Application of Zernike moments for the quantitative analysis of polycyclic aromatic hydrocarbons based on...

A novel method combining three-dimensional fluorescence spectroscopy, Zernike moments, and generalized regression neural networks (GRNN) is proposed for the quantitative analysis of polycyclic aromatic hydrocarbons (PAHs)-such as acenaphthene and naphthalene-in mixture samples. The approach directly converts 3D fluorescence spectral data into grayscale images, from which Zernike moments are extracted as feature descriptors. These moments are used as inputs to a GRNN-based quantitative model, avoiding the need for complex pretreatment steps. By evaluating different orders of Zernike moments, an optimal set was identified, achieving average relative errors of 5.62% for acenaphthene and 5.70% for naphthalene across eight test samples. This strategy demonstrates the effective incorporation of image-based feature extraction into fluorescence analysis and offers a promising tool for the rapid quantification of components in complex environmental samples.