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Related Concept Videos

IR Spectrometers01:25

IR Spectrometers

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There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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IR Spectrum01:19

IR Spectrum

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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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Spectrophotometry: Introduction01:16

Spectrophotometry: Introduction

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Spectrophotometry is the quantitative measurement of the absorption, reflection, diffraction, or transmission of electromagnetic radiation through a material as a function of the intensity and wavelength of the radiation. A spectrophotometer is a device used to measure the change in the radiation intensity caused by its interaction with the material.
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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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...
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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Singular Spectrum Analysis: A Note on Data Processing for Fourier Transform Hyperspectral Imagers.

J Bruce Rafert1, Jaime Zabalza2, Stephen Marshall2

  • 1Department of Physics, North Dakota State University, Fargo, North Dakota, USA bruce.rafert@ndsu.edu.

Applied Spectroscopy
|May 6, 2016
PubMed
Summary
This summary is machine-generated.

Singular spectrum analysis (SSA) offers a novel physics-based framework for processing hyperspectral data from Fourier transform (FT) imagers. This method adaptively decomposes interferograms, addressing data processing challenges and improving analysis of signal, noise, and illumination patterns.

Keywords:
Fourier transform imagersHyperspectral image processingSingular spectrum analysis

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Area of Science:

  • Remote Sensing
  • Signal Processing
  • Data Analysis

Background:

  • Hyperspectral remote sensing is rapidly advancing with new sensors and applications.
  • Fourier transform (FT) systems offer high throughput and resolution but face data processing challenges.
  • Existing methods for FT hyperspectral data processing have limitations.

Purpose of the Study:

  • Introduce a new physics-based analytical framework, Singular Spectrum Analysis (SSA), for hyperspectral FT imagery.
  • Address data processing issues associated with the inverse FT in hyperspectral imaging.
  • Develop a method to effectively decompose and analyze raw hyperspectral interferogram data.

Main Methods:

  • Applied Singular Spectrum Analysis (SSA) to process raw hyperspectral imagery from FT imagers.
  • Utilized synthetic interferogram data for analysis.
  • Employed adaptive decomposition of interferograms into independent components.

Main Results:

  • SSA successfully decomposed synthetic interferogram data.
  • Identified independent components related to signal, photon noise, system noise, and illumination patterns.
  • Demonstrated SSA's capability to address data processing issues in FT hyperspectral imaging.

Conclusions:

  • SSA provides a robust framework for processing hyperspectral data from FT imagers.
  • The method effectively separates signal, noise, and illumination components.
  • SSA shows promise for advancing hyperspectral remote sensing applications using FT systems.