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Related Concept Videos

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...
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
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...

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Related Experiment Video

Updated: May 8, 2026

A Multimodal Wide-Field Fourier-Transform Raman Microscope
06:48

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

Continuous wavelet transform based partial least squares regression for quantitative analysis of Raman spectrum.

Shuo Li, James O Nyagilo, Digant P Dave

    IEEE Transactions on Nanobioscience
    |August 22, 2013
    PubMed
    Summary

    A new Continuous Wavelet Transform-Partial Least Square Regression (CWT-PLSR) algorithm improves in vivo molecular imaging by analyzing Raman spectra. This method robustly handles unstable backgrounds and noise, enhancing prediction accuracy.

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    Last Updated: May 8, 2026

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    Published on: December 30, 2025

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    Published on: February 10, 2020

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    09:32

    Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

    Published on: September 26, 2019

    Area of Science:

    • Analytical Chemistry
    • Biomedical Imaging
    • Spectroscopy

    Background:

    • Surface-enhanced Raman scattering (SERS) nanoparticles show promise for in vivo molecular imaging.
    • Current Partial Least Square Regression (PLSR) methods for SERS data analysis are affected by unstable backgrounds and noise.
    • Existing methods rely solely on Raman spectra intensities, limiting their accuracy.

    Purpose of the Study:

    • To develop a novel Continuous Wavelet Transform-Partial Least Square Regression (CWT-PLSR) algorithm for enhanced quantitative analysis of Raman spectra.
    • To improve the robustness and accuracy of in vivo molecular imaging by mitigating background noise and instability.
    • To provide a more reliable method for predicting concentrations from SERS data.

    Main Methods:

    • A new CWT-PLSR algorithm was designed, utilizing mixing concentrations and average CWT coefficients of Raman spectra.
    • The Mexican hat mother wavelet was employed to derive robust representations of Raman peaks.
    • The algorithm's performance was evaluated using three Raman spectra datasets and three cross-validation methods.

    Main Results:

    • The average CWT coefficients proved to be robust representations of Raman peaks, effectively reducing the impact of unstable baselines and random noise.
    • The CWT-PLSR algorithm demonstrated superior robustness and effectiveness compared to current leading methods.
    • Validation across multiple datasets and cross-validation techniques confirmed the algorithm's reliability.

    Conclusions:

    • The developed CWT-PLSR algorithm offers a significant advancement in quantitative Raman spectra analysis for in vivo molecular imaging.
    • This method provides more accurate predictions by effectively handling spectral interferences.
    • The CWT-PLSR approach represents a robust and effective tool for future molecular imaging applications.