ArticleMikrochimica acta2026
Multiclass machine learning classification of aflatoxin B1 and ochratoxin A in crude palm oil using SERS with statistically validated model benchmarking.
Article in Mikrochimica acta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Mycotoxin contamination in crude palm oil poses significant food safety challenges, yet conventional detection methods remain time-consuming and resource-intensive. This study presents a rapid analytical framework for the simultaneous detection of aflatoxin B1 (AFB1) and ochratoxin A (OTA) by integrating Au@Ag core-shell nanoparticles as surface-enhanced Raman scattering (SERS) substrates with modified dispersive solid-phase extraction and a statistically validated machine-learning classification model. Monodisperse Au@Ag nanoparticles (15 ± 2 nm cores, 7 ± 1 nm shells) were synthesized and characterized, confirming successful core-shell formation and surface plasmon resonance shifts that enable optimal electromagnetic enhancement at 785 nm excitation. The modified extraction protocol effectively removed lipid matrix interferents while maintaining recoveries of 95.1-110.8%, achieving detection limits of 0.94 pg/mL for AFB1 and 0.93 pg/mL for OTA-3-4 orders of magnitude below regulatory thresholds. Spectral analysis revealed orientation-dependent enhancement mechanisms, with consistently enhanced bands at 1369 cm⁻¹ (AFB1) and 1578 cm⁻¹ (OTA) enabling simultaneous detection in binary mixtures through complementary spectral windows. Four supervised machine learning algorithms (PLS-DA, LDA, k-NN, SVM) were systematically evaluated, with linear projection methods achieving near-perfect test accuracies (0.990-1.000) using only 3-10 principal components. Rigorous nonparametric statistical validation using Friedman and Dunn's tests established that PLS-DA and LDA are statistically equivalent and optimal classifiers, significantly outperforming nonlinear approaches. The validated two-tier screening workflow showed no statistically significant differences relative to LC-MS/MS reference measurements, supporting its potential for high-throughput mycotoxin screening in lipid-rich food matrices.
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