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Raman Spectroscopy: Overview

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...

Raman Spectroscopy Instrumentation: Overview

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...

NMR Spectrometers: Resolution and Error Correction

NMR Spectrometers: Resolution and Error Correction

When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...

¹H NMR: Interpreting Distorted and Overlapping Signals

¹H NMR: Interpreting Distorted and Overlapping Signals

Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...

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Related Experiment Video

Updated: Jul 9, 2025

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

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

Published on: September 26, 2019

An Iterative Curve-Fitting Baseline Correction Method for Raman Spectra Driven by Neural Network.

Sicen Dong¹, Yuping Liu^1,2, Hanxiang Yu¹

¹Key Laboratory of Photonic Material and Devices Physics for Oceanic Application, Ministry of Industry and Information Technology of China, College of Physics and Optoelectonic Engineering, Harbin Engineering University, Harbin, China.

Applied Spectroscopy

|December 6, 2023

Summary

A new neural network model improves Raman spectral baseline correction accuracy by adapting function basis to baseline trends, outperforming traditional polynomial fitting and penalized least squares methods.

Keywords:

Baseline correction PLS methods Raman spectra iterative fitting neural network penalized least squares methods

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Area of Science:

Spectroscopy
Chemometrics
Machine Learning

Background:

Baseline correction is crucial for accurate spectral analysis, particularly in Raman spectroscopy.
Conventional methods like iterative polynomial fitting can be inaccurate and prone to distortion.

Purpose of the Study:

To develop a more accurate neural network-based method for spectral baseline correction.
To improve upon the limitations of existing polynomial fitting and penalized least squares (PLS) methods.

Main Methods:

A novel neural network model was designed to detect baseline trends.
The model dynamically selects function bases for precise baseline fitting, unlike fixed polynomial approaches.
A method for generating simulation data to train the neural network was developed.

Main Results:

The proposed neural network model demonstrated reliable baseline correction for noisy real spectral data.
The method effectively handles baselines with unusual shapes.
Comparative analysis showed the approach surpasses conventional iterative polynomial fitting and adaptive iteratively reweighted PLS.

Conclusions:

The neural network model offers a significant advancement in spectral baseline correction accuracy.
This approach provides a flexible and robust alternative for preprocessing challenging spectral data.
The study highlights the potential of machine learning for improving spectral analysis techniques.