Rong, G.; Xu, Y.; Sawan, M. Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors. Biosensors2023, 13, 860.
Rong, G.; Xu, Y.; Sawan, M. Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors. Biosensors 2023, 13, 860.
Rong, G.; Xu, Y.; Sawan, M. Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors. Biosensors2023, 13, 860.
Rong, G.; Xu, Y.; Sawan, M. Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors. Biosensors 2023, 13, 860.
Abstract
We describe a machine learning (ML) approach to process the signals collected from Covid-19 optical-based detector. Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were used to process both raw data and feature engineering data, and high performances for qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/ml has been achieved. Valid detection experiments contain 486 negative and 108 positive samples; and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contains 36 negative samples and 732 positive samples. Data distribution patterns of the valid and control detection dataset, based on T-distributed Stochastic Neighbor Embedding (t-SNE), was used to study the distinguishability between positive and negative samples, and explain the ML prediction performances. This work demonstrates that ML can be a generalized effective approach to process signals and dataset of biosensors dependent on resonant modes as biosensing mechanism.
Keywords
Machine Learning; Support Vector Machine; Multilayer Perceptron; Photonic Biosensor; Signal Processing; Tamm Plasmon Polariton; Localized Surface Plasmon Resonance
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
