Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

1.4K
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...
1.4K
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

1.1K
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...
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effects of cyclic temperature from geothermal heating on BTEX biodegradation in soil.

Journal of contaminant hydrology·2026
Same author

Exploring the effects of pH, ionic strength, and temperature on bisulfide sorption onto bentonite via experiments and numerical modelling.

Journal of contaminant hydrology·2026
Same author

Clinoptilolite-Based Adsorbents for Paracetamol Removal.

Molecules (Basel, Switzerland)·2025
Same author

A Comprehensive Analysis of the Effectiveness of a Water-Based Extraction Method in Cement Bypass Dust Valorization.

Materials (Basel, Switzerland)·2025
Same author

Hepatotoxicity of Nanoparticle-Based Anti-Cancer Drugs: Insights into Toxicity and Mitigation Strategies.

International journal of nanomedicine·2025
Same author

If someone is wrong but sincere, is it a lie? The role of objective falsity, intention, and in children's understanding of lying.

Journal of experimental child psychology·2025

Related Experiment Video

Updated: Jan 16, 2026

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall
07:51

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall

Published on: June 10, 2017

12.4K

Machine learning-assisted Raman spectroscopy for enhanced plastic identification.

Szymon Wójcik1, Magdalena Król1, Paweł Stoch1

  • 1AGH University of Krakow, Faculty of Materials Science and Ceramics, 30-059 Kraków, al. Mickiewicza 30, Poland.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|September 25, 2025
PubMed
Summary

Accurate plastic identification is crucial for recycling. This study combines Raman spectroscopy and a novel machine learning model, Branched PCA-Net, achieving over 99% accuracy in classifying ten common plastic types.

Keywords:
Machine learningNeural networkPlastic wastePolymer classificationRaman spectroscopy

More Related Videos

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.5K
Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
10:16

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Published on: December 16, 2016

50.7K

Related Experiment Videos

Last Updated: Jan 16, 2026

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall
07:51

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall

Published on: June 10, 2017

12.4K
Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.5K
Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
10:16

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Published on: December 16, 2016

50.7K

Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Computer Science

Background:

  • Global plastic waste exceeds 380 million tons annually, with low recycling rates (9%).
  • Current plastic identification methods struggle with visually similar polymers, hindering efficient mechanical recycling.
  • Advanced sorting requires precise identification of diverse plastic types, including contaminants.

Purpose of the Study:

  • To develop a robust methodological framework for accurate plastic classification.
  • To combine handheld Raman spectroscopy with machine learning for enhanced identification.
  • To introduce and validate a novel neural network architecture for spectroscopic data analysis.

Main Methods:

  • Collected 3000 Raman spectra from 10 common plastic types (PET, HDPE, PVC, LDPE, PP, PS, ABS, PC, PLA, PTFE).
  • Developed a branched neural network architecture (Branched PCA-Net) utilizing Principal Component Analysis (PCA) reduced spectral data.
  • Trained and tested the Branched PCA-Net on diverse plastic samples under varied measurement conditions.

Main Results:

  • Achieved over 99% classification accuracy on the test dataset.
  • Perfectly classified 7 out of 10 plastic types and highly accurately classified the remaining three.
  • Validated model robustness and generalization capabilities on new, differently measured samples.

Conclusions:

  • The Branched PCA-Net offers a significant methodological advancement for spectroscopic data analysis.
  • This approach shows high potential for quality control and targeted plastic identification in recycling.
  • While not for high-throughput sorting, it promises improved recycling workflow efficiency.