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Related Concept Videos

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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 stretching vibration...
IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
Stretching vibrations are vibrational motions that occur along the bond line, changing the bond length or distance between two bonded atoms. They are further distinguished as symmetric or asymmetric. In symmetric stretching, the...
IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to the...
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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.
The...
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and the...

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Related Experiment Video

Updated: May 18, 2026

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy
08:49

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Published on: December 1, 2023

Morphological analysis of vibrational hyperspectral imaging data.

Jacob Filik1, Abigail V Rutter, Josep Sulé-Suso

  • 1Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK. jacob.filik@diamond.ac.uk

The Analyst
|September 25, 2012
PubMed
Summary

This study uses image processing to simplify hyperspectral data from biological cells. Morphological analysis automates spectral averaging, enabling easier identification of cellular components like the nucleus.

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Last Updated: May 18, 2026

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy
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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

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Published on: June 18, 2021

Area of Science:

  • Biomedical Imaging
  • Spectroscopy
  • Computational Biology

Background:

  • Hyperspectral imaging generates large datasets, posing challenges for analysis.
  • Fourier transform infrared (FTIR) imaging provides rich biochemical information but requires efficient data reduction.

Purpose of the Study:

  • To develop and validate automated morphological image processing for hyperspectral data reduction.
  • To simplify the analysis of hyperspectral images of biological cells.
  • To identify spectral differences between cellular components.

Main Methods:

  • Standard morphological image processing techniques applied to Focal Plane Array FTIR absorbance images.
  • Automated spectral averaging per particle/cell, even for contacting particles.
  • Principal Components Analysis (PCA) for spectral difference identification.

Main Results:

  • Successfully reduced ~40,000 spectra to ~100 mean spectra per cell.
  • Automated cell size measurement and independent spectral analysis per cell.
  • Identified consistent spectral differences between whole cells and nucleus regions, validated by prior IR measurements.
  • Demonstrated applicability to diverse cell morphologies (CALU-1 and NL20 cell lines).

Conclusions:

  • Morphological image processing offers an efficient method for hyperspectral data reduction in biological samples.
  • This approach simplifies complex datasets, enabling detailed analysis of cellular components.
  • The automated method is robust and adaptable to various cell types and morphologies.