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Related Concept Videos

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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 structure by adding the...
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Related Experiment Video

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The evaluation of evidence for microspectrophotometry data using functional data analysis.

Colin Aitken1, Ya-Ting Chang1, Patrick Buzzini2

  • 1School of Mathematics and Maxwell Institute, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK.

Forensic Science International
|November 23, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces functional data analysis for microspectrophotometry, aiding forensic investigations by comparing ink and fiber evidence. The method effectively evaluates likelihood ratios to support propositions of common or different sources.

Keywords:
Evidence evaluationFunctional data analysisLikelihood ratioMicrospectrophotometry

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Area of Science:

  • Forensic Science
  • Analytical Chemistry
  • Statistics

Background:

  • Microspectrophotometry is crucial in forensic analysis for comparing evidence like inks and fibers.
  • Likelihood ratio methods are standard for evaluating forensic evidence in criminal investigations.
  • Comparing crime scene data with suspect data is fundamental in criminal investigations.

Purpose of the Study:

  • To present a functional data analysis (FDA) technique for calculating likelihood ratios from full spectrum microspectrophotometry data.
  • To assess the method's effectiveness in comparing forensic evidence from different sources.
  • To provide a robust statistical framework for evaluating evidence in forensic casework.

Main Methods:

  • Functional data analysis (FDA) applied to full spectrum microspectrophotometry data.
  • Calculation of likelihood ratios to compare competing propositions (common source vs. different sources).
  • Evaluation of method performance using false positive and false negative rates, and Tippett plots.

Main Results:

  • The FDA method provides a quantitative measure for comparing forensic evidence.
  • The technique effectively distinguishes between samples from common and different sources for inks and fibers.
  • Performance metrics confirm the reliability of the likelihood ratio assessment.

Conclusions:

  • Functional data analysis offers a powerful approach for interpreting microspectrophotometry data in forensic science.
  • The likelihood ratio framework enhances the objective evaluation of ink and fiber evidence.
  • This method supports informed decision-making in criminal investigations by quantifying evidence strength.