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Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy

This paper presents an automatic classification of plastic material’s inorganic pigment using terahertz spectroscopy and convolutional neural networks (CNN). The plastic materials were placed between the THz transmitter and receiver, and the acquired THz signals were classified using a supervised le...

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Detalles Bibliográficos
Autores principales: Sarjaš, Andrej, Pongrac, Blaž, Gleich, Dušan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309565/
https://www.ncbi.nlm.nih.gov/pubmed/34300449
http://dx.doi.org/10.3390/s21144709
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author Sarjaš, Andrej
Pongrac, Blaž
Gleich, Dušan
author_facet Sarjaš, Andrej
Pongrac, Blaž
Gleich, Dušan
author_sort Sarjaš, Andrej
collection PubMed
description This paper presents an automatic classification of plastic material’s inorganic pigment using terahertz spectroscopy and convolutional neural networks (CNN). The plastic materials were placed between the THz transmitter and receiver, and the acquired THz signals were classified using a supervised learning approach. A THz frequency band between 0.1–1.2 THz produced a one-dimensional (1D) vector that is almost impossible to classify directly using supervised learning. This paper proposes a novel pre-processing of 1D THz data that transforms 1D data into 2D data, which are processed efficiently using a convolutional neural network. The proposed pre-processing algorithm consists of four steps: peak detection, envelope extraction, and a down-sampling procedure. The last main step introduces the windowing with spectrum dilatation that reorders 1D data into 2D data that can be considered as an image. The spectrum dilation techniques ensure the classifier’s robustness by suppressing measurement bias, reducing the complexity of the THz dataset with negligible loss of accuracy, and speeding up the network classification. The experimental results showed that the proposed approach achieved high accuracy using a CNN classifier, and outperforms 1D classification of THz data using support vector machine, naive Bayes, and other popular classification algorithms.
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spelling pubmed-83095652021-07-25 Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy Sarjaš, Andrej Pongrac, Blaž Gleich, Dušan Sensors (Basel) Article This paper presents an automatic classification of plastic material’s inorganic pigment using terahertz spectroscopy and convolutional neural networks (CNN). The plastic materials were placed between the THz transmitter and receiver, and the acquired THz signals were classified using a supervised learning approach. A THz frequency band between 0.1–1.2 THz produced a one-dimensional (1D) vector that is almost impossible to classify directly using supervised learning. This paper proposes a novel pre-processing of 1D THz data that transforms 1D data into 2D data, which are processed efficiently using a convolutional neural network. The proposed pre-processing algorithm consists of four steps: peak detection, envelope extraction, and a down-sampling procedure. The last main step introduces the windowing with spectrum dilatation that reorders 1D data into 2D data that can be considered as an image. The spectrum dilation techniques ensure the classifier’s robustness by suppressing measurement bias, reducing the complexity of the THz dataset with negligible loss of accuracy, and speeding up the network classification. The experimental results showed that the proposed approach achieved high accuracy using a CNN classifier, and outperforms 1D classification of THz data using support vector machine, naive Bayes, and other popular classification algorithms. MDPI 2021-07-09 /pmc/articles/PMC8309565/ /pubmed/34300449 http://dx.doi.org/10.3390/s21144709 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sarjaš, Andrej
Pongrac, Blaž
Gleich, Dušan
Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy
title Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy
title_full Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy
title_fullStr Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy
title_full_unstemmed Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy
title_short Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy
title_sort automated inorganic pigment classification in plastic material using terahertz spectroscopy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309565/
https://www.ncbi.nlm.nih.gov/pubmed/34300449
http://dx.doi.org/10.3390/s21144709
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