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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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...
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Infrared (IR) Spectroscopy: Overview01:09

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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.
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IR Spectrum01:19

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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.
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IR Spectrometers01:25

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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...
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Applications of IR Spectroscopy: Overview01:11

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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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IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

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Infrared spectroscopy, also known as vibrational spectroscopy, is mainly used to determine the types of bonds and functional groups in molecules. In aldehydes and ketones, the carbonyl (C=O) bond shows an absorption around 1710 cm-1. The C=O bond vibration of an aldehyde occurs at lower frequencies than that of a ketone. In addition to the C=O absorption in an aldehyde, the aldehydic C–H bond also gives two peaks in the 2700–2800 cm-1 range. This absorption, coupled with the...
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Toward Informative Representations of Blood-Based Infrared Spectra via Unsupervised Deep Learning.

Corinna Wegner1, Zita I Zarandy1,2,3, Nico Feiler1,2

  • 1Chair of Experimental Physics-Laser Physics, Ludwig-Maximilians-Universität München (LMU), Garching, Germany.

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Summary
This summary is machine-generated.

Unsupervised deep learning condenses infrared blood spectra using a denoising autoencoder. This approach enhances lung cancer detection accuracy by improving molecular data representation.

Keywords:
biomarkersdeep learningdisease diagnosticsinfrared spectroscopyliquid biopsies

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Spectroscopy

Background:

  • Infrared molecular fingerprints of human blood contain complex information.
  • Extracting meaningful diagnostic biomarkers from spectral data is challenging.
  • Current methods may struggle with noise reduction and data dimensionality.

Purpose of the Study:

  • To develop a deep learning model for low-dimensional representation of infrared blood spectra.
  • To investigate the utility of this representation for lung cancer detection.
  • To improve the accuracy and interpretability of spectral data analysis.

Main Methods:

  • Utilized a fully convolutional denoising autoencoder for Fourier transform infrared (FTIR) spectroscopy data.
  • Employed a bottleneck architecture and a custom loss function for noise reduction and information preservation.
  • Applied the method to a case-control study for lung cancer detection.

Main Results:

  • Successfully generated a low-dimensional latent space from complex FTIR spectra.
  • Demonstrated effective noise reduction while retaining crucial molecular information.
  • Achieved a 2.6 percentage point improvement in lung cancer detection accuracy.

Conclusions:

  • Unsupervised deep learning offers a powerful approach for analyzing infrared molecular fingerprints.
  • The developed autoencoder effectively compacts spectral data and identifies disease-associated variables.
  • This methodology shows promise for enhancing diagnostic capabilities in medical research.