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• W4310928835 abstract "Challenges to deep sample imaging have necessitated the development of special techniques such as spatially offset optical spectroscopy to collect signals that have travelled through several layers of tissue. However, these techniques provide only spectral information in one dimension (i.e., depth). Here, we describe a general and practical method, referred to as Optical Recognition of Constructs Using Hyperspectral Imaging and Detection (ORCHID). The sensing strategy integrates (1) the spatial offset detection concept by computationally binning 2D optical data associated with digital offsets based on selected radial pixel distances from the excitation source; (2) hyperspectral imaging using tunable filter; and (3) digital image binding and collation. ORCHID is a versatile modality that is designed to collect optical signals deep inside samples across three spatial (X, Y, Z) as well as spectral dimensions. The ORCHID method is applicable to various optical techniques that exhibit narrow-band structures, from Raman scattering to quantum dot luminescence. Samples containing surface-enhanced Raman scattering (SERS)-active gold nanostar probes and quantum dots embedded in gel were used to show a proof of principle for the ORCHID concept. The resulting hyperspectral data cube is shown to spatially locate target emitting nanoparticle volumes and provide spectral information for in-depth 3D imaging." @default.
• W4310928835 created "2022-12-21" @default.
• W4310928835 creator A5022467457 @default.
• W4310928835 creator A5057286302 @default.
• W4310928835 date "2022-12-07" @default.
• W4310928835 modified "2023-10-16" @default.
• W4310928835 title "Optical recognition of constructs using hyperspectral imaging and detection (ORCHID)" @default.
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• W4310928835 doi "https://doi.org/10.1038/s41598-022-25735-9" @default.
• W4310928835 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36476976" @default.
• W4310928835 hasPublicationYear "2022" @default.
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