ORIGINAL RESEARCH article
Front. Sens.
Sec. Optoelectronic and Photonic Sensors
Volume 6 - 2025 | doi: 10.3389/fsens.2025.1662060
Machine Learning Pipeline for Microparticle Size Classification in Self-Mixing Interferometric Signals for Flow Cytometry
Provisionally accepted
- 1Laboratoire d'analyse et d'architecture Des Systèmes (LAAS), Toulouse, France
- 2Toulouse INP, Toulouse, France
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Self-mixing interferometry (SMI) is an emer-ging optical sensing technique for detecting and classifying microparticles in non-contact and label-free flowmetry applications. High precision and reliability are essential for its integration into medical diagnostics, such as blood analysis, and quality control in chemical manufacturing processes. While theoretical models describe SMI-induced signal modulati-ons caused by particle passage, challenges persist due to signal noise, variability, and interpretability under experimental conditions. This study enhances SMI-based particle size classification by integrating machine learning (ML) models to improve feature extraction and classification accuracy. Three ML pipelines are evaluated, achieving 98% classification accuracy in distinguishing particles of different sizes (2, 4, and 10 µm). The high classification accuracy demonstrates the scalability of our approach, ensuring its applicability across diverse particle analysis scenarios.
Keywords: self-mixing interferometry, Micro-Particle Size Classification, machine learning, Flow citometry, Signal processing
Received: 08 Jul 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 Sierra Alarcon, Perchoux, Tronche, Jayat and Quotb. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sebastian Sierra Alarcon, Laboratoire d'analyse et d'architecture Des Systèmes (LAAS), Toulouse, France
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.