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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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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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IR Absorption Frequency: Hybridization01:21

IR Absorption Frequency: Hybridization

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Hydrocarbons such as alkanes, alkenes, and alkynes show characteristic C–H stretching absorption bands. These IR stretching frequencies depend on the hybridization of the involved carbon atom and can be explained in terms of the s character of each hybridized atomic orbital.
Among the sp, sp2, and sp3 hybridized orbitals, sp orbitals have the maximum s character (50%). Consequently, the electrons are held more closely to the nucleus, resulting in stronger and shorter C–H bonds that...
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¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

2.1K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
2.1K
IR Spectrum Peak Broadening: Hydrogen Bonding01:23

IR Spectrum Peak Broadening: Hydrogen Bonding

2.1K
The vibrational frequency of a bond is directly proportional to its bond strength. As a result, stronger bonds vibrate at higher frequencies, while weaker bonds vibrate at lower frequencies. The stretching vibration of the strong O–H bond in alcohols and phenols (very dilute solution or gas phase) appears as a sharp peak at 3600–3650 cm−1.
However, the extent of hydrogen bonding influences the observed stretching frequency and band broadening. Intermolecular or intramolecular...
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Deep Neural Networks for Image-Based Dietary Assessment
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[Terahertz Spectroscopic Identification with Deep Belief Network].

Shuai Ma, Tao Shen, Rui-qi Wang

    Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
    |March 12, 2016
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    Summary

    This study introduces a novel terahertz spectroscopy identification method using Deep Belief Networks (DBN) for automatic feature extraction. The DBN-KNN approach achieves over 90% recognition accuracy for diverse substances, simplifying spectral analysis.

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    Area of Science:

    • Spectroscopy
    • Machine Learning
    • Data Science

    Context:

    • Terahertz (THz) spectroscopy is crucial for material identification.
    • Extracting features from THz spectra is challenging due to limited absorption peaks.
    • Existing methods often require manual feature engineering.

    Purpose:

    • To develop an automated feature extraction and classification method for terahertz spectroscopy.
    • To improve the accuracy and efficiency of identifying substances using THz spectra.
    • To combine Deep Belief Networks (DBN) with K-Nearest Neighbors (KNN) for enhanced identification.

    Summary:

    • Terahertz transmission spectra of eight substances were normalized and processed.
    • A Deep Belief Network (DBN) model, comprising two Restricted Boltzmann Machines (RBMs), was trained unsupervised to learn spectral features automatically.
    • A K-Nearest Neighbors (KNN) classifier was utilized for substance identification based on DBN-extracted features.

    Impact:

    • The DBN-KNN method achieved over 90% recognition accuracy for terahertz spectra identification.
    • DBN automatically extracts effective features, significantly reducing manual workload.
    • KNN outperformed other classifiers (BP, SOM, RBF), demonstrating the method's effectiveness and promising applications in mass terahertz spectroscopy.