Analysis of corn deterioration due to molding using surface-enhanced Raman spectroscopy

Efficient and rapid identification of corn mildew levels is essential for proper storage and transportation. This study utilized surface-enhanced Raman spectroscopy (SERS) to obtain Raman spectral fingerprints of moldy corn, combined with multi-class support vector machines (SVM) for rapid detection. Spectral data were preprocessed using the Savitzky-Golay smoothing method, and principal component analysis (PCA) was applied to extract the top five components. Feature peaks were identified using partial least squares discriminant analysis (PLS-DA) regression coefficients, supplemented by manual selection, resulting in eight characteristic wavenumber peaks (482, 878, 1046, 1082, 1220, 1276, 1452, and 1590 cm–¹). These features were used for clustering analysis, followed by SVM classification to distinguish mildew levels. The model achieved a 100% recognition rate, validated by cross-validation and confusion matrix analysis. The findings demonstrate that SERS combined with SVM enables precise differentiation of mildew levels, providing reliable support for Raman spectroscopy in fungal detection and grain safety monitoring.