Quantitative Detection and Over-limit Identification of Sesamol, Octyl Gallate and Ferulic Acid in Soybean Oil Using AgNP-based SERS and Supervised Learning Algorithms
J. Environ. Nanotechnol., Volume 15, No 1 (March 2026) pp. 378-391
Abstract
The excessive or uncontrolled addition of synthetic and natural antioxidants in edible oils raises increasing food safety concerns and necessitates rapid and sensitive detection techniques. In this study, a surface-enhanced Raman spectroscopy (SERS) platform integrated with machine learning algorithms was developed for the detection and discrimination of sesamol (SM), octyl gallate (OG), and ferulic acid (FA) in soybean oil. Silver nanoparticles (AgNPs) with an average size of 15–20 nm were synthesized using a citrate reduction method and employed as SERS substrates. The synthesized AgNPs exhibited a strong surface plasmon resonance peak at 528 nm, a high enhancement factor of 4.4 × 10⁵, and good reproducibility, with a relative standard deviation (RSD) of 8.17%. Quantitative analysis showed a strong linear relationship between SERS peak intensity and antioxidant concentration in the range of 50–400 mg/kg, with correlation coefficients of 0.9383 (SM), 0.9761 (OG), and 0.9607 (FA). The limits of detection were 9.711 mg/kg for SM, 8.107 mg/kg for OG, and 7.345 mg/kg for FA. To further enhance identification accuracy, several supervised machine learning models, including support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN), were applied to classify SERS spectral data. Among these models, SVM achieved the best classification performance with an accuracy of 97.11%, while over-limit detection at 200 mg/kg showed an overall accuracy greater than 93%, including 100% prediction accuracy for FA and SM samples. The integration of SERS with machine learning provides a rapid, sensitive, and reliable analytical strategy for food safety monitoring and quality control of antioxidants in edible oils, demonstrating strong potential for real-time regulatory screening applications.
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