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Related Concept Videos

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...
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...
Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...

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Related Experiment Video

Updated: Jul 16, 2026

Exploring the Application of Surface-enhanced Raman Scattering-based Biosensing of Individual sEVs in Disease Diagnosis and Therapeutics
07:17

Exploring the Application of Surface-enhanced Raman Scattering-based Biosensing of Individual sEVs in Disease Diagnosis and Therapeutics

Published on: March 13, 2026

Mineral biosignature identification from Raman spectroscopy using machine learning.

Yanzhang Li1, Anirudh Prabhu1, Bingxu Hou2

  • 1Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015, USA.

PNAS Nexus
|July 15, 2026
PubMed
Summary

This study uses Raman spectroscopy and machine learning to identify biosignatures in apatite minerals. The developed model accurately distinguishes between biotic and abiotic apatite, aiding in the search for extraterrestrial life.

Keywords:
Raman spectroscopyastrobiologybiomineralizationbiosignaturesmineralogy

Related Experiment Videos

Last Updated: Jul 16, 2026

Exploring the Application of Surface-enhanced Raman Scattering-based Biosensing of Individual sEVs in Disease Diagnosis and Therapeutics
07:17

Exploring the Application of Surface-enhanced Raman Scattering-based Biosensing of Individual sEVs in Disease Diagnosis and Therapeutics

Published on: March 13, 2026

Area of Science:

  • Astrobiology
  • Geochemistry
  • Planetary Science

Background:

  • Biosignature detection is crucial for astrobiology, but mineral biosignatures are scarce.
  • Raman spectroscopy offers rich spectral data for planetary exploration, yet its full potential for biosignature discrimination is untapped.
  • Apatite, a common phosphate mineral, is found on Earth and potentially other planets, making it a target for biosignature research.

Purpose of the Study:

  • To develop a data-driven method for distinguishing biotic from abiotic apatite using Raman spectroscopy.
  • To leverage interpretable machine learning for enhanced biosignature analysis in mineral samples.
  • To establish a robust framework for identifying potential signs of life in extraterrestrial apatite.

Main Methods:

  • Compilation and analysis of 331 apatite Raman spectra from biotic and abiotic sources.
  • Extraction of 21 band-resolved spectral features and application of Principal Component Analysis (PCA).
  • Development and validation of a Random Forest classifier, including leave-one-source-out cross-validation across 60 data sources.

Main Results:

  • Principal Component Analysis showed clear separation between biotic and abiotic apatite spectra.
  • The Random Forest classifier achieved 96.8% accuracy on an independent test set.
  • Key spectral features identified were phosphate-band broadening (structural disorder) and carbonate-band intensity (chemical substitution).

Conclusions:

  • The machine learning model demonstrates high accuracy and generalizability for distinguishing biotic from abiotic apatite.
  • Raman spectroscopy combined with machine learning provides a powerful tool for biosignature detection in apatite.
  • This framework is applicable to deep-time geological archives and future planetary missions for astrobiological investigations.