This invention discloses a
machine learning-enhanced
fluorescence detection method for silver
nanoclusters containing split
nucleic acid aptamers, used for the detection of
adenosine triphosphate (ATP) in aquatic products, belonging to the field of
analytical chemistry. This detection method uses two split
nucleic acid aptamers of ATP as templates to synthesize
DNA-AgNCs. In the presence of the target ATP, a
conformational change is induced, resulting in a decrease in
fluorescence intensity. Quantitative analysis of ATP is achieved by detecting this
fluorescence change. To further improve the detection effect,
machine learning is introduced to preprocess the fluorescence spectrum. A recursive feature
elimination algorithm is used to screen characteristic wavelengths, and a multi-dimensional
feature set is constructed using maximum
fluorescence intensity,
peak area, and
full width at half maximum (FWHM). The optimal classification model is obtained through cross-validation optimization. This invention combines
aptamer recognition,
DNA-AgNCs, and
machine learning to construct a detection method where fluorescence dynamically decreases with increasing ATP concentration. It has advantages such as low cost, simple operation, and good
biocompatibility, effectively improving detection sensitivity and stability, and has promising application prospects in the qualitative and quantitative analysis of ATP in aquatic products.