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UV–Vis Spectrum01:30

UV–Vis Spectrum

1.2K
When light passes through a substance, a portion of the light is absorbed while the remaining light is reflected or transmitted. If the molecule absorbs light between the wavelengths of 180–400 nm range, the UV spectrum is obtained, and if it absorbs light in the 400–780 nm wavelength range, the visible spectrum is obtained.     
The UV–Vis spectrum of a molecule is the plot of its absorbance versus wavelength. The plot is drawn by taking molar...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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UV–Vis Spectrometers01:14

UV–Vis Spectrometers

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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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UV–Vis Spectroscopy: Woodward–Fieser Rules01:29

UV–Vis Spectroscopy: Woodward–Fieser Rules

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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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Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

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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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Related Experiment Video

Updated: Aug 24, 2025

Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy
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Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy

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A Data Set Comparison Method Using Noise Statistics Applied to VUV Spectrum Match Determinations.

David C Bell1, Randall C Boehm1, John Feldhausen1

  • 1Department of Mechanical Engineering, University of Dayton, 300 College Park, Dayton, Ohio45469, United States.

Analytical Chemistry
|October 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using spectral residual analysis to accurately identify chemical species, even when traditional R-squared values are misleading. It ensures reliable identification by analyzing noise patterns beyond simple spectral similarity.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Traditional spectral matching using coefficient of determination (R-squared) can be ambiguous, with similar R-squared values sometimes indicating different chemical species.
  • The signal-to-noise ratio significantly influences R-squared values in spectral overlays, limiting its reliability for definitive identification.
  • Residual analysis is crucial for distinguishing between spectra of the same and different chemical species.

Purpose of the Study:

  • To develop a robust methodology for definitive identification of chemical species using spectral data beyond traditional R-squared analysis.
  • To establish objective criteria for ranking spectral matches and unambiguously identifying analytes in complex mixtures.
  • To address limitations in spectral matching for challenging separations, such as those encountered in two-dimensional gas chromatography.

Main Methods:

  • Numerical analysis of spectral residuals, focusing on their statistical properties.
  • Normalization of experimental background noise to follow a wavelength-independent Gaussian distribution.
  • Application of seven criteria, including R-squared, Gaussian quantile-quantile plot R-squared, maximum consecutive sign count in residuals, and the first four moments of residuals, for spectral similarity assessment.

Main Results:

  • The study demonstrates that spectral residuals should resemble Gaussian noise only when spectra originate from the same chemical species.
  • Deviations from Gaussian distribution and high consecutive sign counts in residuals clearly indicate spectral mismatches, even when R-squared values are ambiguous.
  • The methodology successfully identified challenging hydrocarbon species (n-undecane, n-dodecane, n-tridecane) in jet fuel, highlighting its effectiveness under stress conditions.

Conclusions:

  • Numerical characteristics of spectral residuals provide invaluable, objective criteria for definitive spectral matching and elimination of false positives.
  • The proposed method enhances the reliability of analyte identification in two-dimensional gas chromatography by offering a logical ranking of plausible matches.
  • This approach offers transparent and unambiguous identification, overcoming the ambiguity often encountered with R-squared values alone, especially in complex samples.