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IR Frequency Region: Fingerprint Region

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
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Related Experiment Video

Updated: Jul 9, 2026

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

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

Published on: March 13, 2026

[Supervised feature extraction based on FDA and galaxy spectra classification].

Xiang-Ru Li¹, Zhan-Yi Hu, Yong-Heng Zhao

¹Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu

|December 7, 2007

Summary

This summary is machine-generated.

Supervised feature extraction using Fisher discriminant analysis (FDA) improves galaxy spectra classification. This method effectively reduces dimensions and enhances feature extraction for astronomical surveys.

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Last Updated: Jul 9, 2026

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

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ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

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Published on: August 19, 2021

Area of Science:

Astronomy and Astrophysics
Data Science
Machine Learning

Context:

Advancements in wide field surveys generate vast astronomical spectral data.
Automated processing of celestial spectra is crucial for modern astronomical research.
Existing unsupervised methods have limitations in spectral feature extraction.

Purpose:

Investigate supervised feature extraction for celestial spectra classification.
Analyze limitations of unsupervised feature extraction methods.
Apply Fisher discriminant analysis (FDA) to galaxy spectra classification.

Summary:

Fisher discriminant analysis (FDA) is proposed as a supervised feature extraction technique.
FDA effectively reduces dimensionality by leveraging class-discriminating information in training data.
Experiments demonstrate FDA's superior performance in dimensional reduction for galaxy spectra.

Impact:

Enhances the efficiency and accuracy of galaxy spectra classification.
Provides a robust method for analyzing large astronomical spectral datasets.
Facilitates deeper understanding of galaxy populations and cosmic evolution.