Peanut oil oxidation was to monitor and quantify combining synchronous fluorescence spectroscopy and chemometrics. Peanut oil was subjected to an accelerated oxidation testing. The spectral and related chemical indicators were caught during oxidation induce testing. Fluorescence spectra were gained for each sample with simultaneous excitation from 200 to 800 nm and the offsets (Δλ) of 10 to 180 nm during the oxidation process. The results showed the induce period (IP) of the peanut oil was 16.45 h. Parallel factor analysis (PARAFAC) was performed to select the best Δλ interval of 70 nm, which spectral data was the most suitable for interval partial least square (iPLS) and synergy interval PLS (siPLS) modeling and forecast. The study presented all interval selection methods had the better results than the global spectrum modelling. iPLS reached the best into 10 intervals with a ratio of prediction to deviation (RPD) of 2.10. siPLS that separated the whole spectrum into 15 intervals and combined the third intervals (282 to 320 nm, 362 to 400 nm, and 761 to 800 nm) had a ratio of RPD of 2.26. The results showed the optimal siPLS model performed a little better than iPLS. The established model lying on interval selection could improve the prediction accuracy. It could provide a quick, accurate method to monitor oil oxidation process.
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