Rapid authentication of rancid edible oil based on fourier transform infrared spectroscopy of principal component and discrimination analysis

40 qualified edible oils and 44 rancid ones were collected and analyzed. 25 qualified edible oils and39 rancid ones were selected to compose training set. Principal component analysis (PCA) was used to compress thousands of spectral data into several variables and describe the body of spectra, the analysis suggested that the accumulate reliabilities of PC1, PC2 and PC3 (the first three principle components) were more than 95%and corresponding 1743¹710cm-1, 1172¹130cm-1, 2945²844cm-1, 1728¹689cm-1, 2987 ²840cm-1and1731 ¹660cm-1were the most sensitive bands for edible oil rancidity. The training set was used to build discrimination analysis (DA) model, and then the most sensitive bands were applied as DA model inputs. The model was validated by other 20 samples as validation set with the correct recognition rate of 100%, which showed this method could be used to distinguish the rancid edible oil rapidly, accurately and soundly.