フライドオイル中の3-モノクロロプロパン-1,2-ジオールのフーリエ変換赤外分光法に基づくハイブリッド特徴選択で最適化された畳み込みニューラルネットワークを用いた定量的分析

To address challenges in quantifying 3-monochloropropane-1,2-diol (3-MCPD), a toxic compound formed during oil refining with nephrotoxic, reproductive toxic, and carcinogenic properties, a convolutional neural network (CNN) integrated with a hybrid feature selection strategy based on Fourier transform infrared spectroscopy was constructed. Before the construction of CNN, relevant spectral variables were selected by the SiPLSR-IRIV method, which achieved a 97 % variable reduction. Under the optimal conditions, the model attained a determination coefficient (R²_C) of 0.9689 and root mean square error of calibration (RMSEC) of 0.0734. The limits of detection (0.41 μg g^-1) and quantification (1.23 μg g^-1) complied with EU regulatory standards for 3-MCPD detection in oils. This hybrid CNN approach demonstrated superior performance with smaller computational cost and narrower spectral data sampling range compared to the conventional methods, providing a robust solution for quality monitoring in edible oil processing industries.