Enhanced Detection of Pesticide Residues Using Two-Dimensional Raman Correlation Spectroscopy and Machine Learning

Abstract

The accurate detection of pesticide residues in fresh produce is essential for public health. This study evaluated the use of two-dimensional correlation spectroscopy (2D-COS), specifically applied to Raman data, combined with principal component analysis (PCA) and support vector machines (SVMs) for the direct and rapid detection of chlorothalonil pesticide residues in four different vegetable matrices. This approach significantly enhances the spectral resolution, enabling the identification of subtle chlorothalonil-specific fingerprints. Raman spectra were analyzed across the full wave range (300–2500 cm –1 ), with four key fingerprint regions identified through 2D-COS: 354−414 cm –1 (C–Cl stretching), 1260−1286 cm –1 (C–C stretching), 1540−1570 cm –1 (C–C stretching), and 2250−2265 cm –1 (C≡N stretching). When applied to the full spectral range, the PCA-SVM model yielded only moderate classification accuracy (accuracy = 72%, κ = 0.43). In contrast, models focused on the fingerprint regions achieved perfect classification (accuracy = 100%, κ = 1), successfully distinguishing between the spiked and control samples. The consistent detection of C–Cl and C≡N bonds in all vegetable matrices highlights their reliability as universal markers for chlorothalonil detection. These findings demonstrate the potential of Raman spectroscopy combined with targeted data analysis, as a highly sensitive and reliable method for pesticide residue screening.