Taneepanichskul, Nutcha;
Hailes, Helen C;
Miodownik, Mark;
(2024)
Using hyperspectral imaging to identify and classify large microplastic contamination in industrial composting processes.
Frontiers in Sustainability
, 5
, Article 1332163. 10.3389/frsus.2024.1332163.
Preview |
PDF
Nutcha 2024.pdf - Published Version Download (2MB) | Preview |
Abstract
Compostable plastics are used as alternatives to conventional (non-compostable) plastics due to their ability to decompose through industrial composting comingled with food waste. However conventional (non-compostable) plastics sometimes contaminate this industrial composting process resulting in the formation of microplastics in the end compost. Therefore, it is crucial to effectively identify the types of plastics entering industrial composters to improve composting rates and enhance compost quality. In this study, we applied Hyperspectral Imaging (HSI) with various pre-processing techniques in the short-wave infrared (SWIR) region to develop an efficient model for identifying and classifying plastics and large microplastics during the industrial composting process. The materials used in the experimental analysis included compostable plastics such as PLA and PBAT, and conventional (non-compostable) plastics including PP, PET, and LDPE. Chemometric techniques, namely Partial Least Squares Discriminant Analysis (PLS-DA), was applied to develop a classification model. The Partial Least Squares Discriminant Analysis (PLS-DA) model effectively distinguished between virgin PP, PET, PBAT, PLA, and PHA plastics and soil-contaminated plastics measuring larger than 20 mm × 20 mm, achieving accuracy of 100%. Furthermore, it demonstrated a 90% accuracy rate in discriminating between pristine large microplastics and those contaminated with soil. When we tested our model on plastic samples during industrial composting we found that the accuracy of identification depended on parameters such as darkness, size, color, thickness and contamination level. Nevertheless, we achieved 85% for plastics and large microplastics detected within compost.
| Type: | Article |
|---|---|
| Title: | Using hyperspectral imaging to identify and classify large microplastic contamination in industrial composting processes |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/frsus.2024.1332163 |
| Publisher version: | http://dx.doi.org/10.3389/frsus.2024.1332163 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Microplastics, composting, hyperspectral imaging, spectrum pre-processing, chemometric analysis, identification and classification model, near infrared |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Chemistry |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10193494 |
Archive Staff Only
 |
View Item |
