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

Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
High-Performance Liquid Chromatography: Types of Detectors01:15

High-Performance Liquid Chromatography: Types of Detectors

The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte properties and...
Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...
Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
Bioplastics01:27

Bioplastics

Bioplastics derived from microbial processes present a sustainable alternative to conventional petroleum-based plastics. Among these, polyhydroxyalkanoates (PHAs), particularly polyhydroxybutyrates (PHBs), have emerged as prominent candidates due to their biodegradability and biocompatibility. These polymers are synthesized by a variety of bacteria, such as Cupriavidus necator and Pseudomonas putida, which naturally accumulate PHAs as intracellular carbon and energy reserves, especially under...
Microbial Bioremediation of Plastics01:28

Microbial Bioremediation of Plastics

Polyethylene terephthalate (PET) is a synthetic polymer widely utilized in the packaging industry, particularly for bottles and containers. Due to its chemical stability and durability, PET accumulates in the environment, contributing significantly to plastic pollution. It comprises repeating units of terephthalic acid and ethylene glycol, resulting in a semi-crystalline structure that is resistant to natural degradation processes.A notable breakthrough in plastic biodegradation came with the...

You might also read

Related Articles

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

Sort by
Same author

The landscape of artificial intelligence in neurodegenerative diseases: a systematic review.

Communications medicine·2026
Same author

Coherent cross-modal generation of synthetic biomedical data to advance multimodal precision medicine.

PLoS computational biology·2026
Same author

Simultaneous multi-disease detection from the same leaf: a generalized approach using deep learning and image splitting.

Environmental monitoring and assessment·2026
Same author

Machine Learning Analysis Applied to Prediction of Early Progression Independent of Relapse Activity in Multiple Sclerosis Patients.

European journal of neurology·2025
Same author

Reply to Comments on the Article "Machine Learning Predicts Risk of Falls in Parkinson's Disease Patients in a Multicenter Observational Study".

European journal of neurology·2025
Same author

Neuropsychological and clinical variables associated with cognitive trajectories in patients with Alzheimer's disease.

Frontiers in aging neuroscience·2025

Related Experiment Video

Updated: Jun 25, 2026

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

49.3K

Plastics detection and sorting using hyperspectral sensing and machine learning algorithms.

Monica Moroni1, Marco Balsi2, Soufyane Bouchelaghem2

  • 1DICEA - Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy.

Waste Management (New York, N.Y.)
|May 3, 2025
PubMed
Summary
This summary is machine-generated.

Hyperspectral imaging and machine learning effectively detect plastic waste for recycling. This approach offers robust performance in both lab and real-world conditions, aiding waste management and litter detection.

Keywords:
Hyperspectral sensorsLinear Discriminant AnalysisMachine LearningMechanical recyclingPlastics wastek-Nearest Neighbors

More Related Videos

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris
05:31

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Published on: July 28, 2018

15.9K
Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
00:07

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

7.9K

Related Experiment Videos

Last Updated: Jun 25, 2026

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

49.3K
Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris
05:31

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Published on: July 28, 2018

15.9K
Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
00:07

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

7.9K

Area of Science:

  • Environmental Science
  • Materials Science
  • Computer Science

Background:

  • Effective plastic waste management is crucial for environmental sustainability.
  • Current detection and sorting methods need enhancement for improved recycling efficiency.
  • Plastic litter poses a significant environmental challenge, necessitating advanced detection techniques.

Purpose of the Study:

  • To investigate the efficacy of hyperspectral imaging (900-1700 nm) and machine learning for plastic waste detection and sorting.
  • To evaluate the performance of various machine learning algorithms for classifying common polymers.
  • To assess the applicability of these techniques for both recycling plant sorting and aerial remote sensing of plastic litter.

Main Methods:

  • Utilized hyperspectral imaging in the 900-1700 nm range.
  • Employed machine learning algorithms including minimum-Redundancy Maximum-Relevance (mRMR), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbors (k-NN).
  • Conducted experiments in both indoor laboratory and outdoor natural lighting conditions, using virgin polymers and collected plastic litter.

Main Results:

  • The combination of mRMR and LDA demonstrated superior performance and processing efficiency across all tested scenarios.
  • High Matthew's Correlation Coefficient (MCC) values (>0.94) were achieved in both indoor and outdoor settings.
  • Classifiers trained indoors showed successful application to outdoor data (MCC >0.90), and realistic plastic litter detection yielded variable but often high MCC scores (0.48-0.96).

Conclusions:

  • Hyperspectral imaging coupled with mRMR-LDA machine learning provides an effective and efficient solution for plastic waste detection and sorting.
  • The developed method shows promise for practical applications in recycling facilities and for remote sensing of environmental plastic pollution.
  • The approach is robust, requiring minimal calibration and demonstrating successful transferability between different lighting conditions.