This application provides a method and
system for detecting antibiotic
pollution in water bodies based on spectral characteristics, belonging to the field of
water environment monitoring and analysis technology. First, this application simultaneously acquires the surface-enhanced Raman spectrum and
redox potential time-
series data of the
water sample to be tested. Next, the ratio of the characteristic peak intensities of the target antibiotic is extracted from the spectrum to generate a first
feature set. Simultaneously,
wavelet packet
decomposition is performed on the potential data, and energy entropy is calculated to construct a second
feature set. Subsequently, the two feature sets are concatenated, and a
mutual information maximization
algorithm is used to filter out the target feature subset highly correlated with concentration. Finally, using a support
vector projection regression model trained based on a
structural risk minimization mechanism, and through kernel
function mapping and hyperplane projection operations, the accurate detection of the target antibiotic concentration in the
water sample is achieved. This application improves the anti-interference capability and
quantitative accuracy of antibiotic detection in complex
water body backgrounds.