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Related Concept Videos

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
UV–Vis Spectroscopy: Woodward–Fieser Rules01:29

UV–Vis Spectroscopy: Woodward–Fieser Rules

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 contributions...
UV–Vis Spectroscopy of Conjugated Systems01:32

UV–Vis Spectroscopy of Conjugated Systems

Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
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IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

IR and UV–Vis Spectroscopy of Aldehydes and Ketones

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 C=O stretching, is...
Spectrophotometry: Introduction01:16

Spectrophotometry: Introduction

Spectrophotometry is the quantitative measurement of the absorption, reflection, diffraction, or transmission of electromagnetic radiation through a material as a function of the intensity and wavelength of the radiation. A spectrophotometer is a device used to measure the change in the radiation intensity caused by its interaction with the material.
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Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

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 electronic transitions. As a result...

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Related Experiment Video

Updated: Jun 26, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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AI Model for Textile Materials Identification Using Hyperspectral Data.

Fariborz Eghtedari1, Leszek Pecyna1, Rhys Evans1

  • 1The Manufacturing Technology Centre, Coventry CV7 9JU, UK.

Journal of Imaging
|June 25, 2026
PubMed
Summary

This study introduces a hyperspectral imaging and AI system for textile recycling, accurately identifying cotton, polyester, and elastane, even with challenging carbon-black dyes. The technology enhances automated sorting for high-value material recovery.

Keywords:
AI modelcarbon-black dyeconvolutional neural networkhyperspectral systemmachine learningnear-infrared spectroscopyspectral signaturetextile recycling

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Area of Science:

  • Materials Science
  • Computer Science
  • Textile Engineering

Background:

  • Accurate textile fibre identification is crucial for efficient recycling and high-value material recovery.
  • Current near-infrared (NIR) spectroscopy systems face limitations with proprietary models and difficulty detecting carbon-black dyed materials.

Purpose of the Study:

  • To develop a hyperspectral imaging approach combined with AI for precise textile fibre identification.
  • To enable the detection of cotton, polyester, elastane, and carbon-black dye regions in textiles.
  • To overcome spectral suppression issues caused by carbon-black dyes.

Main Methods:

  • Utilized hyperspectral imaging on 65 laboratory-verified textile samples.
  • Developed a semi-automatic algorithm for boundary detection and spectral sampling, creating a database of 6500 spectra.
  • Trained a convolutional neural network (CNN) model incorporating spatial clustering for fibre and dye region identification.

Main Results:

  • Achieved mean absolute errors below 2.1% for cotton, polyester, and elastane identification.
  • Demonstrated high precision in fibre composition prediction.
  • Successfully identified regions affected by carbon-black dye, even with suppressed spectral signatures.

Conclusions:

  • The hyperspectral imaging and AI approach offers a robust solution for textile fibre identification and carbon-black dye detection.
  • This technology shows significant potential for improving automated sorting in real-world textile recycling workflows.
  • The system enhances material recovery by accurately classifying diverse textile compositions.