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Raman Spectroscopy: Overview

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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.
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Raman Spectroscopy Instrumentation: Overview

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
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The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell. Samples for...

Molecular Spectroscopy: Absorption and Emission

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Molecules possess discrete energy levels called quantum states. Unlike atoms, which have simpler energy levels, molecules possess additional rotational and vibrational energy levels. Each energy level is separated by an energy gap, with the gaps between adjacent electronic, vibrational, and rotational levels varying significantly. The three types of energy levels in a diatomic molecule are shown in Figure 1.

UV–Vis Spectroscopy of Conjugated Systems

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Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π&VeryThinSpace;→&VeryThinSpace;π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...

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Related Experiment Video

Updated: May 10, 2026

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

Published on: July 25, 2022

Eigenspectra, a robust regression method for multiplexed Raman spectra analysis.

Shuo Li¹, James O Nyagilo, Digant P Dave

¹Computer Science and Engineering Department, University of Texas at Arlington, Arlington, Texas 76013, USA. shuo.li@uta.edu

International Journal of Data Mining and Bioinformatics

|June 27, 2013

Summary

This summary is machine-generated.

This study introduces a machine learning method to precisely identify components and their ratios in complex mixtures using Raman nanotags. This advancement enhances the capabilities of Raman spectroscopy for bioimaging and biosensing applications.

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A Multimodal Wide-Field Fourier-Transform Raman Microscope

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

Area of Science:

Nanotechnology and Spectroscopy
Machine Learning in Chemical Analysis

Background:

Surface Enhanced Raman Scattering (SERS) nanoparticles enable advanced applications of Raman spectroscopy.
Raman spectroscopy's potential in bioimaging and biosensing is expanding with new nanoparticle developments.

Purpose of the Study:

To demonstrate Raman spectroscopy's capability in separating multiple spectral fingerprints using Raman nanotags.
To propose and validate a machine learning method for estimating mixing ratios from spectral data.

Main Methods:

Utilized Raman nanotags for spectral fingerprint separation.
Developed a machine learning algorithm to decompose mixture signals into representative components.
Calculated regression coefficients for predicting mixing ratios and compared method robustness against least squares and weighted least squares.

Main Results:

Successfully demonstrated the separation of multiple spectral fingerprints.
The proposed machine learning method effectively estimates mixing ratios from complex spectral data.
The robustness of the machine learning approach was validated against traditional methods.

Conclusions:

Raman spectroscopy, enhanced by SERS nanoparticles and nanotags, can effectively resolve complex mixtures.
The developed machine learning method provides a robust tool for quantitative analysis in spectral mixtures.
This work advances the application of Raman spectroscopy in bioimaging and biosensing.