Surface-enhanced Raman spectroscopy (SERS) was combined with stoichiometric analysis to synthesize a AuNPs@MIL-100 (Fe) SERS substrate. This high-performance, and low-cost metal-organic framework-based SERS nano sensor is capable of detecting malachite green (MG) residues in fish. The minimum detectable concentration of MG in standard solutions is 0.1 μg/L, while in fish samples, the detection range spanned from 0.003-0.088 μg/kg. The partial least squares regression (PLSR) and support vector machine regression (SVR) were applied to model the SERS data, enhancing both the precision and reliability of the detection. The results show that compared with the traditional linear regression and PLSR models, the prediction accuracy of the SVR model is an improvement with higher stability. MG standard solutions and three fish samples can be analysed directly or after simple extraction. This method can be used to detect trace harmful substances in different aquatic products.
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