Ultrasensitive SERS quantitative detection of antioxidants via diazo derivatization reaction and deep learning for...

Synthetic antioxidants serve as essential protectors against oxidation and deterioration of edible oils, however, prudent evaluation is necessary regarding potential health risks associated with excessive intake. The direct adsorption of antioxidants onto conventional surface-enhanced Raman scattering (SERS) substrates is challenging due to the presence of phenolic hydroxyl groups in their molecular structures, resulting in weak Raman scattering signals and rendering direct SERS detection difficult. In this study, a diazo derivatization reaction was employed to enhance SERS signals by converting antioxidant molecules into azo derivatives, enabling the amplification of the weak Raman scattering signals through the strong vibrational modes induced by the N = N double bond. The resulting diazo derivatives were characterized using UV-visible absorption and infrared spectroscopy, confirming the occurrence of diazo derivatization of the antioxidants. The proposed method successfully achieved the rapid detection of three commonly used synthetic antioxidants, namely butylated hydroxyanisole (BHA), tert-butylhydroquinone (TBHQ), and propyl gallate (PG) on interfacial self-assembled gold nanoparticles. Furthermore, rapid predictions of BHA, PG, and TBHQ within the concentration range of 1 × 10^-6 to 2 × 10^-3 mol/L were achieved by integrating a convolutional neural network model. The predictive range of this model surpassed the traditional quantitative method of manually selecting characteristic peaks, with linear coefficients (R²) of 0.9992, 0.9997, and 0.9997, respectively. The recovery of antioxidants in real soybean oil samples ranged from 73.0 % to 126.4 %. Based on diazo derivatization, the proposed SERS method eliminates the need for complex substrates and enables the analysis and determination of synthetic antioxidants in edible oils within 20 min, providing a convenient analytical approach for quality control in the food industry.