Classification of Microplastic Particles in Water using Polarized Light Scattering and Machine Learning Methods

• URL: http://arxiv.org/abs/2511.06901v1
• Date: Mon, 10 Nov 2025 09:51:15 GMT
• Title: Classification of Microplastic Particles in Water using Polarized Light Scattering and Machine Learning Methods
• Authors: Leonard Saur, Marc von Pawlowski, Ulrich Gengenbach, Ingo Sieber, Hossein Shirali, Lorenz Wührl, Rainer Kiko, Christian Pylatiuk,
• Abstract summary: This paper introduces and validates a novel, reflection-based approach for the in-situ classification and identification of microplastics directly in water bodies.<n>We classify colorless microplastic particles (50-300 $mu$m) by illuminating them with linearly polarized laser light and capturing their reflected signals.<n>Using a deep convolutional neural network (CNN) for image-based classification, we successfully identified three common polymer types, high-density polyethylene, low-density polyethylene, and polypropylene.
• Score: 0.46950229019783496
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Facing the critical need for continuous, large-scale microplastic monitoring, which is hindered by the limitations of gold-standard methods in aquatic environments, this paper introduces and validates a novel, reflection-based approach for the in-situ classification and identification of microplastics directly in water bodies, which is based on polarized light scattering. In this experiment, we classify colorless microplastic particles (50-300 $\mu$m) by illuminating them with linearly polarized laser light and capturing their reflected signals using a polarization-sensitive camera. This reflection-based technique successfully circumvents the transmission-based interference issues that plague many conventional methods when applied in water. Using a deep convolutional neural network (CNN) for image-based classification, we successfully identified three common polymer types, high-density polyethylene, low-density polyethylene, and polypropylene, achieving a peak mean classification accuracy of 80% on the test dataset. A subsequent feature hierarchy analysis demonstrated that the CNN's decision-making process relies mainly on the microstructural integrity and internal texture (polarization patterns) of the particle rather than its macroshape. Critically, we found that the Angle of Linear Polarization (AOLP) signal is significantly more robust against contextual noise than the Degree of Linear Polarization (DOLP) signal. While the AOLP-based classification achieved superior overall performance, its strength lies in distinguishing between the two polyethylene plastics, showing a lower confusion rate between high-density and low-density polyethylene. Conversely, the DOLP signal demonstrated slightly worse overall classification results but excels at accurately identifying the polypropylene class, which it isolated with greater success than AOLP.

Related papers

• Polarization-resolved imaging improves eye tracking [54.79296119759393]
  We show how a polarization-enabled eye tracking system can reveal trackable features across the sclera and gaze-informative patterns on the cornea.<n>Results link light--tissue polarization effects to practical gains in human--computer interaction and position PET as a simple, robust sensing modality for future wearable devices.
  arXiv  Detail & Related papers  (2025-11-06T18:42:09Z)
• PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution [63.87900679891159]
  The resolution of a polarization camera is often much lower than that of a conventional RGB camera.<n>Existing polarized image demosaicing (PID) methods are limited in that they cannot enhance resolution.<n>We propose PIDSR, a joint framework that performs complementary Polarized Image Demosaicing and Super-Resolution.
  arXiv  Detail & Related papers  (2025-04-10T13:56:33Z)
• Shedding Light on the Polymer's Identity: Microplastic Detection and Identification Through Nile Red Staining and Multispectral Imaging (FIMAP) [0.6906005491572401]
  Fluorescence imaging has emerged as a promising technique for enhancing plastic particle detectability.<n>This study introduces the Fluorescence Imaging Microplastic Analysis Platform (FIMAP), a retrofitted multispectral camera with four optical filters and five excitation wavelengths.<n>FIMAP achieves 90% precision, 90% accuracy, 100% recall, and an F1 score of 94.7%.
  arXiv  Detail & Related papers  (2025-02-25T09:03:55Z)
• All-fiber microendoscopic polarization sensing at single-photon level aided by deep-learning [0.0]
  We introduce a real-time polarization measurement method that is accurate down to a single-photon level.<n>We validate the approach by visualizing the polarization structure of biological specimens and the liquid crystal polymer sample.
  arXiv  Detail & Related papers  (2024-05-03T15:22:04Z)
• Learning to Deblur Polarized Images [59.33932533761383]
  A polarization camera can capture four linear polarized images with different polarizer angles in a single shot.<n>Since the on-chip micro-polarizers block part of the light so that the sensor often requires a longer exposure time, the captured polarized images are prone to motion blur.<n>We propose a polarized image deblurring pipeline to solve the problem in a polarization-aware manner.
  arXiv  Detail & Related papers  (2024-02-28T07:56:28Z)
• Spatial super-resolution in nanosensing with blinking emitters [79.16635054977068]
  We propose a method of spatial resolution enhancement in metrology (thermometry, magnetometry, pH estimation, and similar methods) with blinking fluorescent nanosensors.<n>We believe that blinking fluorescent sensing agents being complemented with the developed image analysis technique could be utilized routinely in the life science sector.
  arXiv  Detail & Related papers  (2024-02-27T10:38:05Z)
• Probing polarization response of monolayer cell cultures with photon entanglement [0.24091079613649835]
  We explore polarization-based probing, which often excels over intensity modulation when assessing a specimen's morphology. We present a novel, highly sensitive method for probing single-layer cell cultures using entangled photon pairs.
  arXiv  Detail & Related papers  (2024-01-19T16:21:50Z)
• ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection [88.4359020192429]
  Existing methods either involve computationally expensive context aggregation or lack prior modeling of polyps, resulting in poor performance in challenging cases. In this paper, we propose the Enhanced CenterNet with Contrastive Learning (ECC-PolypDet), a two-stage training & end-to-end inference framework. Box-assisted Contrastive Learning (BCL) during training to minimize the intra-class difference and maximize the inter-class difference between foreground polyps and backgrounds, enabling our model to capture concealed polyps. In the fine-tuning stage, we introduce the IoU-guided Sample Re-weighting
  arXiv  Detail & Related papers  (2024-01-10T07:03:41Z)
• PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection [81.16859686137435]
  We present PARTNER, a novel 3D object detector in the polar coordinate. Our method outperforms the previous polar-based works with remarkable margins of 3.68% and 9.15% on and ONCE validation set.
  arXiv  Detail & Related papers  (2023-08-08T01:59:20Z)
• Monochrome and Color Polarization Demosaicking Using Edge-Aware Residual Interpolation [14.5106375775521]
  A microgrid image polarimeter enables us to acquire a set of polarization images in one shot. Since the polarimeter consists of an image sensor equipped with a monochrome or color polarization filter array, the demosaicking process to interpolate missing pixel values plays a crucial role in obtaining high-quality polarization images. We propose a novel MPFA demosaicking method based on edge-aware residual (EARI) and also extend it to CPFA demosaicking.
  arXiv  Detail & Related papers  (2020-07-28T15:04:36Z)
• Accurate polarization preparation and measurement using twisted nematic liquid crystals [0.0]
  We report fast preparation and detection of polarization states with unprecedented accuracy using liquid-crystal cells extracted from common twisted nematic liquid-crystal displays. In addition to the application in classical polarimetry, we also employ the reported liquid-crystal device for full tomographic characterization of a three-mode Greenberger--Horne--Zeilinger entangled state produced by a photonic quantum processor.
  arXiv  Detail & Related papers  (2020-01-20T14:32:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.