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IR Spectrometers

IR Spectrometers

There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...

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.
The...

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Related Experiment Video

Updated: Jul 3, 2026

High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

Published on: January 21, 2015

[Multi-layer perceptron neural network based algorithm for simultaneous retrieving temperature and emissivity from

Jie Cheng¹, Qing Xiao, Xiao-Wen Li

¹State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China. brucechan2003@126.com

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu

|July 16, 2008

Summary

This summary is machine-generated.

A novel neural network approach improves temperature and emissivity separation from hyperspectral FTIR data, overcoming limitations of traditional algorithms for robust soil emissivity spectra analysis.

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Area of Science:

Remote Sensing
Spectroscopy
Artificial Intelligence

Context:

Hyperspectral Fourier Transform Infrared (FTIR) data analysis presents challenges in accurately separating temperature and emissivity.
Conventional algorithms struggle when radiance differences and instrument noise are comparable, particularly near 714 and 1250 cm(-1).

Purpose:

To address the limitations of existing temperature and emissivity separation algorithms.
To introduce and evaluate a three-layer perceptron neural network (MLP) for simultaneous temperature and emissivity inversion from hyperspectral FTIR data.

Summary:

A three-layer perceptron neural network was trained using soil emissivity spectra from the ASTER spectral library and tested with MODIS spectral library data.
The MLP demonstrated robustness in retrieving temperature and emissivity, outperforming the ISSTES algorithm in certain aspects.
The MLP effectively overcomes the inherent disadvantages of typical temperature and emissivity separation methods.

Impact:

The developed MLP offers a robust and effective solution for accurate temperature and emissivity retrieval from hyperspectral FTIR measurements.
This method serves as a valuable complement to existing algorithms, enhancing the reliability of remote sensing data analysis.
The findings contribute to improved understanding and application of hyperspectral data in various scientific fields.