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New Dimensions in Raman Microspectroscopy
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Researchers developed RamanLIGHT, an interactive software platform integrating preprocessing tools and eight unsupervised unmixing algorithms for processing hyperspectral Raman imaging datasets, and validated it on pharmaceutical, biological, and industrial samples including microplastics. The thesis further explores Raman-based methodological innovations and biomedical applications, finding that MCR unmixing methods outperformed endmember extraction approaches in robustness for heterogeneous chemical samples.
Raman spectroscopy is a powerful laser-based technique for characterizing chemical compositions through molecular vibrations. This thesis explores advancements in Raman spectroscopy and imaging across three key domains: data processing, methodological innovation, and biomedical applications. Part I introduces Hyperspectral Raman Imaging (HSI), which enables spatially resolved chemical analysis of heterogeneous samples. To address the challenges of processing large spectral datasets, we developed RamanLIGHT, an interactive software platform that integrates preprocessing tools and eight unsupervised unmixing algorithms. RamanLIGHT facilitates rapid identification of chemical components and was validated on pharmaceutical, biological, and industrial samples. A comparative study of Endmember Extraction (EX) and Multivariate Curve Resolution (MCR) methods revealed MCR’s superior robustness in complex mixtures lacking pure pixels. Additionally, a novel multimodal Raman setup was introduced, combining point-scanning and wide-field imaging modes to enable near real-time chemical mapping while overcoming fluorescence interference and slow acquisition speeds. Part II presents Raman Diffusion-Ordered Spectroscopy (Raman-DOSY), a novel technique combining diffusion-based size separation with Raman’s chemical specificity. Raman-DOSY enables label-free analysis of molecular mixtures, determining both size and composition. A miniaturized version of the method was developed for characterizing nanoplastics, demonstrating sensitivity to particles as small as 20 nm. Part III applies Raman imaging to investigate silicone breast implant leakage. Using Stimulated Raman Scattering (SRS) microscopy, we visualized silicone debris in tissue samples without additional staining. SRS imaging revealed a strong correlation between silicone presence and capsular contracture severity, and highlighted significant silicone loss during conventional histological preparation. These findings underscore the need for improved diagnostic methods and further research into implant-related complications.
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A dual principal component analysis method was developed to automatically identify and spatially visualize microplastics and nanoplastics in Raman spectral imaging data, overcoming the challenge of manually interpreting large datasets. The computational approach enables faster and more objective characterization of plastic particles across environmental and biological samples.
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