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Research article

Using hyperspectral imaging and machine learning to identify food contaminated compostable and recyclable plastics

Authors
  • Nutcha Taneepanichskul (University College London)
  • Helen C Hailes orcid logo (University College London)
  • Mark Miodownik (University College London)

This is version 1 of this article, this is the latest verison of this preprint.

This article is a preprint and is currently undergoing peer review by UCL Open: Environment.

Abstract

With the increasing public legislation aimed at reducing plastic pollution, compostable plastics have emerged as an alternative to conventional plastics for some food packaging and food service items. However, the true value of compostable plastics can only be realized if they do not enter the environment as contaminants but instead are processed along with food and garden waste using industrial composting facilities. Distinguishing compostable plastics from conventional plastics in this waste stream is an outstanding problem. Currently, Near Infrared (NIR) technology is widely used to identify polymers, but it falls short in distinguishing plastics contaminated with food waste. This study investigates the application of hyperspectral imaging (HSI) to address this challenge, enhancing the detection and sorting of contaminated compostable plastics. By combining HSI with various machine learning algorithms we show it is possible to accurately identify and classify plastic packaging with food waste contamination, achieving up to 99% accuracy. The study also measures the impact of plastic features such as darkness, size, and level of contamination on model performance, with darkness having the most significant impact. Implementing HSI in waste management systems can significantly increase composting and recycling rates. This advanced sorting approach supports the circular economy by ensuring that both compostable and recyclable plastics are effectively processed and recycled, minimizing environmental impact

Keywords: hyperspectral imaging, recycling, composting, machine learning, automatic sorting, food contamination

Funding

Preprint Under Review
License
Creative Commons Attribution 4.0

 Open peer review from Owen Tamin

Review
Thank you for the opportunity to review your manuscript. Your work provides a comprehensive evaluation of the performance of hyperspectral imaging (HSI) combined with near-infrared (NIR) technology for detecting food-contaminated compostable and recyclable plastics. Below are suggestions to further enhance the quality and clarity of your paper:

1) Please ensure consistency in the correct use of terms like “near-infrared (NIR)” instead of "Near Infrared (NIR)" and “hyperspectral imaging (HSI)” throughout the manuscript.

2) In the last paragraph, “In this paper we present work developing new??? Chemometric … contamination” is unclear. Please revise for clarity.

3) The introduction could do with a small section on the importance of integrating near-infrared (NIR) spectrum information in detecting plastic waste, which is particularly important for enhancing accuracy in (real-world) scenarios where traditional RGB-based methods struggle, such as in distinguishing plastic materials with varying transparency, color, or weathered conditions. Some of the recent work on this includes:

i) Tamin, O., Moung, E.G., Dargham, J.A., Yahya, F. and Omatu, S., 2023. A review of hyperspectral imaging-based plastic waste detection state-of-the-arts. Int. J. Electr. Comput. Eng.(IJECE), 13, pp.3407-3419.

ii) Tamin, O., Moung, E.G., Dargham, J.A., Yahya, F., Farzamnia, A., Sia, F., Naim, N.F.M. and Angeline, L., 2023. On-Shore Plastic Waste Detection with YOLOv5 and RGB-Near-Infrared Fusion: A State-of-the-Art Solution for Accurate and Efficient Environmental Monitoring. Big Data and Cognitive Computing, 7(2), p.103.

iii) Tamin, O., Moung, E.G., Dargham, J.A., Yahya, F., Omatu, S. and Angeline, L., 2022, November. Machine learning for plastic waste detection: State-of-the-art, challenges, and solutions. In 2022 International Conference on Communications, Information, Electronic and Energy Systems (CIEES) (pp. 1-6). IEEE.

iv) Tamin, O., Moung, E.G., Dargham, J.A., Yahya, F., Omatu, S. and Angeline, L., 2022, September. A comparison of RGB and RGNIR color spaces for plastic waste detection using the YOLOv5 architecture. In 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (pp. 1-6). IEEE.


4) In subsection 2.1, you mention using HEINZ tomato ketchup for both training and validation, but only HEINZ mayonnaise for validation. Please explain why mayonnaise data wasn’t included in the training.

5) In subsection 2.2, consider adding a table to clarify the distribution of training, validation, and testing datasets for clarity.

6) In subsection 2.4, you may also consider evaluating model performance using additional metrics like precision, recall, and mean average precision (mAP) for a more comprehensive analysis.

7) Please fix the caption in Table 3 to use “The performance…” instead of “the performance…”.

8) I suggest splitting Section 4 Discussion and Conclusion into two separate sections. In the Conclusion, be sure to address both the study’s limitations and potential future research directions.

9) Subsection 4.3, “Application of HSI in anaerobic digestion, in-vessel composting, and recycling plant for detecting food-contaminated compostable plastics,” seems irrelevant. Please clarify its relevance to your study or consider removing it if it does not directly support the main research focus.

I look forward for the revised manuscript.

Note:
This review refers to round 1 of peer review.