Laser-Induced Breakdown Spectroscopy Assisted by Machine Learning for Plastics/Polymers Identification

Dimitrios Stefas, Nikolaos Gyftokostas, Elli Bellou, Stelios Couris

Atoms 2019 article cited by 72

Source Open-access PDF More in Engineering & Materials

importance 4/10 solved Laser-induced spectroscopy and plasma plastics recycling stage: research

Problem

Discriminating plastic samples that share the same polymeric matrix but contain different additives is difficult.

Why it matters

Reliable identification of similar plastics enables LIBS-based analysis of polymers and other organic materials.

What the AI did

PCA and LDA machine learning algorithms were applied to LIBS spectroscopic data to classify the plastic samples.

Results

Identification accuracies as high as 100%.

Impact

ML-assisted LIBS achieved excellent classification of similar plastics in this study.

Record

Venue	Atoms
Published	2019
Work type	article
Authors	Dimitrios Stefas, Nikolaos Gyftokostas, Elli Bellou, Stelios Couris
Citations	72 (OpenAlex, at import time)
References	36
Model or method	Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA)
Organization	not stated in source
Primary source	https://doi.org/10.3390/atoms7030079
Open access	gold
Keywords	LIBS, machine learning, PCA, LDA, plastics identification, polymers

Importance rating

4 / 10

real but narrower advance

Abstract suggests ML-assisted LIBS could aid sorting and recycling of plastics and other organics; a useful materials-analysis advance with no stated scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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