Abstract
The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.
Highlights
IntroductionThe accurate classification of the trapped specimen is a task of paramount importance, paving the way for optical tweezers (OT)-based systems that are able to enter into the mesoscopic scale and real-time operating devices
The results obtained for the accuracy of each method are presented in Table 2, including the mean, the best, and the worst performance during the 6-fold cross-validation procedure
These findings suggest that a conventional optical tweezers (OT) system equipped with a fast response, position-sensitive quadrant photodetector (QPD) may outperform single detector scattering analysis methodologies previously reported in the literature [19]
Summary
The accurate classification of the trapped specimen is a task of paramount importance, paving the way for OT-based systems that are able to enter into the mesoscopic scale and real-time operating devices
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