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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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Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
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Wavelet Transform-Based UV Spectroscopy for Pharmaceutical Analysis.

Erdal Dinç1, Zehra Yazan2

  • 1Department of Analytical Chemistry, Faculty of Pharmacy, Ankara University, Ankara, Turkey.

Frontiers in Chemistry
|November 13, 2018
PubMed
Summary
This summary is machine-generated.

Wavelet transform is a powerful signal processing technique essential for accurate pharmaceutical analysis. It enhances UV spectroscopic data by removing noise and correcting backgrounds, leading to reliable qualitative and quantitative results.

Keywords:
UV spectroscopycontinuous wavelet transformdiscrete wavelet transformfractional wavelet transformpharmaceutical analysis

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Signal Processing

Background:

  • Chemical and pharmaceutical analysis relies on instrument-generated sample signals.
  • Raw signal analysis can be insufficient for complex samples, necessitating advanced processing.
  • Effective signal processing is crucial for accurate qualitative and quantitative determination.

Purpose of the Study:

  • To review the theoretical aspects of wavelet transform (WT).
  • To explore the applications of WT in UV spectroscopic analysis of pharmaceuticals.
  • To highlight WT as an indispensable tool for pharmaceutical R&D.

Main Methods:

  • Discussion of theoretical foundations of discrete, continuous, and fractional wavelet transforms.
  • Review of WT's utility in noise removal and background correction.
  • Examination of WT's role in data smoothing, filtering, compression, and signal separation.

Main Results:

  • Wavelet transform effectively addresses limitations of raw signal analysis in complex pharmaceutical samples.
  • WT facilitates noise reduction, background correction, and signal enhancement in UV spectroscopy.
  • The method proves versatile for various signal processing tasks including differentiation and data compression.

Conclusions:

  • Wavelet transform is a vital signal processing methodology for pharmaceutical analysis.
  • WT significantly improves the accuracy and reliability of UV spectroscopic data.
  • The application of WT enhances the capabilities of research and development laboratories in the pharmaceutical sector.