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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

295
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
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Applications of IR Spectroscopy: Overview01:11

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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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Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
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Infrared (IR) Spectroscopy: Overview01:09

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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Updated: Jun 3, 2025

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall
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Using Infrared Raman Spectroscopy with Machine Learning and Deep Learning as an Automatic Textile-Sorting Technology

Pei-Fen Tsai1, Shyan-Ming Yuan1

  • 1Department of Computer Science, National Yang Ming Chiao Tung University, ChiaoTung Campus, Hsinchu 300093, Taiwan.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
Summary

This study introduces advanced Raman spectroscopy and AI for textile sorting, enhancing recycling efficiency and fiber quality. This technology supports the circular economy by improving waste management for sustainable fashion.

Keywords:
Raman spectroscopyartificial intelligencecircular economymachine learningneural networkstextile recyclingtextile sorting

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Area of Science:

  • Materials Science
  • Environmental Science
  • Computer Science

Background:

  • The fast-fashion industry generates substantial pre-consumer and post-consumer textile waste, negatively impacting environmental sustainability.
  • Linear economic models exacerbate textile waste, highlighting the urgent need for effective recycling solutions within a circular economy framework.

Purpose of the Study:

  • To develop an efficient textile-sorting technology for enhanced textile recycling.
  • To address the challenges posed by diverse fiber compositions in waste textiles.
  • To improve the quality of recycled fibers through accurate sorting.

Main Methods:

  • Development of a Raman spectroscopy-based textile-sorting system capable of detailed molecular compositional analysis.
  • Integration of Artificial Intelligence (AI) algorithms, including Principal Component Analysis (PCA), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN).
  • Categorization of textiles into six distinct groups based on fiber composition.

Main Results:

  • The developed sorter achieved a sorting efficiency of one piece per second.
  • Textile grouping precision exceeded 95% based on fiber compositional analysis.
  • Raman spectroscopy proved effective in providing crucial molecular compositional data for sorting.

Conclusions:

  • The Raman spectroscopy and AI-integrated system offers a highly accurate and efficient solution for textile sorting.
  • This technology significantly contributes to improving recycled fiber quality and advancing sustainable textile recycling.
  • The interdisciplinary approach provides a viable pathway towards achieving circular economy objectives in the textile industry.