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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
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Raman Spectroscopy: Overview01:20

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
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Spectroscopy of Carboxylic Acid Derivatives01:26

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Infrared spectroscopy is primarily used to determine the types of bonds and functional groups. In carboxylic acid derivatives, a typical carbonyl bond absorption is observed around 1650–1850 cm−1. For esters, the absorption is recorded at around 1740 cm−1, while acid halides show the absorption at about 1800 cm−1. Another acid derivative, the acid anhydrides, exhibit two carbonyl absorption around 1760 cm−1 and 1820 cm−1, arising from the symmetrical and...
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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
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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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Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS
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Automatic Cocrystal Detection by Raman Spectral Deconvolution-Based Novelty Analysis.

Mehrdad Yaghoobi1, Tudor Grecu2, Stephanie Brookes3

  • 1School of Engineering, University of Edinburgh, EH9 3FB Edinburgh, U.K.

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Summary
This summary is machine-generated.

This study introduces an automated Raman spectroscopy method for rapid cocrystal screening. The sparse decomposition algorithm significantly accelerates the analysis of cocrystal preparations, enabling faster drug discovery.

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Area of Science:

  • Pharmaceutical Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Cocrystals offer significant advantages for modifying active pharmaceutical ingredients' properties.
  • Discovering new cocrystals is crucial for drug development, but screening generates vast datasets.
  • Manual analysis of cocrystal screening data is time-consuming and presents a bottleneck.

Purpose of the Study:

  • To develop an automated signal processing routine for rapid cocrystal screening.
  • To improve the efficiency of analyzing Raman spectroscopy data from cocrystal preparations.
  • To maintain the accuracy of cocrystal classification compared to manual methods.

Main Methods:

  • Employed an automated signal processing routine based on a sparse decomposition algorithm.
  • Utilized Raman spectroscopy for discriminating between cocrystals and physical mixtures.
  • Screened 31 potential cocrystal preparations using the developed algorithm.

Main Results:

  • The automated algorithm significantly speeds up data processing for cocrystal screening.
  • A computationally generated threshold allowed clear classification of cocrystals and physical mixtures.
  • Classification was achieved in under a minute, compared to several hours manually.

Conclusions:

  • Automated sparse decomposition of Raman spectra offers a rapid and accurate method for cocrystal screening.
  • This approach can overcome the challenges of analyzing large datasets in drug discovery programs.
  • The developed technique enhances the efficiency of identifying novel cocrystals for pharmaceutical applications.