Abstract: In this study, a method for the rapid quantitative determination of the content of high-value vegetable oil in blended edible vegetable oils (BEVO) was proposed based on Raman spectroscopy and a selection algorithm of spectral characteristic intervals. First, the particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms were combined to develop a hybrid intelligent optimization algorithm called PSOGWO. Second, the PSOGWO algorithm and the combined moving window (CMW) strategy were combined to develop a novel spectral characteristic interval selection algorithm named PSOGWO-CMW. Third, blends of corn oil (CO) and extra virgin olive oil (EVOO) at different ratios were prepared, and then their Raman spectra were acquired. Using the Raman spectra as input variables, partial least squares regression (PLSR), PSO-CMW, GWO-CMW, and PSOGWO-CMW models were developed to predict the content of EVOO, and their performance was comparatively studied. The results showed that the PSOGWO-CMW model had the best prediction performance. The results of the proposed method for the content of EVOO in CO-EVOO blends were not significantly different from those of gas chromatography-mass spectrometry. In conclusion, this method is rapid and accurate, and can be used for rapid and quantitative determination of the content of high-value vegetable oil in BEVO.

Key words: Raman spectroscopy; blended edible vegetable oils; intelligent optimization algorithms; spectral characteristic intervals selection; quantitative authentication