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UV–Vis Spectroscopy: Woodward–Fieser Rules01:29

UV–Vis Spectroscopy: Woodward–Fieser Rules

UV–Visible absorption spectra of conjugated dienes arise from the lowest energy π → π* transitions. The light-absorbing part of the molecule is called the chromophore, and the substituents directly attached to the chromophore are called auxochromes. A strong correlation exists between the absorption maxima, λmax, and the structure of a conjugated π system. The Woodward–Fieser rules predict the value of λmax for a given structure by adding the contributions...
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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[Mineral information extraction for hyperspectral image based on modified spectral feature fitting algorithm].

Ning Xu1, Yu-Xin Hu, Bin Lei

  • 1Institute of Electronics, Chinese Academy of Sciences, Beijing 100080, China. x_ning520@yahoo.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|August 19, 2011
PubMed
Summary
This summary is machine-generated.

A modified spectral feature fitting (SFF) algorithm enhances mineral identification in hyperspectral images by incorporating user-defined constraints. This improved method offers more accurate target information extraction compared to general SFF and spectral angle mapping (SAM).

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Area of Science:

  • Geoscience
  • Remote Sensing
  • Spectroscopy

Context:

  • Hyperspectral imaging is crucial for mineral exploration and geological mapping.
  • Traditional spectral feature fitting (SFF) algorithms have limitations in accurately extracting mineral information.
  • Subtle spectral features can be challenging to differentiate using existing methods.

Purpose:

  • To introduce a modified spectral feature fitting (SFF) algorithm designed to overcome the drawbacks of the general SFF method.
  • To enhance the accuracy of mineral information extraction from hyperspectral data.
  • To validate the efficacy of the modified SFF algorithm through experimental comparison.

Summary:

  • A novel modified spectral feature fitting (SFF) algorithm is presented, integrating user-defined constraints into spectral absorption features.
  • The modified SFF algorithm was tested against general SFF and spectral angle mapping (SAM) using hyperspectral data with varying resolutions and signal-to-noise ratios (SNR).
  • Experimental results demonstrate that the modified SFF algorithm excels at differentiating subtle spectral features and yields more accurate mineral identification, including muscovite, calcite, and chlorite.

Impact:

  • The modified SFF algorithm provides a more effective tool for accurate mineral identification in hyperspectral remote sensing.
  • This advancement improves the extraction of geological information from complex spectral datasets.
  • The validated algorithm has practical applications in mineral resource exploration and geological surveys.