Combining Dense Au Nanoparticle Layers and 2D Surface-Enhanced Raman Scattering Arrays for the Identification of Mutant Cyanobacteria Using Machine Learning

We report the crowding of Au nanoparticles (Au NPs) on a surface-enhanced Raman scattering (SERS) 2D array substrate with high nanoparticle surface coverage in a combined approach for the identification of cyanobacteria with machine learning. By simply using the screening effect of NaCl, the crowding effect of PEG to overcome the repulsion between nanoparticles, and different dithiol chain lengths during the deposition process of Au NPs on a substrate, we provide a general approach to increase the deposition density of nanoparticles on the films over nanodisk-array SERS substrates. The optimized substrate was subsequently utilized for the discrimination of wild-type (WT) and mutant cyanobacteria using SERS and machine learning methods (principal component analysis, logistic model, Gaussian nai??ve Bayes model, K-nearest-neighbor model, and a support vector classifier model with radial basis function). The best performance to discriminate between WT and mutant cyanobacteria was achieved by using the support vector classifier (SVC) with a positive rate as high as 97% using five repeat tests for the congeneric cells. These results indicate that highly sensitive SERS substrates, in combination with efficient data analysis, can be employed in mutant identification by SERS, enabling high-throughput screening in the current biological research.