Quantitative detection of peanut oil adulteration in olive oil via three-dimensional fluorescence spectroscopy and a PARAFAC–BPNN combination

Résumé fourni par la source

Olive oil as a high-value edible oil is highly susceptible to adulteration in the marketplace, particularly through the addition of low-cost oils, which severely undermines consumer interests and market fairness. In this study, a novel method based on three-dimensional fluorescence spectroscopy combined with PARAFAC–BPNN was developed for detecting olive oil adulteration. A total of 33 samples were prepared using olive oil–peanut oil mixtures, and their three-dimensional fluorescence spectra were collected using an FS5 steady-state fluorescence spectrometer. Scattering interferences were eliminated using Delaunay triangular interpolation, followed by noise reduction via the Savitzky–Golay smoothing algorithm. PARAFAC was then employed to resolve spectral features, and a backpropagation neural network was constructed for quantitative prediction of adulteration levels. Qualitative analysis showed that the spectra predicted by PARAFAC exhibited high overlap with the measured spectra. The correlation coefficients between the predicted and actual excitation and emission spectra of peanut oil were both 0.999, while those of olive oil were 0.968 and 0.998, respectively. The BPNN model achieved a coefficient of determination (R2) of 0.980, with RMSE and MAE as low as 0.041 and 0.029, whose test set RMSE and MAE are 0.046 and 0.036, showing excellent predictive accuracy and robustness. All 33 samples were correctly classified across adulteration levels from 0% to 90% with a step size of 10%. This study provides a rapid and accurate approach for detecting peanut oil adulteration in olive oil, offering practical support for edible oil safety supervision and quality control.

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