Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

479
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...
479
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

494
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...
494
Spectroscopy of Carboxylic Acid Derivatives01:26

Spectroscopy of Carboxylic Acid Derivatives

2.5K
Infrared spectroscopy is primarily used to determine the types of bonds and functional groups. In carboxylic acid derivatives, a typical carbonyl bond absorption is observed around 1650–1850 cm−1. For esters, the absorption is recorded at around 1740 cm−1, while acid halides show the absorption at about 1800 cm−1. Another acid derivative, the acid anhydrides, exhibit two carbonyl absorption around 1760 cm−1 and 1820 cm−1, arising from the symmetrical and...
2.5K
IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

2.6K
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...
2.6K
UV–Vis Spectroscopy: Woodward–Fieser Rules01:29

UV–Vis Spectroscopy: Woodward–Fieser Rules

24.9K
UV–Visible absorption spectra of conjugated dienes arise from the lowest energy π → π* transitions. The light-absorbing part of the molecule is called the chromophore, and the substituents directly attached to the chromophore are called auxochromes. A strong correlation exists between the absorption maxima, λmax, and the structure of a conjugated π system. The Woodward–Fieser rules predict the value of λmax for a given...
24.9K
IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

1.5K
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...
1.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Temperature-Responsive Aqueous Two-Phase System Based on Cationic Polyelectrolytes for Polymer Microspheres Preparation.

ACS applied materials & interfaces·2026
Same author

Light-quality-dependent pigment remodeling and <sup>13</sup>C incorporation dynamics in Haematococcus pluvialis revealed by confocal Raman microscopy and Raman-SIP.

Bioresource technology·2026
Same author

Genetic Parameters and Weighted Single-Step Genome-Wide Association Studies of Fertility Traits in Chinese Holstein.

Animals : an open access journal from MDPI·2026
Same author

Stratification system with tumor-associated macrophages for predicting prognostic and therapeutic implications in clear cell renal cell carcinoma.

Journal of the National Cancer Center·2026
Same author

Postoperative Acetaminophen Use and Acute Kidney Injury in Abdominal Surgery: A MIMIC-IV Analysis.

The Journal of surgical research·2026
Same author

Quantifying the causal impact of diseases on lactation features in Holstein cattle based on causal inference approaches.

Journal of dairy science·2026

Related Experiment Video

Updated: Aug 7, 2025

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional &#960;-conjugate Systems
09:57

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems

Published on: February 10, 2020

7.2K

A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning.

Xiaqiong Fan1, Yue Wang1, Chuanxiu Yu1

  • 1College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.

Analytical Chemistry
|March 13, 2023
PubMed
Summary

DeepRaman, a novel method combining a pseudo-Siamese neural network and spatial pyramid pooling, accurately identifies molecules using Raman spectroscopy. This universal, ready-to-use tool overcomes spectral interferences for reliable molecular identification.

More Related Videos

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.2K
Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
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

7.2K

Related Experiment Videos

Last Updated: Aug 7, 2025

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional &#960;-conjugate Systems
09:57

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems

Published on: February 10, 2020

7.2K
Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.2K
Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
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

7.2K

Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Computational Chemistry

Background:

  • Raman spectroscopy provides molecular structural fingerprints for identification.
  • Challenges in Raman spectral analysis include noise, baseline drift, instrument variations, and complex mixtures.
  • Accurate component identification is crucial across various scientific disciplines.

Purpose of the Study:

  • To develop a robust and accurate method for molecular component identification using Raman spectra.
  • To address the limitations of existing methods in handling complex spectral data and instrument variability.
  • To create a universal and readily applicable tool for Raman spectral analysis.

Main Methods:

  • A novel method, DeepRaman, was developed, integrating a pseudo-Siamese neural network (pSNN) for spectral comparison and spatial pyramid pooling (SPP) for input flexibility.
  • The DeepRaman model was trained and validated on 41,564 augmented Raman spectra from pharmaceutical and S.T. Japan databases.
  • Performance was evaluated on six additional datasets acquired using different instruments and in various spectral complexities.

Main Results:

  • DeepRaman achieved high accuracy (96.29%), true positive rate (98.40%), and true negative rate (94.36%) on the test set.
  • The method significantly outperformed the Hit Quality Index (HQI) and other deep learning models.
  • DeepRaman demonstrated robust performance across diverse datasets, including those with complex spectra, low-content components, surface-enhanced Raman spectroscopy (SERS), and Raman imaging.

Conclusions:

  • DeepRaman offers an accurate, universal, and ready-to-use solution for molecular component identification from Raman spectra.
  • The method effectively mitigates challenges posed by spectral interference, noise, and instrument variations.
  • DeepRaman shows significant potential for broad applications in chemical analysis and material science.