The Study of LSPR Biosensor Data Processing Based on Deep Learning

In order to process the data of the localized surface plasmon resonance (LSPR) biosensor more quickly and scientifically, this paper takes the reflection spectrum data obtained from the LSPR biosensor precipitated gold nanoparticle self-assembled monolayer (NPSAM) as an example, and designs a data processing algorithm based on deep learning . The data is processed into a two-dimensional color image, transferred to the VGG16 model for feature extraction, after the principal component analysis algorithm (PCA) is used for feature dimensionality reduction, the result is visualized. Experiments show that the result of visualization can clearly distinguish the status of LSPR biosensor nanoparticle addition.