Identification of Camellia Oil Adulteration With Excitation-Emission Matrix Fluorescence Spectra and Deep Learning.

Abstract

Camellia oil (CAO), known for its high nutritional and commercial value, has raised increasing concerns about adulteration. Developing an accurate and non-destructive method to identify CAO adulterants is crucial for safeguarding public health and well-being. This study simulates potential real-world adulteration cases by designing representative adulteration scenarios, followed by the acquisition and analysis of corresponding excitation-emission matrix fluorescence (EEMF) spectra. Parallel factor analysis (PARAFAC) was employed to characterize and explore the variations of fluorophores in the EEMF spectra of different adulterated scenarioss, which showed a linear correlation between the relative concentration of PARAFAC components and adulteration levels. A deep learning model named ResTransformer, which combines residual modules with Transformer, was proposed for both the qualitative detection of adulteration types and the quantitative detection of adulteration concentrations from local and global perspectives. The global ResTransformer qualitative models achieved accuracies of over 96.92% based on EEMF spectra and PARAFAC, and quantitative models showed determination coefficient of validation ([Formula: see text]) > 0.978, root mean square error of validation ([Formula: see text]) < 3.0643%, and the ratio performance deviation (RPD) > 7.6741. Compared to traditional chemometric models, the ResTransformer model demonstrated superior performance. The integration of EEMF and ResTransformer presents a highly promising strategy for rapid and reliable detection of CAO adulteration.