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Infrared (IR) Spectroscopy: Overview

Infrared (IR) Spectroscopy: Overview

When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...

IR Spectrum

IR Spectrum

When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0% (complete...

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...

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 Spectroscopy: Molecular Vibration Overview

IR Spectroscopy: Molecular Vibration Overview

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...

Applications of IR Spectroscopy: Overview

Applications of IR Spectroscopy: Overview

The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...

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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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A factorization method for the classification of infrared spectra.

Carsten Henneges¹, Pavel Laskov, Endang Darmawan

¹Zentrum für Bioinformatik Tübingen, Eberhard Karls Universität Tübingen, Sand 13, Tübingen, Germany. carsten.henneges@uni-tuebingen.de

BMC Bioinformatics

|November 17, 2010

Summary

This summary is machine-generated.

This study introduces a new signal factorization method for analyzing mixed data, improving disease classification accuracy in metabolic monitoring. The technique enhances data mining by estimating class-specific signals from infrared spectroscopy data.

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Area of Science:

Bioinformatics
Signal Processing
Spectroscopy

Background:

Bioinformatics data analysis frequently involves additive signal mixtures with unknown class-specific signals.
Metabolic monitoring using infrared spectroscopy is a key application where compounds contribute quantitatively to spectral data.

Purpose of the Study:

To develop a novel factorization technique for additive signals using classified samples.
To enable the estimation of class-related signals for data mining in mixture analysis.

Main Methods:

Proposing a novel factorization technique for additive signal factorization.
Defining a composed loss function and deriving a closed-form equation for model training.
Reducing model training to finding an optimal threshold vector.

Main Results:

Achieving high sensitivity (up to 0.958) and specificity (up to 0.841) in a 15-class disease classification task.
Demonstrating superior performance over linear SVM for training with many classes and limited data.
Validating the method on both synthetic and clinical infrared spectroscopy data.

Conclusions:

The proposed factorization method is simple and generative.
It represents a foundational step towards developing predictive factorization techniques.
The method facilitates learning from classified samples for signal estimation.