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 naïve 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.