Classification of antibiotics by neural network analysis of optical resonance data of whispering gallery modes in dielectric microspheres

Vladimir A. Saetchnikov; Elina A. Tcherniavskaia; Gustav Schweiger; Andreas Ostendorf

doi:10.1117/12.920397

30 April 2012 Classification of antibiotics by neural network analysis of optical resonance data of whispering gallery modes in dielectric microspheres

Vladimir A. Saetchnikov, Elina A. Tcherniavskaia, Gustav Schweiger, Andreas Ostendorf

Author Affiliations +

Proceedings Volume 8424, Nanophotonics IV; 84240Q (2012) https://doi.org/10.1117/12.920397
Event: SPIE Photonics Europe, 2012, Brussels, Belgium

Abstract

A novel emerging technique for the label-free analysis of nanoparticles and biomolecules in liquid fluids using optical micro cavity resonance of whispering-gallery-type modes is being developed.A scheme based on polymer microspheres fixed by adhesive on the evanescence wave coupling element has been used. We demonstrated that the only spectral shift can't be used for identification of biological agents by developed approach. So neural network classifier for biological agents and micro/nano particles classification has been developed. The developed technique is the following. While tuning the laser wavelength images were recorded as avi-file. All sequences were broken into single frames and the location of the resonance was allocated in each frame. The image was filtered for noise reduction and integrated over two coordinates for evaluation of integrated energy of a measured signal. As input data normalized resonance shift of whispering-gallery modes and the relative efficiency of whispering-gallery modes excitation were used. Other parameters such as polarization of excited light, "center of gravity" of a resonance spectra etc. are also tested as input data for probabilistic neural network. After network designing and training we estimated the accuracy of classification. The classification of antibiotics such as penicillin and cephasolin have been performed with the accuracy of not less 97 %. Developed techniques can be used for lab-on-chip sensor based diagnostic tools as for identification of different biological molecules, e.g. proteins, oligonucleotides, oligosaccharides, lipids, small molecules, viral particles, cells and for dynamics of a delivery of medicines to bodies.

Citation Download Citation

Vladimir A. Saetchnikov, Elina A. Tcherniavskaia, Gustav Schweiger, and Andreas Ostendorf "Classification of antibiotics by neural network analysis of optical resonance data of whispering gallery modes in dielectric microspheres", Proc. SPIE 8424, Nanophotonics IV, 84240Q (30 April 2012); https://doi.org/10.1117/12.920397

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