Emerging Innovations in Hyperspectral Imaging: Experimental Technologies, Chemometrics, and AI
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 21
Special Issue Editor
2. Institute of Physical and Theoretical Chemistry, University of Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany
Interests: fluorescence and Raman imaging; sensor development; biophysics; process analytics; hyperspectral imaging; chemometrics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In optical spectral sensing and imaging, spatial information is combined with spectroscopy to create versatile tools for science and industry.
These methods provide rapid, non-destructive insights and have become indispensable in recent years.
Progress is increasingly driven by experimental advances in hyperspectral imaging (HSI). Improvements in detector sensitivity, optical components, and illumination concepts have enabled faster acquisition, higher resolution, and more robust setups. Compact, application-specific HSI modules are moving from laboratory prototypes to field-ready and clinical systems. New hardware developments—such as miniaturized spectrographs, tunable filters, and integrated sensor chips—extend the applicability of HSI to real-time quality control, biomedical diagnostics, and environmental monitoring.
Alongside these hardware innovations, powerful computational methods ensure that the vast data streams generated can be handled efficiently. Chemometrics and artificial intelligence (AI) provide essential tools for extracting meaningful information, supporting the transition of HSI from experimental systems to routine analytical solutions.
This Special Issue focuses on the applications and technological evolution of spectral imaging, highlighting experimental breakthroughs in sensors and imaging setups, advanced data analysis methods, and cross-disciplinary case studies.
Prof. Dr. Marc Brecht
Guest Editor
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Keywords
- hyperspectral imaging
- spectral sensor technology
- experimental spectroscopy
- chemometrics
- machine learning in spectral analysis
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