Rapid identification and quantitative analysis of malachite green in fish via SERS and 1D convolutional neural network

Rapid and quantitative detection of malachite green (MG) in aquaculture products is very important for safety assurance in food supply. Here, we develop a point-of-care testing (POCT) platform that combines a flexible and transparent surface-enhanced Raman scattering (SERS) substrate with deep learning network for achieving rapid and quantitative detection of MG in fish. The flexible and transparent SERS substrate was prepared by depositing silver (Ag) film on the polydimethylsiloxane (PDMS) film using laser molecular beam epitaxy (LMBE) technique. The wrinkled Ag NPs@PDMS film exhibits high SERS activity, excellent reproducibility and good mechanical stability. Additionally, the fast in situ detection of MG residues on fish scales was achieved by using the wrinkled Ag NPs/PDMS film and a portable Raman spectrometer, with a minimum detectable concentration of 10-6 M. Subsequently, a one-dimensional convolutional neural network (1D CNN) model was constructed for rapid quantification of MG concentration. The results demonstrated that the 1D CNN quantitative analysis model possessed superior predictive performance, with a coefficient of determination (R2) of 0.9947 and a mean squared error (MSE) of 0.0104. The proposed POCT platform, integrating a transparent flexible SERS substrate, a portable Raman spectrometer and a 1D CNN model, provides an efficient strategy for rapid identification and quantitative analysis of MG in fish.