The present application belongs to the technical field of
data analysis, and particularly relates to a high-
throughput biosensing
system for early warning of algal blooms and pathogens, which comprises multiple sampling units, a
microfluidic chip with a multi-channel structure, and a high-
throughput surface-enhanced Raman detection unit with an integrated
metal nanostructure surface, and obtains the original surface-enhanced Raman spectrum of the
water sample through a Raman spectrum acquisition mechanism. The
system uses compressed spectrum sampling and sparse reconstruction methods to reduce the data volume and maintain the integrity of the spectrum information, constructs a
pathogen Raman
fingerprint dictionary through a
dictionary learning method, and realizes the identification of different algal metabolites, algal toxins and
pathogen-related molecular components and the concentration interval judgment thereof in combination with a sparse representation classification mechanism. Based on the type and concentration interval of the pathogens, the
system further generates
algal bloom risk warning and
pathogen risk warning, and realizes early discovery of abnormal changes in the
water body. The present application has significant advantages in stability, sensitivity,
throughput and real-time performance.