Quantification of polycyclic aromatic hydrocarbons in roasted Tan lamb using fluorescence hyperspectral imaging technology

Abstract

Polycyclic aromatic hydrocarbons (PAHs) are one of the main harmful substances produced during the roasting of lamb. In this study, fluorescence hyperspectral imaging (FHSI) at different excitation wavelengths was used for the rapid determination of total PAHs (T-PAHs) content in roasted Tan lamb. Heterogeneous two-dimensional correlation spectroscopy (H2D-COS) was performed to resolve the fluorescence hyperspectral peaks. Competitive adaptive reweighed sampling (CARS), uninformative variable elimination (UVE), variable combination population analysis (VCPA), iterative retained information variable (IRIV) and interval variable iterative spatial shrinkage analysis (iVISSA) methods were employed to extract effective wavelengths to reduce data dimensionality. Partial least squares regression (PLSR), least squares support vector machine (LS-SVM) and convolutional neural network (CNN) algorithms were used to develop the T-PAHs content prediction model. For the FHSI at 357 nm excitation wavelength, the Smoothing-CARS-PLSR quantitative prediction model worked the best, with RC2 and RP2 reaching 0.9011 and 0.9169, and RMSEC and RMSEP reaching 0.0179 mg/kg and 0.0181 mg/kg, respectively. For the FHSI at 452 nm excitation wavelength, Smoothing-VCPA-CNN showed the best results, with RC2 and RP2 of 0.9199 and 0.9189, and RMSEC and RMSEP reaching 0.0175 mg/kg and 0.0171 mg/kg, respectively. This study provided a reference for the quantitative detection of PAHs.