cases along the food production chain.
08/03/2023
Characterization and digital aberration correction of a hyperspectral imaging system for plant disease detection
Hyperspectral imaging is a key technology for monitoring agricultural crops and vegetation. It can be used for health estimation and the early detection of disease symptoms in plants. This can help to reduce the use of pesticides by allowing targeted and early intervention. Cost-efficient hyperspectral imaging systems are necessary to meet the increasing demand for monitoring techniques for agricultural products. These systems usually suffer from sub-optimal image quality. Here we present a digital aberration correction for hyperspectral image data.
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