The
unique capability of surface plasmon resonance microscopy (SPRM) in single nanoparticle analysis has
found use in various chemical and biological applications. While SPRM
offers exceptional sensitivity, the statistical analysis of numerous
nanoparticles has been extremely laborious and time-consuming. Herein,
we presented an image processing software package for nanoparticle
analysis in SPRM, which is empowered by a deep learning algorithm.
This package enabled fully automated nanoparticle identification,
digital counting, three-dimensional tracking of particle locations,
and quantification of dwell time and Brownian motion properties. With
a built-in image filtering process to improve the contrast, robust
identification and analysis have been achieved from SPRM images of
low refractive index nanoparticles. This software tool would largely
promote the translation of SPRM technology into the digital sensing
platform for high throughput sample screening.
Electrochemical impedance spectroscopy (EIS) is a common method in biosensing detection of pathogens for public health and safety. In its most general form, increases of charge transfer resistance or decrease of double layer capacitance at the interface are used for reporting EIS system changes due to pathogens. However, this strategy is not universally adaptable to various EIS sensors and could lead to inaccurate detection. Herein, we demonstrated a machine learning-based EIS biosensor for E.coli detection with improved accuracy. EIS data was obtained from gold electrodes immobilized with E.coli through antibody binding and fitted with the Randles model to extrapolate multiple impedimetric parameters. A machine learning model, using principle component analysis and support vector regression, was trained to automatically establish a quantitative relationship between multiple impedimetric parameters and bacterial concentrations. Results showed an improved accuracy in determining bacterial concentration. The improvement is due to the integration of both capacitance and resistance information. These results thus pave the way for automatic and accurate EIS biosensors in various applications.
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