Material sorting using hyperspectral imaging for biocomposite recycling

Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsScientific

Abstract

The impending exhaustion of non-renewable natural resources emphasizes the importance of sustainable materials. One environmentally friendlier alternative to plastics are biocomposite materials, which are composed of recycled or virgin polymer and a natural fiber material. In order to be to be truly sustainable, the material has to be efficiently recycled, a task which demands a means for their sorting. This paper outlines a method for distinguishing different plastic and biocomposite samples from one another based on hyperspectral imaging. The developed regression model correctly classified 96 % of the samples in the dataset. In the case of biocomposite samples, the accompanying polymer was quite accurately recognized.
Original languageEnglish
Title of host publication4th International Conference on Natural Fibers – Smart Sustainable Solutions, ICNF 2019
Subtitle of host publicationBook of Abstracts
EditorsRaul Fanguiero
Pages250-251
Number of pages2
ISBN (Electronic)978-972-8600-30-3
Publication statusE-pub ahead of print - 1 Jul 2019
MoE publication typeNot Eligible

Fingerprint

sorting
recycling
polymer
plastic
nonrenewable resource
natural resource
material

Cite this

Sormunen, T., Järvinen, S., Lämsä, A., Immonen, K., Mannila, J., & Peltola, J. (2019). Material sorting using hyperspectral imaging for biocomposite recycling. In R. Fanguiero (Ed.), 4th International Conference on Natural Fibers – Smart Sustainable Solutions, ICNF 2019: Book of Abstracts (pp. 250-251)
Sormunen, Tuomas ; Järvinen, Sari ; Lämsä, Arttu ; Immonen, Kirsi ; Mannila, Juha ; Peltola, Johannes. / Material sorting using hyperspectral imaging for biocomposite recycling. 4th International Conference on Natural Fibers – Smart Sustainable Solutions, ICNF 2019: Book of Abstracts. editor / Raul Fanguiero. 2019. pp. 250-251
@inbook{1f0102dae9ad4e88b0d0952fb7898648,
title = "Material sorting using hyperspectral imaging for biocomposite recycling",
abstract = "The impending exhaustion of non-renewable natural resources emphasizes the importance of sustainable materials. One environmentally friendlier alternative to plastics are biocomposite materials, which are composed of recycled or virgin polymer and a natural fiber material. In order to be to be truly sustainable, the material has to be efficiently recycled, a task which demands a means for their sorting. This paper outlines a method for distinguishing different plastic and biocomposite samples from one another based on hyperspectral imaging. The developed regression model correctly classified 96 {\%} of the samples in the dataset. In the case of biocomposite samples, the accompanying polymer was quite accurately recognized.",
author = "Tuomas Sormunen and Sari J{\"a}rvinen and Arttu L{\"a}ms{\"a} and Kirsi Immonen and Juha Mannila and Johannes Peltola",
year = "2019",
month = "7",
day = "1",
language = "English",
pages = "250--251",
editor = "Raul Fanguiero",
booktitle = "4th International Conference on Natural Fibers – Smart Sustainable Solutions, ICNF 2019",

}

Sormunen, T, Järvinen, S, Lämsä, A, Immonen, K, Mannila, J & Peltola, J 2019, Material sorting using hyperspectral imaging for biocomposite recycling. in R Fanguiero (ed.), 4th International Conference on Natural Fibers – Smart Sustainable Solutions, ICNF 2019: Book of Abstracts. pp. 250-251.

Material sorting using hyperspectral imaging for biocomposite recycling. / Sormunen, Tuomas; Järvinen, Sari; Lämsä, Arttu; Immonen, Kirsi; Mannila, Juha; Peltola, Johannes.

4th International Conference on Natural Fibers – Smart Sustainable Solutions, ICNF 2019: Book of Abstracts. ed. / Raul Fanguiero. 2019. p. 250-251.

Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsScientific

TY - CHAP

T1 - Material sorting using hyperspectral imaging for biocomposite recycling

AU - Sormunen, Tuomas

AU - Järvinen, Sari

AU - Lämsä, Arttu

AU - Immonen, Kirsi

AU - Mannila, Juha

AU - Peltola, Johannes

PY - 2019/7/1

Y1 - 2019/7/1

N2 - The impending exhaustion of non-renewable natural resources emphasizes the importance of sustainable materials. One environmentally friendlier alternative to plastics are biocomposite materials, which are composed of recycled or virgin polymer and a natural fiber material. In order to be to be truly sustainable, the material has to be efficiently recycled, a task which demands a means for their sorting. This paper outlines a method for distinguishing different plastic and biocomposite samples from one another based on hyperspectral imaging. The developed regression model correctly classified 96 % of the samples in the dataset. In the case of biocomposite samples, the accompanying polymer was quite accurately recognized.

AB - The impending exhaustion of non-renewable natural resources emphasizes the importance of sustainable materials. One environmentally friendlier alternative to plastics are biocomposite materials, which are composed of recycled or virgin polymer and a natural fiber material. In order to be to be truly sustainable, the material has to be efficiently recycled, a task which demands a means for their sorting. This paper outlines a method for distinguishing different plastic and biocomposite samples from one another based on hyperspectral imaging. The developed regression model correctly classified 96 % of the samples in the dataset. In the case of biocomposite samples, the accompanying polymer was quite accurately recognized.

M3 - Conference abstract in proceedings

SP - 250

EP - 251

BT - 4th International Conference on Natural Fibers – Smart Sustainable Solutions, ICNF 2019

A2 - Fanguiero, Raul

ER -

Sormunen T, Järvinen S, Lämsä A, Immonen K, Mannila J, Peltola J. Material sorting using hyperspectral imaging for biocomposite recycling. In Fanguiero R, editor, 4th International Conference on Natural Fibers – Smart Sustainable Solutions, ICNF 2019: Book of Abstracts. 2019. p. 250-251