Smart polarization and spectroscopic holography for real-time microplastics identification

Smart polarization and spectroscopic holography for real-time microplastics identification

(2024):3:32 | Yanmin Zhu, Yuxing Li, Jianqing Huang, Edmund Y. Lam
The article introduces a novel method called Smart Polarization and Spectroscopic Holography (SPLASH) for real-time microplastics (MP) identification. SPLASH integrates polarization imaging, digital holography, and spectroscopy to automatically analyze the molecular structure and composition of MPs, improving the accuracy and efficiency of identification. The system uses a Stokes polarization mask to capture four polarization states simultaneously, eliminating the need for a separate spectroscopic system. Machine learning algorithms, such as ensemble subspace discriminant classifier, k-nearest neighbors classifier, and support vector machine, are employed to enhance the classification performance. The effectiveness of SPLASH is demonstrated through experiments with various MP materials and natural particles, achieving over 0.8 AUC values and less than 0.05 variance in classification accuracy. The method provides a promising tool for addressing MP pollution assessment, source identification, and long-term water pollution monitoring.The article introduces a novel method called Smart Polarization and Spectroscopic Holography (SPLASH) for real-time microplastics (MP) identification. SPLASH integrates polarization imaging, digital holography, and spectroscopy to automatically analyze the molecular structure and composition of MPs, improving the accuracy and efficiency of identification. The system uses a Stokes polarization mask to capture four polarization states simultaneously, eliminating the need for a separate spectroscopic system. Machine learning algorithms, such as ensemble subspace discriminant classifier, k-nearest neighbors classifier, and support vector machine, are employed to enhance the classification performance. The effectiveness of SPLASH is demonstrated through experiments with various MP materials and natural particles, achieving over 0.8 AUC values and less than 0.05 variance in classification accuracy. The method provides a promising tool for addressing MP pollution assessment, source identification, and long-term water pollution monitoring.
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[slides and audio] Smart polarization and spectroscopic holography for real-time microplastics identification