A Novel Method For Scatterers Type Enumeration In Polydisperse Suspensions Through Fiber Trapping And Unsupervised Scattering Analysis

IMAGING, MANIPULATION, AND ANALYSIS OF BIOMOLECULES, CELLS, AND TISSUES XVII(2019)

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摘要
Colloids and suspensions are part of our daily routines. Even the blood is considered a "naturally" occurring colloid. However, the majority of colloids are complex and composed by a diversity of nano to microparticles. The characterization of both synthetic and physiological fluids in terms of particulate types, size and surface characteristics plays a vital role in products formulation, and in the early diagnosis through the identification of abnormal scatterers in physiological fluids, respectively. Several methods have been proposed for characterizing suspensions, including imaging, electrical sensing counters, hydrodynamic or field flow fractionation. However, the Dynamic Light Scattering (DLS) has evolved as the most convenient method from these. Based also on the scattering signal, we propose a novel, simple and fast method able to determine the number of different scatterers type present in a suspension, without any previous information about its composition (in terms of particle classes). This is achieved by collecting features from a 980 nm laser back-scattered signal acquired through a polymeric lensed optical fiber tip dipped into the solution. Unlike DLS, this technique allows the trapping of particles whose diameter >= 1 mu m. For smaller particles, despite not guaranteeing their immobilization, it is also able to determine the number of different nanoparticles classes in an ensemble. The number of particle types was correctly determined for suspensions of synthetic particles and yeasts; different bacteria; and 100 nm nanoparticles types, using both Principal Component Analysis and K-means algorithms. This method could be a valuable alternative to complex and time-consuming methods for particles separation, such as field flow fractionation.
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关键词
Optical Fiber Tweezers, Optical Manipulation, Back-Scattering, Principal Component Analysis (PCA), K-means Clustering, Unsupervised Learning
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