Sea salt increasingly harbors organic contaminants from personal care products, yet current monitoring methods lack spatial resolution and require destructive sampling. This study introduces an innovative analytical framework integrating Laser-Induced Fluorescence (LIF) Hyperspectral Imaging (HSI) with machine learning for the rapid, non-destructive detection of sunscreen residues on salt crystals. To simulate contamination, seawater from the Mediterranean coast (Alexandria, Egypt) was spiked to achieve a 10 mg/L sunscreen concentration within the seawater matrix prior to crystallization; this formulation contained Ethylhexyl Methoxycinnamate, Homosalate, and Ethylhexyl Salicylate. A SOC710 HS camera (128 bands) acquired fluorescence data under 450 nm laser excitation. Raw data underwent preprocessing and dimensionality reduction via Sparse Principal Component Analysis (Sparse PCA, λ = 0.5, k = 4 components, 73.4% sparsity). A Support Vector Machine (SVM) with an RBF kernel was trained on these sparse features. Performance evaluation employed tenfold stratified cross-validation, an 80-20 holdout test on ROI-based spectra, and independent sample validation against manually annotated pixel-wise ground-truth masks. While ROI-based tests yielded near-perfect accuracy under ideal conditions, full-image evaluation achieved ≈96% pixel-wise accuracy (precision ≈ 0.99, recall ≈ 0.95, F1 ≈ 0.97), providing a realistic estimate under heterogeneous conditions. Full-image classification mapped widespread contamination (57.8% of pixels), whereas an independently prepared clean salt sample produced zero false positives. The integrated Sparse PCA-SVM framework transforms fluorescence-imaging data into spatio-chemical maps, simultaneously revealing contaminant presence and spatial distribution on salt surfaces, thereby offering a powerful paradigm for the interpretable monitoring of organic pollutants in food materials.
Ebrahem et al. (Fri,) studied this question.