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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8309565 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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