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Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
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In IR spectroscopy of carboxylic acids, the C=O bond shows a characteristic band between 1710 and 1760 cm⁻¹, and the O–H bond exhibits a broad band between 2500 and 3300 cm⁻¹.
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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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Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
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UV–Visible absorption spectra of conjugated dienes arise from the lowest energy π → π* transitions. The light-absorbing part of the molecule is called the chromophore, and the substituents directly attached to the chromophore are called auxochromes. A strong correlation exists between the absorption maxima, λmax, and the structure of a conjugated π system. The Woodward–Fieser rules predict the value of λmax for a given...
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The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
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Updated: Aug 8, 2025

Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores
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Machine Learning Identification of Organic Compounds Using Visible Light.

Thulasi Bikku1,2, Rubén A Fritz1, Yamil J Colón3

  • 1Department of Physics, Universidad de Santiago de Chile, Av. Victor Jara 3493, Santiago, Chile.

The Journal of Physical Chemistry. A
|March 6, 2023
PubMed
Summary
This summary is machine-generated.

Scientists developed a machine learning classifier to identify organic compounds using visible light. This method analyzes refractive index data, enabling accurate chemical identification without needing infrared spectroscopy.

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Area of Science:

  • Chemistry
  • Materials Science
  • Spectroscopy

Background:

  • Chemical compound identification is crucial across science and engineering.
  • Laser-based techniques offer remote detection by analyzing optical responses.
  • Infrared spectroscopy utilizes molecular fingerprints for identification, but visible light methods are lacking.

Purpose of the Study:

  • To develop a method for chemical identification using visible light.
  • To overcome limitations of infrared spectroscopy for autonomous material identification.

Main Methods:

  • Compiled experimental refractive index data for organic compounds and polymers.
  • Utilized a machine learning classifier trained on ultraviolet to far-infrared spectral data.
  • Focused on single-wavelength dispersive measurements in the visible spectrum, away from absorption resonances.

Main Results:

  • Developed a machine learning classifier capable of accurate organic species identification.
  • Demonstrated effective identification using visible light measurements.
  • Showcased the potential for autonomous material identification.

Conclusions:

  • Visible light spectroscopy, coupled with machine learning, provides a viable alternative for chemical identification.
  • The proposed method enhances autonomous material identification capabilities.
  • This approach broadens the scope of optical techniques for chemical analysis.