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Related Concept Videos

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

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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.
The ATR process begins by directing a beam...
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
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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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Related Experiment Video

Updated: Sep 19, 2025

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry
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Using ATR-FTIR spectroscopy and machine learning for forensic hair identification.

Zehua Fan1, Chenyu Li2, Qiran Sun3

  • 1Institute of Forensic Science, Fudan University, Shanghai, People's Republic of China.

Journal of Forensic Sciences
|June 9, 2025
PubMed
Summary

Attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy effectively distinguishes human hair from different body areas. A support vector machine (SVM) model achieved 90% accuracy, showing promise for forensic applications.

Keywords:
armpit hairidentificationpubic hairscalp hair

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Area of Science:

  • Forensic Science
  • Analytical Chemistry
  • Biophysics

Background:

  • Hair analysis is crucial in forensic investigations.
  • Spectroscopic techniques offer non-destructive methods for material identification.
  • Fourier transform infrared (FTIR) spectroscopy is established for substance classification.

Purpose of the Study:

  • To evaluate Attenuated Total Reflection (ATR) FTIR spectroscopy for differentiating human scalp, pubic, and armpit hair.
  • To develop and compare machine learning models for hair origin identification.
  • To assess the potential of ATR-FTIR spectroscopy combined with machine learning in forensic hair analysis.

Main Methods:

  • ATR-FTIR spectroscopy was used to analyze human hair samples from different body regions.
  • Partial Least Squares Discriminant Analysis (PLS-DA), Random Forest (RF), and Support Vector Machine (SVM) classification models were developed.
  • Model performance was evaluated based on accuracy, recall, and precision.

Main Results:

  • The Support Vector Machine (SVM) model demonstrated superior performance with 90.37% accuracy, 90.37% recall, and 90.38% precision.
  • Key spectral differences contributing to hair discrimination were identified in amide I, amide III, and C-H deformation regions.
  • ATR-FTIR spectroscopy combined with SVM provided accurate and non-destructive hair identification.

Conclusions:

  • ATR-FTIR spectroscopy coupled with SVM is a promising, non-destructive method for identifying human hair from different body regions.
  • This technique offers a fast and accurate approach with no sample preparation required, suitable for forensic science.
  • The study highlights the potential of spectroscopic and machine learning methods for advancing forensic identification capabilities.